C: Number of different countries where the model has been applied. # E: Number of different ecosystems where the model has been applied.

assessed.

database).

**last reference**

BGC process [13] 1.81 23 1.33 2 1.89 178 1.67 6.70 6 14 20 1.48 8.18 FORECAST hybrid [17] 2.36 17 0.98 1 2.00 81 0.76 6.10 6 21 27 2.00 8.10 3-PG process [15] 2.08 14 0.81 2 1.89 118 1.11 5.89 7 10 17 1.26 7.15 ECOSYS process [18] 2.50 14 0.81 1 2.00 25 0.23 5.54 2 11 13 0.96 6.51 LINKAGES process [7] 0.97 26 1.50 2 1.89 110 1.03 5.40 3 7 10 0.74 6.14 BIOMASS process [7] 0.97 20 1.15 4 1.68 149 1.40 5.21 4 7 11 0.81 6.02 CENTURY process [3] 0.42 18 1.04 2 1.89 213 2.00 5.35 4 3 7 0.52 5.87 LANDIS process [7] 0.97 12 0.69 1 2.00 132 1.24 4.90 N / A 4.90 ZELIG process [9] 1.25 24 1.38 1 2.00 27 0.25 4.89 N / A 4.89 SORTIE process [11] 1.53 18 1.04 2 1.89 44 0.41 4.87 N / A 4.87 MGM hybrid [8] 1.11 17 0.98 1 2.00 37 0.35 4.44 N / A 4.44 SILVA process [6] 0.83 24 1.38 5 1.58 45 0.42 4.22 N / A 4.22 FORCLIM process [8] 1.11 18 1.04 4 1.68 35 0.33 4.16 N / A 4.16 SWAT process [6] 0.83 6 0.35 2 1.89 105 0.99 4.06 N / A 4.06 PnET process [5] 0.69 17 0.98 4 1.68 29 0.27 3.63 N / A 3.63 G´DAY process [4] 0.56 17 0.98 3 1.79 28 0.26 3.59 N / A 3.59 LPJ process [4] 0.56 11 0.63 1 2.00 18 0.17 3.36 N / A 3.36 EFIMOD hybrid [4] 0.56 12 0.69 2 1.89 10 0.09 3.24 N / A 3.24 FORWADY hybrid [3] 0.42 14 0.81 2 1.89 4 0.04 3.16 N / A 3.16 PICUS process [3] 0.42 12 0.69 3 1.79 20 0.19 3.09 N / A 3.09 ORCHIDEE process [4] 0.56 4 0.23 3 1.79 39 0.37 2.94 N / A 2.94 TRIPLEX hybrid [3] 0.42 8 0.46 3 1.79 11 0.10 2.77 N / A 2.77 GYPSY empirical [4] 0.56 5 0.29 4 1.68 9 0.08 2.61 N / A 2.61 PROGNOSIS hybrid [3] 0.42 8 0.46 8 1.26 31 0.29 2.43 N / A 2.43 JABOWA process [4] 0.56 22 1.27 19 0.11 25 0.23 2.16 N / A 2.16 CLASS process [3] 0.42 4 0.23 9 1.16 9 0.08 1.89 N / A 1.89 FORMIX process [4] 0.56 9 0.52 13 0.74 3 0.03 1.84 N / A 1.84 FORET process [3] 0.42 17 0.98 19 0.11 32 0.30 1.80 N / A 1.80 FORGRO process [3] 0.42 5 0.29 12 0.84 11 0.10 1.65 N / A 1.65 FORECE process [3] 0.42 7 0.40 17 0.32 5 0.05 1.18 N / A 1.18

N / A: Non applicable, for models that did not pass the cut-off score of 5.0, the number of countries and ecosystems was not

**Table 1.** Ranking and scores of the models included in the comparative study (with 3 or more documents in the

**Citations in Web of Science**

**Type # score years score years score # Score SCORE # C # E Total score SCORE**

**PARTIAL Forest types applied FINAL**

3-PG (the acronym represents Physiological Principles in Predicting Growth) was originally developed to simulate homogeneous, fast-growing plantations such as Eucalyptus [37], but has since been calibrated for other forest types [38]. 3-PG is a monthly time-step model working at stand and population levels. It is a model that includes general ecological processes and therefore needs to be calibrated for each individual species. It is designed for homogeneous forests, particularly even-aged or planted stands.

The model is built around the basic principles that drive ecosystem production. These same principles underlie earlier models such as FOREST-BGC [39] and BIOMASS [40]. The structure of 3-PG is based on two linked sets of calculations [41]: one set estimates biomass and growth values, whereas the other set estimates biomass allocation among different tree components. 3-PG is a conservation-of-mass model.

The model, like most process-based approaches, calculates rates of photosynthesis, transpira‐ tion, growth allocation and litter production. 3-PG derives estimates of radiation interception, gross primary production (GPP), net primary production (NPP) and allocation of the resultant carbohydrate pool to component parts of the trees. NPP is calculated as a fixed fraction of gross photosynthesis [42]. GPP is derived by applying a canopy quantum efficiency value to the amount of photosynthetically active radiation absorbed by a stand.

Quantum efficiency (the potential rate of photosynthesis) is a constant fraction of absorbed photosynthetically active radiation, and is constrained by atmospheric vapour pressure deficit. The latter is a function of stomatal conductance, which is influenced by air temperature, frost, water balance and nutrition. Canopy conductance is estimated as a function of leaf area index. The ratio of actual/potential photosynthesis is assumed to decrease in response to a suite of limiting environmental factors. It decreases with reduced availability of water and nutrients, which triggers a higher proportion of photosynthate allocated belowground.

Soil nutritional status (the availability of nutrients such as N and P) is represented by an index, the *fertility rating*, which can assume a value between 0 and 1 [38]. The fraction of production not allocated to roots is partitioned among foliage, stem and branches based on species-specific allometric equations.

3-PG can be used as a stand-level tool, or ground-based forest inventory data can be incorpo‐ rated into a Geographical Information System (GIS) to simulate forest growth over large areas. 3-PG has a wide range of predicted stand properties that are directly compatible with con‐ ventional inventory measurements, including stem density, DBH, basal area, total volume, current and mean annual increment. In addition, the model outputs information pertaining to the underlying biophysical relationships. This means that growth patterns can be linked to specific controls, such as resource deficiencies and climate.

From the perspective of reclamation, a strength of 3-PG is that it appears suitable for predicting tree growth in areas currently devoid of tree cover and has relatively low calibration require‐ ments [38]. Whether it could be reliably calibrated for oil sands materials, however, is un‐ known. 3-PG can be used to evaluate different management effects of stand density, thinning and fertilization (within the limitations of the fertility rating approach used for simulating nutrient availability).Arguably, the main weakness of 3-PG is its relative simplicity. It does not accommodate stands with complex structure (either in space or in terms of multiple aged trees), multiple species, and it has no understory representation. In addition, representation of soil nutritional status is overly simplified and is considered a static site property (it cannot vary through time). This significantly limits its application to oil sands materials and how soil properties might be expected to change over time.

### *3.2.3. BGC*

BGC is a family of models, designed to accommodate different biological scales (TREE-BGC, FOREST-BGC, and BIOME-BGC). The original model was FOREST-BGC [39], an individualentity, distance-independent model [42]. The term "entity" is used because STAND-BGC (a derivative of FOREST-BGC; [43]) grows shrubs and grass in addition to trees. Shrubs and grasses are described as per unit area entities, while trees have unique dimensions. All the models have the same core architecture and work on a daily time step, with results typically summarized annually. BIOME-BGC is a biome/ecosystem model, with spatial scales from stand to region.

BGC simulates fluxes and storage of water, carbon, and nitrogen [44-46]. BGC simulates fluxes and storage of water, carbon, and nitrogen [44-46]. The model has been designed to study the interactions between management, disturbances, climate and vegetation ecophysiological features, and their influences in water, nitrogen and carbon flows.

Net primary productivity is calculated as the difference between gross primary productivity (GPP) and autotrophic respiration, where GPP is a function of air temperature, water vapour pressure deficit, soil moisture, CO2 concentration, LAI, and solar radiation at the top of the canopy. N concentrations in root and leaf, combined with temperature, are used to estimate respiration [47]. Canopy is simulated as one layer with sunlit and shaded foliage. The Farquhar equation is used to calculate photosynthesis [48]. Atmospheric CO2 and humidity, leaf water and N contents, radiation and air temperature are used to calculate leaf conductance. Then, based on LAI values at leaf level, canopy C and water fluxes are calculated.

BGC is fundamentally driven by daily weather data. Therefore, ecophysiological descriptors of site vegetation, daily weather records and site physical properties are used by the model to simulate plant, soil, and litter variables, as well as water, carbon and nitrogen fluxes between the soil, the vegetation and the atmosphere. Unlike earlier models in the BGC model family (e.g. Forest-BGC, [39]), in Biome-BGC LAI is predicted as a function of the amount of leaf carbon, one of multiple vegetation state variables that are updated daily within the model [22]. Vegetation type is a user-defined, constant set of ecophysiological parameters. However, the model simulates changes in vegetation structure as consequence of disturbance, climate and ecophysiological characteristics of each vegetation type simulated.

The main strength of the model is its application in a broad range of ecosystem types. BGC´s structure makes the model a suitable research tool to predict the impact of climate change. Forest-BGC, for example, has been widely used to predict climate change effects on natural disturbance and carbon dynamics [49]. In addition, BIOME-BGC offers a link between input data and GIS databases, which is useful for application of data collected from regional studies. A shortcoming of BGC is that the canopy is homogeneous. Therefore, although leaf area index is proportional to canopy depth, this may not be sufficient to capture water and carbon budgets accurately[39].Itsmaindrawbackis the lackof amanagementinterface,whichmakes itdifficult to consider BGC as a decision-support tool for forest management and land reclamation.

### *3.2.4. FORECAST*

gross primary production (GPP), net primary production (NPP) and allocation of the resultant carbohydrate pool to component parts of the trees. NPP is calculated as a fixed fraction of gross photosynthesis [42]. GPP is derived by applying a canopy quantum efficiency value to the

Quantum efficiency (the potential rate of photosynthesis) is a constant fraction of absorbed photosynthetically active radiation, and is constrained by atmospheric vapour pressure deficit. The latter is a function of stomatal conductance, which is influenced by air temperature, frost, water balance and nutrition. Canopy conductance is estimated as a function of leaf area index. The ratio of actual/potential photosynthesis is assumed to decrease in response to a suite of limiting environmental factors. It decreases with reduced availability of water and nutrients,

Soil nutritional status (the availability of nutrients such as N and P) is represented by an index, the *fertility rating*, which can assume a value between 0 and 1 [38]. The fraction of production not allocated to roots is partitioned among foliage, stem and branches based on species-specific

3-PG can be used as a stand-level tool, or ground-based forest inventory data can be incorpo‐ rated into a Geographical Information System (GIS) to simulate forest growth over large areas. 3-PG has a wide range of predicted stand properties that are directly compatible with con‐ ventional inventory measurements, including stem density, DBH, basal area, total volume, current and mean annual increment. In addition, the model outputs information pertaining to the underlying biophysical relationships. This means that growth patterns can be linked to

From the perspective of reclamation, a strength of 3-PG is that it appears suitable for predicting tree growth in areas currently devoid of tree cover and has relatively low calibration require‐ ments [38]. Whether it could be reliably calibrated for oil sands materials, however, is un‐ known. 3-PG can be used to evaluate different management effects of stand density, thinning and fertilization (within the limitations of the fertility rating approach used for simulating nutrient availability).Arguably, the main weakness of 3-PG is its relative simplicity. It does not accommodate stands with complex structure (either in space or in terms of multiple aged trees), multiple species, and it has no understory representation. In addition, representation of soil nutritional status is overly simplified and is considered a static site property (it cannot vary through time). This significantly limits its application to oil sands materials and how soil

BGC is a family of models, designed to accommodate different biological scales (TREE-BGC, FOREST-BGC, and BIOME-BGC). The original model was FOREST-BGC [39], an individualentity, distance-independent model [42]. The term "entity" is used because STAND-BGC (a derivative of FOREST-BGC; [43]) grows shrubs and grass in addition to trees. Shrubs and grasses are described as per unit area entities, while trees have unique dimensions. All the models have the same core architecture and work on a daily time step, with results typically

amount of photosynthetically active radiation absorbed by a stand.

154 12 Biodiversity in Ecosystems - Linking Structure and Function

specific controls, such as resource deficiencies and climate.

properties might be expected to change over time.

allometric equations.

*3.2.3. BGC*

which triggers a higher proportion of photosynthate allocated belowground.

FORECAST is a management-oriented, stand-level forest growth and ecosystem dynamics simulator [50]. The model was originally designed to accommodate a wide variety of harvest‐ ing and silvicultural systems in order to compare and contrast their effect on forest produc‐ tivity, stand dynamics and a series of biophysical indicators of non-timber values. FORECAST-Climate version (see below) calculates climate modifiers on forest productivity on a daily basis. The modifiers are then accumulated across the year to estimate annual biomass production. FORECAST performs many calculations at the stand level but it also disaggregates stand-level productivity across individual stems in relation to age-specific stem size distributions. Top height and DBH are calculated for each stem and used in a taper function to calculate total and individual gross and merchantable volumes, and biomass.

Stand growth and ecosystem dynamics are based on a representation of the rates of key ecological processes regulating the availability of, and competition for, light and nutrient resources. FORECAST calculates biomass productivity (NPP) based on estimates of inherent productivity derived from historical bioassay data (see below) constrained by site-specific nutrient and water availability determined from within the model. The rates of the key ecological processes driving tree and plant growth are calculated from the bioassay data and inputted values for ecosystem variables (decomposition rates, photosynthetic saturation curves, for example) and their relation to nutrient uptake, the capture of light energy, and net primary production. Using this 'internal calibration' (hybrid simulation) approach, the model generates a suite of growth properties for each tree and plant species [50]. These growth properties are retained within the model and used to model subsequent growth as a function of resource availability and competition.

FORECAST's reliance on historical bioassay data serves to reduce calibration requirements while ensuring its projections of productivity are reasonable. Calibration data are assembled that describe the accumulation of biomass (above and below-ground components) in trees and minor vegetation for three chronosequences of stands, representing three different nutritional qualities. Tree biomass and stand self-thinning data can be derived from height, diameter at breast height, and stand density output generated by traditional growth and yield models in conjunction with species-specific biomass allometric equations [51]. To calibrate the nutritional aspects of the model, data describing the concentration of nutrients in the various biomass components are required. FORECAST also requires data on the degree of shading produced by different quantities of foliage and the photosynthetic response of foliage to different light levels. A comparable but simpler set of data for minor vegetation must be provided if the user wishes to represent this ecosystem component (see, for example, [52]). Lastly, data describing the rates of decomposition of various litter types and soil organic matter are required for the model to simulate nutrient cycling. The second aspect of calibration requires running the model in "spin-up" mode to establish initial site conditions. This component is a key feature in the ability of the model to simulate the site conditions characteristic of oil sands reclamation. For a broader discussion on this topic, see [7, 53, 54]).

Stand hydrology and water limitation for tree growth (see [25]) are simulated within the FORECAST-Climate model [55], which on a daily time step provides a mechanistic represen‐ tation of above and belowground hydrological interactions in forest stands with multiple soil and canopy layers. This facilitates a representation of competition between trees in different canopy layers and minor vegetation for available soil water. In addition, the hydrological model also estimates the influence of drought on litter decomposition rates, and therefore on nutrient mineralization and its availability for vegetation [56]. Hence, as noted above the model tracks the balance between inputs from precipitation and seepage, and outputs by canopy interception, evapotranspiration, plant uptake, percolation and runoff.

FORECAST has been calibrated for the Ft. McMurray region. It has been applied to oil sands reclamation for over almost 15 years, in large part to compare current and alternative recla‐ mation practices and their relationship to indicators of ecosystem function and the achieve‐ ment of end land-use objectives. In this regard, FORECAST output was used to derive multipliers and nutrient regime classes for the Landscape Capability Classification System [57]; to explore issues associated with peat decomposition rates; the depth and type of the capping material; nitrogen deposition; subsoil organic matter content; species mixes, planting densities, understory dynamics, and dead organic matter dynamics (specifically snags), all within the context of growth and yield [58 – 60]. Recently, FORECAST-Climate was used in a risk analysis of the potential development of water stress in young reclamation plantations consisting of white spruce, trembling aspen, and jack pine established on different ecosites, as a function of soil texture and slope position [61]. In the second phase of this work, the principal objective was an evaluation of the impact of climate and climate change on reclamation success, as compared to the base case analysis (no climate-related impacts) [62]. The potential effect of different climate change scenarios on growth and mortality in reclamation areas was therefore projected using the FORECAST Climate model and associated modelling tools to evaluate their combined impacts on overall ecosystem development in a risk assessment context. A final component of this work consisted of: (1) Model projections of tree regeneration under climate change on actual oil sands reclamation materials, and (2) A comprehensive model analysis of the risks to ecosystem productivity from climate change as a consequence of the impact of moisture stress on tree mortality [55]. Recently, funding was approved for a project to:


Produce a guidance document on how to implement the tools, interpret output, and assess the implications for reclamation principles and practices as reflective of an adaptive decision framework.

## **4. Conclusions**

productivity across individual stems in relation to age-specific stem size distributions. Top height and DBH are calculated for each stem and used in a taper function to calculate total and

Stand growth and ecosystem dynamics are based on a representation of the rates of key ecological processes regulating the availability of, and competition for, light and nutrient resources. FORECAST calculates biomass productivity (NPP) based on estimates of inherent productivity derived from historical bioassay data (see below) constrained by site-specific nutrient and water availability determined from within the model. The rates of the key ecological processes driving tree and plant growth are calculated from the bioassay data and inputted values for ecosystem variables (decomposition rates, photosynthetic saturation curves, for example) and their relation to nutrient uptake, the capture of light energy, and net primary production. Using this 'internal calibration' (hybrid simulation) approach, the model generates a suite of growth properties for each tree and plant species [50]. These growth properties are retained within the model and used to model subsequent growth as a function

FORECAST's reliance on historical bioassay data serves to reduce calibration requirements while ensuring its projections of productivity are reasonable. Calibration data are assembled that describe the accumulation of biomass (above and below-ground components) in trees and minor vegetation for three chronosequences of stands, representing three different nutritional qualities. Tree biomass and stand self-thinning data can be derived from height, diameter at breast height, and stand density output generated by traditional growth and yield models in conjunction with species-specific biomass allometric equations [51]. To calibrate the nutritional aspects of the model, data describing the concentration of nutrients in the various biomass components are required. FORECAST also requires data on the degree of shading produced by different quantities of foliage and the photosynthetic response of foliage to different light levels. A comparable but simpler set of data for minor vegetation must be provided if the user wishes to represent this ecosystem component (see, for example, [52]). Lastly, data describing the rates of decomposition of various litter types and soil organic matter are required for the model to simulate nutrient cycling. The second aspect of calibration requires running the model in "spin-up" mode to establish initial site conditions. This component is a key feature in the ability of the model to simulate the site conditions characteristic of oil sands reclamation.

Stand hydrology and water limitation for tree growth (see [25]) are simulated within the FORECAST-Climate model [55], which on a daily time step provides a mechanistic represen‐ tation of above and belowground hydrological interactions in forest stands with multiple soil and canopy layers. This facilitates a representation of competition between trees in different canopy layers and minor vegetation for available soil water. In addition, the hydrological model also estimates the influence of drought on litter decomposition rates, and therefore on nutrient mineralization and its availability for vegetation [56]. Hence, as noted above the model tracks the balance between inputs from precipitation and seepage, and outputs by canopy

individual gross and merchantable volumes, and biomass.

of resource availability and competition.

156 14 Biodiversity in Ecosystems - Linking Structure and Function

For a broader discussion on this topic, see [7, 53, 54]).

interception, evapotranspiration, plant uptake, percolation and runoff.

Over the last four decades, a large number of ecological models that can simulate tree growth and forest hydrology have been developed for temperate and boreal ecosystems. The models best suited for simulating forest growth and hydrology in reclamation are likely to be at the scale of the stand level and in the daily to yearly time scale, as these scales provide sufficient detail to account for the key processes involved in tree growth but can also use operational data from forest management for calibration. In addition, a variety of tools have been devel‐ oped to assist biodiversity planning in forest management. Among these are statistical models that utilize correlations between forest attributes and the presence of a particular wildlife or plant species or guild to determine habitat suitability [64]. These models have gained popu‐ larity because habitat descriptors can be derived from variables commonly available in forestry databases tor through modeling (for example, timber volume, forest age, dominant tree height, and species composition) [65-68]. When properly applied, they can also be used to predict the response of selected species to forest reclamation and to evaluate the efficacy of alternative practices [6, 69, 70].

Few models achieve recognition and use much beyond their development team, and even less have used within an operational setting [7]. Even among the four shortlisted models (ECOSYS, BGC, 3PG, and FORECAST) there is considerable variation in their ultility as decision support tools, particularly within the context of reclamation.

ECOSYS [28] is a complex model, with a strong representation of plant ecophysiological processes. It is a research tool to explore energy and matter fluxes in forest ecosystems. Its calibration requirements are substantial. BGC, particularly its most recent variant BIOME-BGC, is designed to represent the state and fluxes of carbon (C), nitrogen (N), and water (H2O). The model has been applied to several forest and non-forest ecosystems around the world. The latest versions of the model include options for alternative forest management activities (see Table 4). BGC, however, is mainly a research tool designed to start from equilibrium conditions in a well-established ecosystem [71]. Hence, it is questionable whether the model is suitable for representing the biophysical characteristics of a reclaimed site. BGC also has fairly extensive and elaborate calibration requirements, though not as data-intensive as ECOSYS.

3-PG is a relatively popular forest growth model. It has been used as a research tool in a variety of forest ecosystems around the world. The model has been applied mostly in plantations, especially fast-growing species such as Eucalyptus and subtropical pines. 3-PG has been streamlined in recent years to facilitate its calibration with remote sensing data, therefore making it easy to apply to new sites and over large spatial scales [72]. One conceptual limitation of the model in terms of its application to reclamation is that site quality is represented as a fixed property [49]. This is problematic for two reasons. First, site quality must be known beforehand. This is generally not an issue in established natural forests (though it can be) but it has much greater uncertainty in a peat-based reclaimed system. Secondly, a reclaimed site is expected to transition from nutrient cycling based on the peat/mineral mix to that derived from the dead organic matter deposited by the developing plant community. It is unclear whether this transition will accompany a change in site quality. 3-PG also has no understory representation. Shrubs and herbs can be a key determinant of ecosystem development and productivity [52, 73].

FORECAST is model with a long history of development, but with a strong focus on manage‐ ment applications [50]. With the inclusion of a hydrology submodel (ForWaDy; see [25]), FORECAST now has the capability to simulate climate and climate impacts, and its impact on moisture availability, and C and N fluxes. The calibration requirements of FORECAST are moderate (but they are not trivial) though many parameters can be calibrated with standard inventory data and/or growth and yield tables. Some parameter values are universal and exhibit little variation; for others, the model is relatively insensitive to their variability (see [74], for a sensitivity analysis). Although FORECAST is a stand-alone model, it has been used for landscape-level analysis by linking it to GIS systems that classify the area under study into different ecosystem types [75, 76]. One advantage with FORECAST is that it has already been used extensively in oil sands reclamation (12, and references therein), and so datasets have already been constructed for the dominant tree and understory species. In this respect, FORECAST can be used to simulate complex mixtures of tree and understory species [77].

data from forest management for calibration. In addition, a variety of tools have been devel‐ oped to assist biodiversity planning in forest management. Among these are statistical models that utilize correlations between forest attributes and the presence of a particular wildlife or plant species or guild to determine habitat suitability [64]. These models have gained popu‐ larity because habitat descriptors can be derived from variables commonly available in forestry databases tor through modeling (for example, timber volume, forest age, dominant tree height, and species composition) [65-68]. When properly applied, they can also be used to predict the response of selected species to forest reclamation and to evaluate the efficacy of alternative

Few models achieve recognition and use much beyond their development team, and even less have used within an operational setting [7]. Even among the four shortlisted models (ECOSYS, BGC, 3PG, and FORECAST) there is considerable variation in their ultility as decision support

ECOSYS [28] is a complex model, with a strong representation of plant ecophysiological processes. It is a research tool to explore energy and matter fluxes in forest ecosystems. Its calibration requirements are substantial. BGC, particularly its most recent variant BIOME-BGC, is designed to represent the state and fluxes of carbon (C), nitrogen (N), and water (H2O). The model has been applied to several forest and non-forest ecosystems around the world. The latest versions of the model include options for alternative forest management activities (see Table 4). BGC, however, is mainly a research tool designed to start from equilibrium conditions in a well-established ecosystem [71]. Hence, it is questionable whether the model is suitable for representing the biophysical characteristics of a reclaimed site. BGC also has fairly extensive and elaborate calibration requirements, though not as data-intensive as

3-PG is a relatively popular forest growth model. It has been used as a research tool in a variety of forest ecosystems around the world. The model has been applied mostly in plantations, especially fast-growing species such as Eucalyptus and subtropical pines. 3-PG has been streamlined in recent years to facilitate its calibration with remote sensing data, therefore making it easy to apply to new sites and over large spatial scales [72]. One conceptual limitation of the model in terms of its application to reclamation is that site quality is represented as a fixed property [49]. This is problematic for two reasons. First, site quality must be known beforehand. This is generally not an issue in established natural forests (though it can be) but it has much greater uncertainty in a peat-based reclaimed system. Secondly, a reclaimed site is expected to transition from nutrient cycling based on the peat/mineral mix to that derived from the dead organic matter deposited by the developing plant community. It is unclear whether this transition will accompany a change in site quality. 3-PG also has no understory representation. Shrubs and herbs can be a key determinant of ecosystem development and

FORECAST is model with a long history of development, but with a strong focus on manage‐ ment applications [50]. With the inclusion of a hydrology submodel (ForWaDy; see [25]), FORECAST now has the capability to simulate climate and climate impacts, and its impact on moisture availability, and C and N fluxes. The calibration requirements of FORECAST are

practices [6, 69, 70].

ECOSYS.

productivity [52, 73].

tools, particularly within the context of reclamation.

158 16 Biodiversity in Ecosystems - Linking Structure and Function

"A model should be as simple as possible, but no simpler". This is the principle put forward by Albert Einstein (in reference to scientific theories) and is applicable to model construction. Complex models are often required in ecology when the interactions between different ecological factors, both biotic and abiotic, need to be explicitly represented and understood [12]. This is especially important for ecosystems in which there are often no natural analogues, such as reclaimed landscapes [78]. The four shortlisted models provide a good representation of the range of complexity and approaches used to estimate biomass production, nutrient and water cycling. These differences are also reflected in the calibration requirements and calibra‐ tion load associated with a given model. For example, ECOSYS is fundamentally a 'bottomup' model in that it integrates ecophysiological processes starting at leaf scale to generate values of biomass production and water consumption at the stand level. BGC, in contrast, is more of a top-down model. FORECAST and 3-PG are somewhere 'in-between', estimating stand productivity with some simplification of the ecophysiological processes that occur at the cellular or leaf levels. The range in modeling approaches is also a reflection of the different origins of each model; FORECAST and 3-PG are forest management models, ECOSYS began as a crop research model, and BGC a forest ecology research model.

Determining the appropriateness of a given model to support biodiversity restoration within the context of reclamation depends on the balance between the accuracy required from the model output, the calibration effort and data available for calibration, model complexity, model flexibility, model robustness, and the capability to assess model performance [51]. Highly complex models such as ECOSYS simulate a large array of ecophysiological processes at fine temporal and spatial scales. Consequently, they require a considerable effort to assemble the data required for calibration. Often, it is necessary to make educat‐ ed guesses for parameter values that are difficult to measure or which may not exist for the particular circumstances to which the model is to be applied. For obvious reasons, uncertainty in the input data reduces confidence in model output, an issue that becomes more problematic as the calibration requirements increase. Relatively simple models such as 3-PG have low calibration needs which allows for easier portability of the model to new ecosystem types. An overly simplified structure, however, also reduces model applicabili‐ ty (and flexibility) to complex systems and to account for interactions among all the ecosystem compartments. Conversely, robustness refers to a model's capability to pro‐ duce acceptable estimates of the target variables in the required application. Robustness is not an inherent property of model complexity, and both complex and simple models can be robust, provided that calibration parameters are estimated with low uncertainty, especially for those key parameters for which the model is more sensitive [7].

Recovery of biodiversity in reclaimed sites depends on the timing of reclamation events, the type of forest system reclaimed, and how progressive reclamation impacts the vegetation (understory and stem distribution) relative to what would have been present had the landscape not been mined. Reclamation practices could be targeted toward the habitat requirements of particular wildlife or vegetation species by preferentially reclaiming more favourable ecolog‐ ical sites. Conversely, a broad range of ecological sites is necessary to promote suitable habitats for a diverse range of species on the reclaimed landscape. Such planning needs decision support tools that incorporate the best scientific knowledge available.

## **Acknowledgements**

This work was conducted with funding generously supplied by Total E&P Canada Ltd., Calgary, Alberta, Canada. The opinions expressed herein are solely those of the authors and are not necessarily in accordance with that of any other group or individual.

### **Author details**

Yueh-Hsin Lo1 , Juan A. Blanco1 , Clive Welham2\* and Mike Wang3

\*Address all correspondence to: clive.welham@ubc.ca

1 Dep. Ciencias del Medio Natural, Universidad Pública de Navarra, Campus de Arrosadía, Pamplona, Navarra, Spain

2 Dep. Forest Management, University of British Columbia, 2424 Main Mall, Vanocuver, British Columbia, Canada

3 Total E&P Canada Ltd., 2900-240 4th Ave SW, Calgary, Alberta, Canada

### **References**


[3] Carey S.K. 2008. Growing season energy and water exchange from an oil sands over‐ burden reclamation soil cover, Fort McMurray, Alberta, Canada. Hydrological Proc‐ esses 22: 2847-2857.

be robust, provided that calibration parameters are estimated with low uncertainty,

Recovery of biodiversity in reclaimed sites depends on the timing of reclamation events, the type of forest system reclaimed, and how progressive reclamation impacts the vegetation (understory and stem distribution) relative to what would have been present had the landscape not been mined. Reclamation practices could be targeted toward the habitat requirements of particular wildlife or vegetation species by preferentially reclaiming more favourable ecolog‐ ical sites. Conversely, a broad range of ecological sites is necessary to promote suitable habitats for a diverse range of species on the reclaimed landscape. Such planning needs decision

This work was conducted with funding generously supplied by Total E&P Canada Ltd., Calgary, Alberta, Canada. The opinions expressed herein are solely those of the authors and

, Clive Welham2\* and Mike Wang3

1 Dep. Ciencias del Medio Natural, Universidad Pública de Navarra, Campus de Arrosadía,

2 Dep. Forest Management, University of British Columbia, 2424 Main Mall, Vanocuver,

[1] Schwarz A.G., Wein R.W. 1997. Threatened dry grassland in the continental boreal forest of Wood Buffalo National Park. Canadian Journal of Botany 75: 1363-1370.

[2] Beyen W., Meire P. 2003. Ecohydrology of saline grasslands: consequences for their

especially for those key parameters for which the model is more sensitive [7].

support tools that incorporate the best scientific knowledge available.

are not necessarily in accordance with that of any other group or individual.

3 Total E&P Canada Ltd., 2900-240 4th Ave SW, Calgary, Alberta, Canada

restoration. Applied Vegetation Science 6: 153-160.

**Acknowledgements**

160 18 Biodiversity in Ecosystems - Linking Structure and Function

**Author details**

Pamplona, Navarra, Spain

British Columbia, Canada

, Juan A. Blanco1

\*Address all correspondence to: clive.welham@ubc.ca

Yueh-Hsin Lo1

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## **Microbial Assembly in Agroecosystems — From the Small Arise the Big**

Dennis Goss de Souza, Lucas William Mendes, Acácio Aparecido Navarrete and Siu Mui Tsai

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/58992

## **1. Introduction**

Since the dawn of classical microbiology, scientists have applied efforts to unravel the ecological patterns of occurrence, distribution and function of microorganisms in several ecosystems, including soil. In the last years, the famous Baas-Becking affirmation "Everything is everywhere, but, the environment selects", have been largely used as central question, in microbial biogeographic studies and also in theoretical niche-based theories [1].

Despite we known the importance of soil "small"-microorganisms to maintain the dynamic balance and resilience of the "big"-ecosystems, little is known about their patterns of assembly and its relationship with the functions resilience due the conversion of natural areas, such as tropical forests, to agriculture. Advances arising from genomics era have enabled microbial ecologists to access the ecological dimension of genetic and functional biodiversity, through genomic sequencing techniques, at scale and depth never seen before [2].

However, a topic that remains unclear is how to analyze and interpret these patterns of biodiversity generated by millions (or even billions) of genetic and functional data, resulting in robust and concise answers about ecological issues, among them: which is/are the effect(s) of conversion of forest to agriculture on microbial ecological patterns? Moreover, how to integrate this dimension of genetic and functional biodiversity, with the dimension expressed by metabolic products and ecological relations of microorganisms, and with a third and not less important, environmental dimension, which can modulate the patterns of occurrence and distribution of microorganisms in several ecosystems around the globe? Elucidating these dimensions, through metacommunity ecology and biogeography may allow us to unravel the black box of microbial assembly and functionality in the agroecosystems, and give answers to Baas-Becking affirmation supporters and opponents.

© 2015 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. © 2014 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and eproduction in any medium, provided the original work is properly cited.

The dimension 1, has been massively analyzed through large scale sequencing of nucleic acids. Recently, metagenomic libraries from soil microbial DNA are being used as template [3], in order to evaluate the effects of the conversion of forest into agriculture [4]. The dimension 2 has been assessed by biochemical assays of production/consumption of microbial-mediated greenhouse gases and microbial enzyme activities in soil [5–8]. The Dimension 3 is evaluated by observation or data collection and analysis of environmental factors, including soil physicochemical, climatic and geographical attributes and their relation with microbial molecular parameters [9–12]. A multidimensional approach, linking these dimensions that modulate the distribution and abundance of microorganisms in the ecosystems is obtained by multivariate pairwise correlations among the parameters evaluated in both the three dimen‐ sions, generating an integrative view in systems biology (Figure 1).

**Figure 1.** Dimensions to unravel the patterns of distribution and abundance of soil microorganisms in agroecosystems.

The aim of this chapter is to provide to readers some conceptual and practical bases for analysis and interpretation of microbial metacommunity assembly (structure), functions and their linkage, with applications in agroecosystems conservation. To achieve that, we consider results from the recent advances in high throughput next-generation sequencing (NGS) and bioin‐ formatics that allow us to assess deeply, both the taxonomic, the phylogenetic and the genetic microbial biodiversity, establishing a novel border in microbial metacommunity ecology. We argue that metacommunity ecology and biogeography may be used as cornerstones to microbial ecology studies, helping us to elucidate tricking questions regarding microbial distribution and ecological relationships, from the local community level to the global level.

## **2. Molecular advances in microbial ecology**

The rapid development of molecular biology techniques at the end of the twentieth century and their successful application to the study of microbial ecology has changed our view of the assess structure and function of microorganisms. In recent years, advances in the field of molecular microbial ecology, in which are included the NGS techniques [13], have revealed a far unknown microbial biodiversity that was not detected previously by classical microbiolo‐ gy.

The dimension 1, has been massively analyzed through large scale sequencing of nucleic acids. Recently, metagenomic libraries from soil microbial DNA are being used as template [3], in order to evaluate the effects of the conversion of forest into agriculture [4]. The dimension 2 has been assessed by biochemical assays of production/consumption of microbial-mediated greenhouse gases and microbial enzyme activities in soil [5–8]. The Dimension 3 is evaluated by observation or data collection and analysis of environmental factors, including soil physicochemical, climatic and geographical attributes and their relation with microbial molecular parameters [9–12]. A multidimensional approach, linking these dimensions that modulate the distribution and abundance of microorganisms in the ecosystems is obtained by multivariate pairwise correlations among the parameters evaluated in both the three dimen‐

**Figure 1.** Dimensions to unravel the patterns of distribution and abundance of soil microorganisms in agroecosystems.

The aim of this chapter is to provide to readers some conceptual and practical bases for analysis and interpretation of microbial metacommunity assembly (structure), functions and their linkage, with applications in agroecosystems conservation. To achieve that, we consider results from the recent advances in high throughput next-generation sequencing (NGS) and bioin‐ formatics that allow us to assess deeply, both the taxonomic, the phylogenetic and the genetic microbial biodiversity, establishing a novel border in microbial metacommunity ecology. We argue that metacommunity ecology and biogeography may be used as cornerstones to microbial ecology studies, helping us to elucidate tricking questions regarding microbial distribution and ecological relationships, from the local community level to the global level.

The rapid development of molecular biology techniques at the end of the twentieth century and their successful application to the study of microbial ecology has changed our view of the

sions, generating an integrative view in systems biology (Figure 1).

1702 Biodiversity in Ecosystems - Linking Structure and Function

**2. Molecular advances in microbial ecology**

The NGS tools have decreased the relative costs of sequencing and increased massively the capacity of data production and quality. Their advances have contributed significantly to the understanding of the structure and function of soil microbial communities. Several molecular methods have been used to investigate the microbial diversity and changes in the microbial community structure in a wide range of environments (e.g. Shotgun metagenomics).

The studies in microbial ecology have been improved with the development and advance of the sequencing technologies. DNA sequencing is the process of determining the order of nucleotides that constitute a DNA molecule. The method determines the order of the four bases, i.e. adenine (A), guanine (G), cytosine (C), and thymine (T), in a strand of DNA. The DNA sequencing provides a mean of identifying organisms by comparing to databases. From a known and identified species, a molecular marker (e.g. 16S rRNA gene) is sequenced and deposited in publicly available databases for future comparison. DNA sequencing is suitable for sequence individual genes, molecular markers, larger genetic regions, full chromosomes or the entire genome. The sequencing approach is a powerful tool for the study of microbial communities inhabiting soil and could be useful to predict changes in soil properties and quality, as well as to understand the community assembly in these environments. The assessment of the microbial diversity will be advanced by the development of new technolo‐ gies that answer some key questions about the "who, what, when, where, why and how" of microbial communities [14].

The rapid development of molecular biology techniques at the end of the twentieth century and their successful application to the study of microbial ecology has changed our view of the assess structure and function of microorganisms. In recent years, advances in the field of molecular microbial ecology, in which are included the Next Generation Sequencing (NGS) techniques [13], have revealed a far unknown microbial biodiversity that was not detected previously by classical microbiology.

The advance in sequencing technologies from Sanger to 454 pyrosequencing and Illumina has opened new possibilities in microbial community analysis by making it possible to collect millions of sequences, spanning hundreds of samples. The increase in the number of sequences per run from parallel pyrosequencing technologies such as the Roche 454 GS FLX (5 x 105 ) to Illumina GAIIx (1 x 108 ) is of the order of 1,000-fold and greater than the increase in the number of sequences per run from Sanger (1 x 103 through 1 x 104 ) to 454 [15]. In addition, the use of barcode strategies allows the analysis of thousands of samples in a single run. With the advance of such technologies the read length has increased, although they are far shorter than the desirable length or the read length obtained from traditional Sanger sequencing (~1000 bp) [16]. The 454 pyrosequencing was the first next-generation sequencing technology available as a commercial product [17] and can be considered the cornerstone of the sequencing revolution. The development of the pyrosequencing method allowed an advance of metage‐ nome studies by increasing the number of reads and decreasing costs per sequence, enabling a deep phylogenetic community analysis.

The NGS tools have decreased the relative costs of sequencing and increased massively the capacity of data production and quality. Their advances have contributed significantly to the understanding of the structure and function of microbial communities. Several molecular methods are used to investigate the microbial diversity and changes in the microbial com‐ munity structure in a wide range of environments. The use of metagenomics in the studies of microbial communities has enabled researchers to have an overview not only of the diversity, but also the functional traits, which are also an important approach to link the microbial community structure to functions. The rapid advance of sequence technologies allied to bioinformatics tools are increasing the possibility of massive studies on microbial ecology for a deep comprehension of the composition and function that soil microorganisms play in a wide range of ecosystems. The new information available will be useful for a better understanding of microbial assembly, at both phylogenetic and functional aspect of a community.

## **3. The metacommunity concept in microbial ecology**

The first and simplest concept defines *metacommunity* as a set of communities that interact each other exchanging individuals of multiple species, linked by dispersal [18]. Different species interact each other via ecological relations, at the *community* (local level). There might be events of immigration, dispersal, besides other, that modulate the exchange of individuals from local communities to a broader range of communities, culminating with species evolution [19]. The use of different terms and different perspectives is a concerning question in metacommunity ecology. To reach scales of organization and set populations and communities dynamics within metacommunities, we use the terms and definitions conceptualized by [20] and applied by [21]. In order to assess the metacommunity assembly we regard the four central theories in Metacommunity Ecology, namely: (I) patch-dynamic, (II) species-sorting, (III) mass effects and (IV) neutral theory [22].

Metacommunity studies have been applied to ecology [19,23]. The patters of community distribution can vary in regional scale across environments, between environments at the same region and inside a specific environment [24]. Thus, a multidimensional approach is needed to have a comprehensive picture. To achieve that, four paradigms of metacommunities can be reached (Figure 2):

**i.** Patch-dynamic (stochastic+deterministic) – as found to the neutral model, it assumes that the habitat quality is constant across different arrays (microbial cores) of the landscape. In this model, both stochastic and deterministic extinctions are affected by interspecific relations e counterbalanced by dispersion [20].

The approaches undergoing this paradigm are based on two different versions, based in occupancy formalisms, in which the patches are occupied or vacant by certain populations at

nome studies by increasing the number of reads and decreasing costs per sequence, enabling

The NGS tools have decreased the relative costs of sequencing and increased massively the capacity of data production and quality. Their advances have contributed significantly to the understanding of the structure and function of microbial communities. Several molecular methods are used to investigate the microbial diversity and changes in the microbial com‐ munity structure in a wide range of environments. The use of metagenomics in the studies of microbial communities has enabled researchers to have an overview not only of the diversity, but also the functional traits, which are also an important approach to link the microbial community structure to functions. The rapid advance of sequence technologies allied to bioinformatics tools are increasing the possibility of massive studies on microbial ecology for a deep comprehension of the composition and function that soil microorganisms play in a wide range of ecosystems. The new information available will be useful for a better understanding

of microbial assembly, at both phylogenetic and functional aspect of a community.

The first and simplest concept defines *metacommunity* as a set of communities that interact each other exchanging individuals of multiple species, linked by dispersal [18]. Different species interact each other via ecological relations, at the *community* (local level). There might be events of immigration, dispersal, besides other, that modulate the exchange of individuals from local communities to a broader range of communities, culminating with species evolution [19]. The use of different terms and different perspectives is a concerning question in metacommunity ecology. To reach scales of organization and set populations and communities dynamics within metacommunities, we use the terms and definitions conceptualized by [20] and applied by [21]. In order to assess the metacommunity assembly we regard the four central theories in Metacommunity Ecology, namely: (I) patch-dynamic, (II) species-sorting, (III) mass effects and

Metacommunity studies have been applied to ecology [19,23]. The patters of community distribution can vary in regional scale across environments, between environments at the same region and inside a specific environment [24]. Thus, a multidimensional approach is needed to have a comprehensive picture. To achieve that, four paradigms of metacommunities can be

**i.** Patch-dynamic (stochastic+deterministic) – as found to the neutral model, it assumes

The approaches undergoing this paradigm are based on two different versions, based in occupancy formalisms, in which the patches are occupied or vacant by certain populations at

by interspecific relations e counterbalanced by dispersion [20].

that the habitat quality is constant across different arrays (microbial cores) of the landscape. In this model, both stochastic and deterministic extinctions are affected

**3. The metacommunity concept in microbial ecology**

a deep phylogenetic community analysis.

1724 Biodiversity in Ecosystems - Linking Structure and Function

(IV) neutral theory [22].

reached (Figure 2):

**Figure 2.** Representation of four metacommunity hypothetical situations at the local scale (dashed brown circle) and at the regional scale (dashed blue frame). Schematic situations for two competing species with populations A and B. (a) Patch-dynamic paradigm, (b) species-sorting paradigm, (c) mass-effects paradigm and (d) neutral paradigm. Adapted from [20].

their equilibrium. Both versions assume that the local and the regional population dynamics have a time gap, which means that the effects of changes in extinction-colonization patterns in the local community take certain period to affect the regional metacommunity dynamics.

In the first version for this paradigm, only regional coexistence is considered to influence the metacommunity patterns. It assumes that species that coexist compete for niche resources, but there is no interactions between species that influence local community dynamics, since local communities are not considered in the model. In the second version, given a homogeneous environment where a set of species co-occur in equilibrium, the regional coexistence is possible due a trade-off between competition and dispersal or fecundity and dispersal [25] (Figure 1a). A limitation of patch-dynamic paradigm is that a set of local communities or patches are assumed identical.

**ii.** Species-sorting (deterministic) – based on the traditional theory of niche segregation of species that co-occur in certain environment [26]. The theory infers about changes in communities across environmental gradients [27].

In this case, the role of environmental parameters such as soil fertility and plant cover species, soil organic matter content, besides other, acquire fundamental importance in modulate the patterns of distribution and abundance of microorganisms [21]. The species-sorting paradigm infers that local patches differ in some attributes and the result of local species interactions depends on the environmental abiotic factors [28].

This paradigm assumes that different habitats patches are heterogeneous and that rates of dispersal are moderate, which means that all species have similar probability to reach all patches of the metacommunity (Figure 1b). Thus, it is expected to occur a species-sorting through niche partitioning, since there species are differently adapted to particular conditions, defined by environmental gradients.

**iii.** Mass-effects (deterministic) – assumes that a certain population can vary in regional and local scales. This population can be affected quantitatively by dispersion. This model of mass effects due dispersion requires that different arrays of habitat have different conditions in certain moment, and that these relations should sufficiently tightly related. Thus, dispersion results in a sink/source dynamic between popula‐ tions in different arrays at the landscape [29].

Dispersal has a great role in mass-effects paradigm. In one hand, an increase in immigration rates might enhance the abundance of certain populations in a local community, in detriment of neighbor communities from the metacommunity. In other hand an increase in emigration rates can decrease the rates of birth of local populations apart from the abundance expected in a close metacommunity (Figure 1c). Considering competing species of microorganisms in a certain environment where the total community has a constant abundance, a fluctuation in local population abundances may occur by chance [29].

**iv.** Neutral Model (stochastic) – one thing that all the previous paradigms have in common is the assumption that species in local communities differ from each other in their capacities of competition for niche occupation. The dynamics of a metacom‐ munity depends on the trade-offs resulting from the assembly of several co-occurring populations.

Neutral paradigm emerges as a null hypothesis for microbial assembly. Thus, in its models, the persistence in a certain community is the result of random processes of immigration and extinction (Figure 1d). The species have equal competition capacity [22]. Neural theory is the simplest way to characterize the complexity of a set of populations in a local community. To asses that, we only need a **θ** number of potential species in a given community, and a *m* immigration rate parameter [30].

A classical approach to evaluate neutral paradigm was described by [22], through a reinterpretation of Ewens' sampling distribution, that was initially developed for genetics studies [31]. The model undergoing this approach is based on zero-sum dynamics of a metacommunity. Indices deriving from this view are being also used for local communities [32], with recent applications in studies related to the patterns of microbial assembly in agroecosystems and rhizosphere [4,21]

A more comprehensive picture of the application of these paradigms in microbial ecology studies can be reached by both theoretical (e.g. Classic Metapopulation – neutral) and numerical (e.g. Zero-Sum Model – neutral, Broken-Stick Model – niche-based) models. The models describe the organization of microorganisms into communities, at the local level [4], and along metacommunities, at the regional level [21] (e.g. Rates of dispersal and immigration coefficient).

## **4. Application of metacommunity models to unravel microbial structure and functions in agroecosystems**

This paradigm assumes that different habitats patches are heterogeneous and that rates of dispersal are moderate, which means that all species have similar probability to reach all patches of the metacommunity (Figure 1b). Thus, it is expected to occur a species-sorting through niche partitioning, since there species are differently adapted to particular conditions,

**iii.** Mass-effects (deterministic) – assumes that a certain population can vary in regional

Dispersal has a great role in mass-effects paradigm. In one hand, an increase in immigration rates might enhance the abundance of certain populations in a local community, in detriment of neighbor communities from the metacommunity. In other hand an increase in emigration rates can decrease the rates of birth of local populations apart from the abundance expected in a close metacommunity (Figure 1c). Considering competing species of microorganisms in a certain environment where the total community has a constant abundance, a fluctuation in

**iv.** Neutral Model (stochastic) – one thing that all the previous paradigms have in

Neutral paradigm emerges as a null hypothesis for microbial assembly. Thus, in its models, the persistence in a certain community is the result of random processes of immigration and extinction (Figure 1d). The species have equal competition capacity [22]. Neural theory is the simplest way to characterize the complexity of a set of populations in a local community. To asses that, we only need a **θ** number of potential species in a given community, and a *m*

A classical approach to evaluate neutral paradigm was described by [22], through a reinterpretation of Ewens' sampling distribution, that was initially developed for genetics studies [31]. The model undergoing this approach is based on zero-sum dynamics of a metacommunity. Indices deriving from this view are being also used for local communities [32], with recent applications in studies related to the patterns of microbial assembly in

A more comprehensive picture of the application of these paradigms in microbial ecology studies can be reached by both theoretical (e.g. Classic Metapopulation – neutral) and numerical (e.g. Zero-Sum Model – neutral, Broken-Stick Model – niche-based) models. The models describe the organization of microorganisms into communities, at the local level [4], and along metacommunities, at the regional level [21] (e.g. Rates of dispersal and

common is the assumption that species in local communities differ from each other in their capacities of competition for niche occupation. The dynamics of a metacom‐ munity depends on the trade-offs resulting from the assembly of several co-occurring

and local scales. This population can be affected quantitatively by dispersion. This model of mass effects due dispersion requires that different arrays of habitat have different conditions in certain moment, and that these relations should sufficiently tightly related. Thus, dispersion results in a sink/source dynamic between popula‐

defined by environmental gradients.

1746 Biodiversity in Ecosystems - Linking Structure and Function

populations.

immigration rate parameter [30].

agroecosystems and rhizosphere [4,21]

immigration coefficient).

tions in different arrays at the landscape [29].

local population abundances may occur by chance [29].

Although we know a lot about plant and animal distribution, demography and functions, these patterns remain abstruse, when it comes to microorganisms. Knowledge about microbial assembly and functions, due conversion of pristine forests into agricultural systems, is vital to understand the possible ecological consequences.

Biogeography and metacommunity ecology studies have made possible to investigate the mechanisms leading to microbial diversity generation and maintenance in these ecosystems, such as emergence of new species, extinction, dispersal and ecological interactions [20] in several levels of complexity and scale ranges. A framework to investigate microbial patterns is needed, with references to that found to macroorganisms and the establishment of possible exceptions regarding microbial assembly and functional niche occupation. This comprise the knowledge whether microbial assembly differ among environments and space, besides the effects that modulate this variation. Moreover, a biogeographic multiscale approach can help us to unravel if spatial variation is due to punctual environmental factors, such as land-use and seasonality [11] or evolutionary selecting events [33].

As mentioned in the previous section, different species inside a local community and even communities along a metacommunity, use to have different patterns of assembly through space and time, due different ability to compete, occupy niches, and disperse along the ecosystems gradient. Thus, besides the application of metacommunity paradigms to describe microbial assembly and niche occupancy, we can also argue about the limitations and barriers to dispersal that make some species behavior to differ in a biogeographic scale (Figure 3).

**Figure 3.** Relationship between geographic distance and microbial community dissimilarity. (a) According to the as‐ sumption "Everything is everywhere", all communities appear to be similar to each other, apart from distance. (b) Communities have a continuous decay of dissimilarity over geographic distance. (c) Communities have autocorrela‐ tion until a threshold (vertical dashed line), in which the limit of spatial correlation is reached. (d) Communities have a lag before autocorrelation begins (vertical dashed line), what means that in low distances, we are sampling systemati‐ cally the same community. Adapted from [34].

An early conceptual groundwork for microbial biogeography can be found in Candolle province and habitat definitions for plants [35]. Bringing that into microbial boundaries, a province could be any area, in which the microbial structure reflects historical evolutionary events. Thus, the limits of a single province should vary greatly in size and are inwardly linked to the resolution and the taxa in study [33]. Areas of soybean cultivation, hundreds of miles distant each other, might be considered particular provinces, considering the general structure of bacterial communities that inhabit their rhizosphere and surrounding soil. Although, those areas may also be treated as members of a single province, taking into account that members of the bacterial class Alphaproteobacteria, are able to colonize the rhizosphere and nodulate these plants, apart from distance, due a high level of conserved genes related to this mechanism and a large number of strategies of signaling to a broader range of environments and plant species [36].

### **4.1. Local, regional and global factors affect soil microbial community structure**

Understanding controls over the distribution of soil microbial communities is a fundamental step toward describing soil ecosystems, understanding their functional capabilities, and predicting their responses to environmental change. However, the complexity of these communities and their interactions with environmental characteristics have made generali‐ zations difficult. Recently, high throughput sequencing technologies have facilitated the investigation of soil bacterial communities at local [37], regional [12], and global scales [38].

Microbial groups related to environmental characteristics has been recognized as the most important mechanism controlling soil microbial communities [39] with chemical soil factors identified as a master variable explaining significant portions of the variation in soil microbial diversity and community structure at local [40,41], regional [42–44] and global [45,46] scales.

However, while environmental factors have been identified as exerting primary control on soil microbial distribution, on average approximately 50% of the variation in microbial diversity and structure remains unexplained [39]. Additionally, very few examinations have been made of how controls on soil microbial communities operate simultaneously at multiple scales to contrast local and regional drivers of microbial diversity and community structure.

The factors that control soil microbial community composition are much debated. It has been suggested that while local scale variation in soil microbial communities can be explained by plant identity, substrate hotspots and soil chemical factors [47–49], at regional and continental scales, additional factors, such as climate, topography, and soil pH, become more important [50–52]. However, pH has been shown to shape soil microbial communities over distances < 1 m2 [53], as well as at field and continental scales [38,41]. Microbial communities have also been shown to be influenced by vegetation type, land use, soil nutrient status, and soil organic matter quality and quantity at landscape scales [51,52,54].

The relationship among soil microbial communities and landscape factors, soil factors and plant communities at different spatial scales is relatively lacking, despite their importance for ecosystem functioning. This lack of understanding of the factors that explain variation in microbial communities at larger spatial scales is surprising given their functional importance in regulating ecosystem processes, such as carbon and nitrogen cycling [49,55] and the resistance of nutrient cycles to climate change-related disturbances [56]. In this sense, no studies have simultaneously tested the importance of a range of abiotic factors, including climate and soil properties, and biotic factors, such as vegetation composition, across a wide range of spatial scales. According to [56], this represents a major gap in knowledge given the potential for both abiotic and biotic factors to explain variation in soil microbial communities.

An early conceptual groundwork for microbial biogeography can be found in Candolle province and habitat definitions for plants [35]. Bringing that into microbial boundaries, a province could be any area, in which the microbial structure reflects historical evolutionary events. Thus, the limits of a single province should vary greatly in size and are inwardly linked to the resolution and the taxa in study [33]. Areas of soybean cultivation, hundreds of miles distant each other, might be considered particular provinces, considering the general structure of bacterial communities that inhabit their rhizosphere and surrounding soil. Although, those areas may also be treated as members of a single province, taking into account that members of the bacterial class Alphaproteobacteria, are able to colonize the rhizosphere and nodulate these plants, apart from distance, due a high level of conserved genes related to this mechanism and a large number of strategies of signaling to a broader range of environments and plant

**4.1. Local, regional and global factors affect soil microbial community structure**

Understanding controls over the distribution of soil microbial communities is a fundamental step toward describing soil ecosystems, understanding their functional capabilities, and predicting their responses to environmental change. However, the complexity of these communities and their interactions with environmental characteristics have made generali‐ zations difficult. Recently, high throughput sequencing technologies have facilitated the investigation of soil bacterial communities at local [37], regional [12], and global scales [38].

Microbial groups related to environmental characteristics has been recognized as the most important mechanism controlling soil microbial communities [39] with chemical soil factors identified as a master variable explaining significant portions of the variation in soil microbial diversity and community structure at local [40,41], regional [42–44] and global [45,46] scales.

However, while environmental factors have been identified as exerting primary control on soil microbial distribution, on average approximately 50% of the variation in microbial diversity and structure remains unexplained [39]. Additionally, very few examinations have been made of how controls on soil microbial communities operate simultaneously at multiple scales to

The factors that control soil microbial community composition are much debated. It has been suggested that while local scale variation in soil microbial communities can be explained by plant identity, substrate hotspots and soil chemical factors [47–49], at regional and continental scales, additional factors, such as climate, topography, and soil pH, become more important [50–52]. However, pH has been shown to shape soil microbial communities over distances < 1 m2 [53], as well as at field and continental scales [38,41]. Microbial communities have also been shown to be influenced by vegetation type, land use, soil nutrient status, and soil organic

The relationship among soil microbial communities and landscape factors, soil factors and plant communities at different spatial scales is relatively lacking, despite their importance for ecosystem functioning. This lack of understanding of the factors that explain variation in microbial communities at larger spatial scales is surprising given their functional importance

contrast local and regional drivers of microbial diversity and community structure.

matter quality and quantity at landscape scales [51,52,54].

species [36].

1768 Biodiversity in Ecosystems - Linking Structure and Function

### **4.2. Case study: Niche-based theory explains microbial assembly in soybean rhizosphere**

The rhizosphere is the immediate surroundings of the plant root, the portion of soil under influence of root exudates. The rhizosphere is considered a hot spot of microbial species, and the communities inhabiting this environment are shaped by the nutrients released by the plant, such as exudates, border cells and mucilage. Studies on rhizosphere microbiome increased in the last year, mainly because this microbiota can have profound effects on the growth, nutrition and health of plants in agroecosystems [for rhizosphere microbiome review see [57]].

In an experimental research, [4] studied the process of microbial selection in the rhizo‐ sphere from bulk soil reservoir under agricultural management of soybean in Amazon forest soils. They used a shotgun metagenomics approach to investigate the taxonomic and functional diversities of microbial communities and to test the validity of neutral and niche theories to explain the community assembly in the rhizosphere. The species rank abun‐ dance distribution generated by metagenomics was fitted to five theoretical models of assembly. The neutral theory predicts that rank abundance distribution will be consistent with ZSM model [22] and niche-based fits the pre-emption, broken stick, log-normal and Zipf-Mandelbrot models [58–60].

The authors collected samples of bulk and rhizosphere soil of soybean harvested in agricultural fields in order to evaluate which microbial groups and functional genes are selected in the rhizosphere when compared to the bulk soil. At first, they showed that there is a selection process in the rhizosphere, where the species abundance fitted the log-normal distribution model, which is an indicator of the occurrence of niche-based process. The niche theory predicts that changes in species community composition are related to changes in environ‐ mental variables, since species have unique properties that allow them to exploit unique niches available [61]. Thus, the root exudates may select organisms to inhabit the rhizosphere environment.

With the sequencing data, the authors also could show what groups of organisms are selected in the rhizosphere and what function they are playing. In this study [4], they showed that there was a selection process at both taxonomic and functional levels operating in the assembly of the rhizospheric community, with different community structure compared to the bulk soil community. The phyla Actinobacteria, Acidobacteria, Chloroflexi, Cyanobacteria, Chlamy‐ diae, Tenericutes, Deferribacteres, Chlorobi, Verrucomicrobia and Aquificae were selected in the rhizosphere. In addition, the functional analysis indicated that functions related to the metabolism of nutrients, such as nitrogen, phosphorus, potassium and iron were more abundant in rhizosphere than the bulk soil (Figure 4).

**Figure 4.** Taxonomical and functional groups selected in soybean rhizosphere, following the niche-based mechanisms.

The phyla indicate in the figure 4 were selected in the rhizosphere and are playing important functions related to the metabolism of some important nutrients to the plant. The community selection in rhizosphere is influenced by exudates released from the roots, which create different niches to be exploited by the soil microorganisms. The roots deposits consist mainly of carbon, and secondary metabolites such as antimicrobial compounds and flavonoids. Other soil parameters also are affected by the root system, such as pH, moisture, oxygen pressure and nutrient availability. In this study, the authors used a community assembly approach to understand the microbial selection process in rhizosphere, and they have shown that soybean selects a specific microbial community inhabiting the rhizosphere based on functional traits, which may be related to benefits to the plant, as growth promotion and nutrition. The microbial community assembly in the rhizosphere follows largely the niche-based mechanisms, showing that variations in the rhizosphere promoted by roots exudates shape the microbial community structure.

## **5. Concluding remarks**

In this chapter, we have discussed the considerations of the applications of metacommunity theories and biogeography for land-use management and agroecosystems conservation. The contribution of these models to explain the patterns of structure, abundance and functional traits at local and regional scales were emphasized here. We settled some bases for the application of metacommunity models, regarding community assembly and microbial functions in agroecosystems, including recent results from our group and several theoretical and experimental studies available in the literature.

Studies of microbial assembly and its linkage to the functional resilience in the agroecosystems are very important for microbial ecologists. Comparative studies in different agroecosystems and regions of the globe are needed, to stablish a huge conceptual view about the patterns of microbial distribution and ecological relationships. Based on these several studies, we can argue about soil quality and find global bioindicators of soil health as well as endemic microbial populations with local and regional importance to maintain the ecosystems equilibrium and guarantee the biodiversity, acting as niche holders.

Metacommunity and biogeography concepts emerge as important tools to evaluate bioindi‐ cators of soil quality and functional resilience, since both can be applied to a broader range of environments, from the microcosm scale up to the landscape or regional scale, independently of the type of soil, management or species to be reached.

## **Acknowledgements**

We wish to thank Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP 2008/58114-3, 2011/51749-6) and Conselho Nacional de Desenvolvimento Científico e Tecno‐ lógico (CNPq 485801/2011-6) for funding.

## **Author details**

**Figure 4.** Taxonomical and functional groups selected in soybean rhizosphere, following the niche-based mechanisms.

The phyla indicate in the figure 4 were selected in the rhizosphere and are playing important functions related to the metabolism of some important nutrients to the plant. The community selection in rhizosphere is influenced by exudates released from the roots, which create different niches to be exploited by the soil microorganisms. The roots deposits consist mainly of carbon, and secondary metabolites such as antimicrobial compounds and flavonoids. Other soil parameters also are affected by the root system, such as pH, moisture, oxygen pressure and nutrient availability. In this study, the authors used a community assembly approach to understand the microbial selection process in rhizosphere, and they have shown that soybean selects a specific microbial community inhabiting the rhizosphere based on functional traits, which may be related to benefits to the plant, as growth promotion and nutrition. The microbial community assembly in the rhizosphere follows largely the niche-based mechanisms, showing that variations in the rhizosphere promoted by roots exudates shape the microbial community

In this chapter, we have discussed the considerations of the applications of metacommunity theories and biogeography for land-use management and agroecosystems conservation. The contribution of these models to explain the patterns of structure, abundance and functional traits at local and regional scales were emphasized here. We settled some bases for the application of metacommunity models, regarding community assembly and microbial functions in agroecosystems, including recent results from our group and several theoretical

Studies of microbial assembly and its linkage to the functional resilience in the agroecosystems are very important for microbial ecologists. Comparative studies in different agroecosystems

structure.

**5. Concluding remarks**

178 10 Biodiversity in Ecosystems - Linking Structure and Function

and experimental studies available in the literature.

Dennis Goss de Souza1,2, Lucas William Mendes1 , Acácio Aparecido Navarrete1 and Siu Mui Tsai1\*

\*Address all correspondence to: tsai@cena.usp.br

1 Cellular and Molecular Biology Laboratory, Center for Nuclear Energy in Agriculture (CENA), University of São Paulo (USP), Piracicaba, Brazil

2 Department of Land, Air and Water Resources, College of Agricultural and Environmental Sciences, University of California, Davis (UCDavis), Davis, USA

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## **Impact of Organic Farming on Biodiversity**

Martina Bavec and Franc Bavec

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/58974

## **1. Introduction**

In last several decades agriculture has been oriented towards industrial and extremely intensive farming practices, aimed at ensuring enough food for the human population, a goal that was not achieved. These types of farming practices also caused several negative environ‐ mental impacts such as decreasing biodiversity, including the farm bird index, where a decline has been observed in Slovenia since 2008. Many farms intensified their activities and became highly mechanized, whilst those unable to do so became increasingly marginalized and were sometimes forced to abandon their land, causing equally devastating consequences for biodiversity [1]. Today, it is globally imperative that the growing demand for food be met in a manner that is socially equitable and ecologically sustainable over the long term. It is possible to design farming systems that are equally productive and that maintain or enhance the provisioning of ecosystem services (i.e., biodiversity, soil quality, nutrient management, waterholding capacity, control of weeds, diseases and pests, pollination services, carbon sequestra‐ tion, energy efficiency and reducing global warming potential, as well as resistance and resilience to climate change and crop productivity) and thus agroecosystem resilience and sustainability [2].

Organic agriculture refers to a farming system that enhances soil fertility by maximizing the efficient use of local resources, while foregoing the use of agrochemicals, genetically modified organisms and the many synthetic compounds used as food additives. The high quality of organic food and its added value relies on a number of farming practices based on ecological cycles, and aims at minimizing the environmental impact of the food industry, preserving the long term sustainability of soil and reducing to a minimum the use of nonrenewable resources [3].

Organic farming practices have been promoted as reducing the environmental impacts of agriculture. The results of meta-analysis of studies that compare the environmental impacts

© 2015 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. © 2015 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

of organic and conventional farming in Europe show that organic farming practices generally have positive impacts on the environment per unit of area, but not necessarily per product unit. Significant differences between the two farming systems include soil organic matter content, nitrogen leaching, nitrous oxide emissions per unit of field area, energy use and land use. Most of the studies that compared biodiversity in organic and conventional farming demonstrated lower environmental impacts from organic farming [4]. Furthermore, organic farming appears to perform better than conventional farming and also provides other important environmental advantages such as halting the use of harmful chemicals and their spread in the environment and along the trophic chain, and reducing water use [3]. A life cycle analysis approach calculating the ecological footprint of different productions systems confirmed, respectively, 8.5 and 5.9 times better environmental performance of organic farming practices, compared to their conventional counterparts in winter wheat and spelt production [5].

Biodiversity loss and the degradation of ecosystems have important implications for the environment and are costly for society as a whole [6]. In Europe, loss of plant biodiversity is primarily reflected in the decline of many species of plants and in the disappearing of local and old plant varieties. In 2011, the European Parliament adopted the European Union (EU) Biodiversity Strategy to 2020 with aim of preventing biodiversity loss and the degradation of ecosystems. The strategy includes combating invasive alien species that jeopardize biodiver‐ sity and aims also at enhancing the positive contribution of European agriculture, forest and fishery sectors to biodiversity conservation and sustainable use, and to increase by 2020 the EUs contribution to drawing attention to global biodiversity loss [1]. The World Trade Organization notes a crop variety loss of 75% during the past 100 years, even of 90% in the EU. Only 17% of species and habitats assessed under the Habitats Directive have been deemed to be in good status and the degradation and loss of natural capital is jeopardizing efforts for attaining the EUs biodiversity and climate change objectives [7, 4], which did not reach its 2010 biodiversity target [1].

Organic farming is a production method that preserves or even enrich biodiversity at the field level, at the farm level and in the ecosystem as per its regulatory demands, where the objectives of organic farming in EU regulation 834/2007 is noted thus: that organic farming shall pursue to establish a sustainable management system for agriculture with respect to nature's systems and cycles, and sustain and enhance the health of soil, water, plants and animals and the balance between them, and to contribute to a high level of biological diversity [5]. Organic farming systems generally harbour larger floral and faunal biodiversity, more so than conventional systems; however, when properly managed, the latter can also improve biodi‐ versity. Importantly, the landscape surrounding farmed land also appears to have the potential to enhance biodiversity in agricultural areas [3]. However, the benefits of organic farming to biodiversity in agriculture landscapes are still being discussed.

Agrobiodiversity is an important aspect of biodiversity that is directly influenced by different production methods, especially at the field level. It can also supply several ecosystem services to agriculture, thus reducing environmental externalities and the need for off-farm inputs. Organic farming is considered an environmentally-friendly agricul‐ ture practice and a holistic approach encompassing several demands and bans from a regulatory point of view [8], and receives primarily from European countries additional agri-environmental payments for ecosystem services, including biodiversity. In some countries, payments are available as single biodiversity measures (i.e., hedgerows, insecta‐ ry strips, crop rotation, or the retention of semi-natural areas) in agri-environmental programmes that are also aimed at conventional agriculture.

## **2. Aim and methodology**

of organic and conventional farming in Europe show that organic farming practices generally have positive impacts on the environment per unit of area, but not necessarily per product unit. Significant differences between the two farming systems include soil organic matter content, nitrogen leaching, nitrous oxide emissions per unit of field area, energy use and land use. Most of the studies that compared biodiversity in organic and conventional farming demonstrated lower environmental impacts from organic farming [4]. Furthermore, organic farming appears to perform better than conventional farming and also provides other important environmental advantages such as halting the use of harmful chemicals and their spread in the environment and along the trophic chain, and reducing water use [3]. A life cycle analysis approach calculating the ecological footprint of different productions systems confirmed, respectively, 8.5 and 5.9 times better environmental performance of organic farming practices, compared to their conventional counterparts in winter wheat and spelt

Biodiversity loss and the degradation of ecosystems have important implications for the environment and are costly for society as a whole [6]. In Europe, loss of plant biodiversity is primarily reflected in the decline of many species of plants and in the disappearing of local and old plant varieties. In 2011, the European Parliament adopted the European Union (EU) Biodiversity Strategy to 2020 with aim of preventing biodiversity loss and the degradation of ecosystems. The strategy includes combating invasive alien species that jeopardize biodiver‐ sity and aims also at enhancing the positive contribution of European agriculture, forest and fishery sectors to biodiversity conservation and sustainable use, and to increase by 2020 the EUs contribution to drawing attention to global biodiversity loss [1]. The World Trade Organization notes a crop variety loss of 75% during the past 100 years, even of 90% in the EU. Only 17% of species and habitats assessed under the Habitats Directive have been deemed to be in good status and the degradation and loss of natural capital is jeopardizing efforts for attaining the EUs biodiversity and climate change objectives [7, 4], which did not reach its 2010

Organic farming is a production method that preserves or even enrich biodiversity at the field level, at the farm level and in the ecosystem as per its regulatory demands, where the objectives of organic farming in EU regulation 834/2007 is noted thus: that organic farming shall pursue to establish a sustainable management system for agriculture with respect to nature's systems and cycles, and sustain and enhance the health of soil, water, plants and animals and the balance between them, and to contribute to a high level of biological diversity [5]. Organic farming systems generally harbour larger floral and faunal biodiversity, more so than conventional systems; however, when properly managed, the latter can also improve biodi‐ versity. Importantly, the landscape surrounding farmed land also appears to have the potential to enhance biodiversity in agricultural areas [3]. However, the benefits of organic farming to

Agrobiodiversity is an important aspect of biodiversity that is directly influenced by different production methods, especially at the field level. It can also supply several ecosystem services to agriculture, thus reducing environmental externalities and the need for off-farm inputs. Organic farming is considered an environmentally-friendly agricul‐

biodiversity in agriculture landscapes are still being discussed.

production [5].

1862 Biodiversity in Ecosystems - Linking Structure and Function

biodiversity target [1].

The aim of this paper is to establish whether organic farming fulfils the promise of protecting biodiversity better than conventional farming, based on the review of recent publications emphasizing the importance of precisely quantifying the effect of organic vs. conventional farming. Additional to an extensive review, data from the University of Maribor regarding the effects of different production systems on the earthworm population [9] and the biodiversity of weed species from field experiments in the north east of Slovenia [10] were compared with other findings.

The reader is kindly referred to previously mentioned sources [5, 9, 10, 36] for a detailed description of differences between farming systems. For a better general understanding, some details are explained. Earthworms were collected in October 2009, 2010 and 2011 using a mustard aqueous solution as a non-toxic irritant that drove deep burrowing earthworm species to the surface [11]. After measurements were taken, earthworms were returned back to the soil. Analyses were carried out using the Statgraphics Centurion XV statistical program [12]. The biodiversity of weed species [9] was measured using two methods: (i) above-ground weed population sampling; (ii) seedbank sampling. The size of the weed seedbank was determined within the 0 to 0.2 m soil layer of each plot using the green‐ house emergence method [13]. The in situ number of the above-ground weed population per m2 was measured at the end of June or at the beginning of July 2009, 2010 and 2011, after mechanical control and the use of herbicides. Weeds were counted in four 0.25 m2 quadrates randomly located in the centre of each plot, parallel to the working direction of machinery. The weed species were determined when a 2/3 population was at the stages of 2 to 3 true leaves and 1/3 was at the stages of 4 to 5 true leaves. Species diversity was calculated for both seedbank and weed communities using H' [14].

## **3. Results and discussion**

### **3.1. Overall data**

Results of several research studies and published scientific articles showed that organic farming benefits to the environment, including biodiversity. Comparison of biodiversity in organic and conventional farms has shown that organic farming generally had positive impacts on many species [15]. Results of meta-analyses that compared biodiversity in organic and conventional farms found that organic farms generally have 30% higher species richness and 50% higher abundance of organisms than conventional farms. However, there are wide variations between different studies, which have to be discussed; for example, 16% of studies found a negative effect of organic farming on species richness. Additionally, it was also found that the effect of organic farming on species richness was larger for intensively managed landscapes than for diverse landscapes with many non-crop biotopes [16]. In 327 out of 396 relevant results [17], a higher degree of biodiversity in organic farming was found when compared to conventional farming. In 56 papers (14 %), no difference was verified and in 13 contributions (3%), organic farming yielded less biodiversity (seven of them for soil inverte‐ brates). Significantly, the positive effect of organic farming on biodiversity compared to conventional farming was noticed in 80% of cases; in 16%, differences were unclear and less biodiversity was found in 4% of comparisons (Table 1).


<sup>1</sup> Multiple citations of used studies are possible due to different conclusions for different species or multiple answers; 2 biodiversity indicators i.e., flora, weeds, soil biota, earthworms, pollinators, birds, etc.

**Table 1.** Impact of organic farming on biodiversity based on the literature review.

On average, organic farming increased species richness by about 30%. This result has been robust over the past 30 years of published studies. Organic farming had a greater effect on biodiversity as the percentage of arable fields of the landscape increased, that is, it is higher in intensively farmed regions [19]. Thus, it may be concluded that organic farming produces more biodiversity. Research gaps still exist for the understanding of functional biodiversity and ecosystem impact, which comprise soil biota, landscape (ecosystem and habitat) and genetic biodiversity on agricultural land in natural habitats [17]. The majority of current studies are from Northern and Western Europe and North American agriculture practices, while other regions with large areas of organic farming have been poorly investigated. Comparison between paired organic and conventional fields in India assessed a wide range of taxa (plants, soil microbes, earthworms, butterflies, dragonflies and other arthropods, reptiles, molluscs, amphibians/frogs and birds) trough different methods that showed similar trends. Habitat area, composition and management of organic fields were likely to favour higher levels of biodiversity by supporting higher numbers of species, dominance and abundance across most taxa. Organic fields are systems that are less dependent on external inputs to restore and rejuvenate the environment, resulting in higher biodiversity that promotes higher sustainable production on a long-term basis [20]. The effects of time since conversion to organic farming on species richness and abundance have been poorly researched. Plant and butterfly species richness was 20% higher on organic farms and butterfly abundance was about 60% higher, compared to conventional farms. Time since conversion to organic farming affected butterfly abundance gradually over a 25-year period, resulting in a 100% increase; however, no effect was found on plant or butterfly species richness, indicating that the main effect took place immediately after the conversion to organic farming [21].

Three recent multiregional studies from Europe have also demonstrated the negative effects of both agricultural intensification (increased use of synthetic fertilizers and pesticides combined with the reduced use of diversified farming system techniques) and landscape simplification on components of biodiversity [2]. The EU Biodiversity Strategy to 2020 also focuses on sustainable farming and forestry as the focus of one of six targets in the form of improving the integration of biodiversity conservation into key policies for agriculture and forestry. Combined, these two sectors include almost 72% of land in the EU and play a major role in Europe's biodiversity [1].

Crop rotation brings biodiversity in the time scale. It is mandatory on organic farms and is stated as a method to maintain and increase the fertility and biological activity of the soil, and means the prevention of damage caused by pests, diseases and weeds [8]. Due to more diverse crop rotation and the use of green manure and intercroppings on organic farms, there is also greater biodiversity. Furthermore, using domestic populations of seed varieties preserves biodiversity, but the production of alternative crops (rare, underutilized, disregarded, neglected or new) increase biodiversity at the filed level [22].

### **3.2. Weeds biodiversity**

and conventional farms found that organic farms generally have 30% higher species richness and 50% higher abundance of organisms than conventional farms. However, there are wide variations between different studies, which have to be discussed; for example, 16% of studies found a negative effect of organic farming on species richness. Additionally, it was also found that the effect of organic farming on species richness was larger for intensively managed landscapes than for diverse landscapes with many non-crop biotopes [16]. In 327 out of 396 relevant results [17], a higher degree of biodiversity in organic farming was found when compared to conventional farming. In 56 papers (14 %), no difference was verified and in 13 contributions (3%), organic farming yielded less biodiversity (seven of them for soil inverte‐ brates). Significantly, the positive effect of organic farming on biodiversity compared to conventional farming was noticed in 80% of cases; in 16%, differences were unclear and less

> **Significantly positive effect – more biodiversity**

**No significant differences – unclear, indifferent**

**Significantly negative effect – less biodiversity**

biodiversity was found in 4% of comparisons (Table 1).

**Number of biodiversity indicators2**

biodiversity indicators i.e., flora, weeds, soil biota, earthworms, pollinators, birds, etc.

**Table 1.** Impact of organic farming on biodiversity based on the literature review.

Rahmann [17] 343 10 327 56 13 Hole et al. [15] 76 9 66 25 8 Pfiffner [18] 44 7 49 5 1 Sum 442 86 22 Share (%) 80 16 4

<sup>1</sup> Multiple citations of used studies are possible due to different conclusions for different species or multiple answers; 2

On average, organic farming increased species richness by about 30%. This result has been robust over the past 30 years of published studies. Organic farming had a greater effect on biodiversity as the percentage of arable fields of the landscape increased, that is, it is higher in intensively farmed regions [19]. Thus, it may be concluded that organic farming produces more biodiversity. Research gaps still exist for the understanding of functional biodiversity and ecosystem impact, which comprise soil biota, landscape (ecosystem and habitat) and genetic biodiversity on agricultural land in natural habitats [17]. The majority of current studies are from Northern and Western Europe and North American agriculture practices, while other regions with large areas of organic farming have been poorly investigated. Comparison between paired organic and conventional fields in India assessed a wide range of taxa (plants, soil microbes, earthworms, butterflies, dragonflies and other arthropods, reptiles, molluscs, amphibians/frogs and birds) trough different methods that showed similar trends. Habitat area, composition and management of organic fields were likely to favour higher levels of biodiversity by supporting higher numbers of species, dominance and abundance across most taxa. Organic fields are systems that are less dependent on external inputs to restore and rejuvenate the environment, resulting in higher biodiversity that promotes higher sustainable

**Number of comparisons1**

1884 Biodiversity in Ecosystems - Linking Structure and Function

**Author**

The biodiversity of weed communities in agro-ecosystems provides several valuable ecological functions [23]. Conventional and integrated production systems tend to be similar in both intensity of management and within-field biodiversity, but organic production tends to support greater density, species number and biological diversity in comparison with other investigated production systems [24]. At the field level, species richness was the greatest on organic farms where there was a greater abundance of weeds [24-27, 31; organic production system had the highest biodiversity of weed species [28-31]. Organic agricultural practices yielded more weed species in root crops, red clover/grass mixtures and in winter triticale. Weed species richness was reduced in red clover/grass stands, while root crops and spring barley undersown with red clover and grasses decreased weed species diversity, which is also important for achieving higher yields in an organic production system. The species composi‐ tion and in particular the quantitative structure of weeds were affected more by crop species and cultivation regime, compared to different agriculture practices (organic vs. integrated). Weed communities of crops grown using organic and integrated farming systems were more similar in terms of species composition than quantitative structure [30].

The maintenance of a diverse weed community is one step towards the sustainability of an agro-ecosystem through improved nutrient cycling and pest control, improved soil chemical and physical properties, and the reduction of soil erosion. An important aspect in the evalua‐ tion of the environmental impact of production systems is the biodiversity index for weed species (Table 2). Using the Shannon-Weaver diversity index for weeds of different production systems (conventional, integrated, organic) growing white cabbage and red beet showed that the biodiversity index was significantly higher in organic systems (0.86 in organic vs. 0.66 in conventional systems for cabbage and 0.81 in organic vs. 0.59 in conventional for red beet). Using ecological footprint calculation for the evaluation of different production systems showed that organic farming had the lowest impact on the environment. In the case of white cabbage and red beet production, ratio in ecological footprint between organic and conven‐ tional production was 1 to 3.5 [10].

The emerged weed flora is more affected by recent agrochemical inputs than the seedbank, which is buffered by the persistence of weed seeds in the soil. The seedbank is more strongly influenced by soil characteristics, such as the percentage organic carbon and percentage total nitrogen than by management [26]. The same weed species were in the seedbank and at field counted as germinated weeds, totalling 29 weed species in the survey (Table 2). The accumu‐ lated number of observed species pooled over fields was highest in the organic production of white cabbage and red beet, with 29 and 28 species, respectively. Within the conventional crop rotations, 18 species were observed in the cabbage field and 17 in the red beets field, while 20 and 19 were observed in the integrated crop rotation for cabbage and red beets. The differences in the number of weed species between conventional and integrated fields for cabbage were not significantly different; however, the difference when comparing organic and conventional fields was significantly different for both vegetables. For red beet, differences among all production systems were significant, which is contrary to the findings of [30], where weed communities of crops grown under organic and integrated farming systems were similar with regard to species composition but not quantitative structure. Different farming practices (described as organic, integrated and conventional) appeared to exert selection pressure on the species composition of the seedbank, building up different communities under the three farming systems over time [26]. These effects were scale dependent. At a within-field scale, species richness was greatest in organic farms, where there was a greater abundance of weeds; this was similar to our results and those of many others [24-31]. These results suggest that weed species diversity can be promoted by using organic cropping practices [31].


a-d Mean values followed by different letters within a column are significantly different (Duncan, α=0.05)

**Table 2.** Shannon-Weaver diversity index (H') and the frequency of occurrence (O) of weed species from the 30 species present in white cabbage and red beet in different production systems [10],3 and the influence on earthworm population [9, 36].

### **3.3. Earthworms population**

species (Table 2). Using the Shannon-Weaver diversity index for weeds of different production systems (conventional, integrated, organic) growing white cabbage and red beet showed that the biodiversity index was significantly higher in organic systems (0.86 in organic vs. 0.66 in conventional systems for cabbage and 0.81 in organic vs. 0.59 in conventional for red beet). Using ecological footprint calculation for the evaluation of different production systems showed that organic farming had the lowest impact on the environment. In the case of white cabbage and red beet production, ratio in ecological footprint between organic and conven‐

The emerged weed flora is more affected by recent agrochemical inputs than the seedbank, which is buffered by the persistence of weed seeds in the soil. The seedbank is more strongly influenced by soil characteristics, such as the percentage organic carbon and percentage total nitrogen than by management [26]. The same weed species were in the seedbank and at field counted as germinated weeds, totalling 29 weed species in the survey (Table 2). The accumu‐ lated number of observed species pooled over fields was highest in the organic production of white cabbage and red beet, with 29 and 28 species, respectively. Within the conventional crop rotations, 18 species were observed in the cabbage field and 17 in the red beets field, while 20 and 19 were observed in the integrated crop rotation for cabbage and red beets. The differences in the number of weed species between conventional and integrated fields for cabbage were not significantly different; however, the difference when comparing organic and conventional fields was significantly different for both vegetables. For red beet, differences among all production systems were significant, which is contrary to the findings of [30], where weed communities of crops grown under organic and integrated farming systems were similar with regard to species composition but not quantitative structure. Different farming practices (described as organic, integrated and conventional) appeared to exert selection pressure on the species composition of the seedbank, building up different communities under the three farming systems over time [26]. These effects were scale dependent. At a within-field scale, species richness was greatest in organic farms, where there was a greater abundance of weeds; this was similar to our results and those of many others [24-31]. These results suggest that

weed species diversity can be promoted by using organic cropping practices [31].

**Production system H' O H' O**

14c 18b 29a 20b -

a-d Mean values followed by different letters within a column are significantly different (Duncan, α=0.05)

**Table 2.** Shannon-Weaver diversity index (H') and the frequency of occurrence (O) of weed species from the 30 species present in white cabbage and red beet in different production systems [10],3 and the influence on earthworm

0.38d 0.66c 0.86a 0.74b -

**Weeds in white cabbage Weeds in red beet**

0.32d 0.59c 0.81a 0.64b -

13c 17b 28a 19b -

**Earthworm population (no./ 0.25m2 )**

> 11.58b 11.25b 22.41a 13.00b 24.00a

tional production was 1 to 3.5 [10].

1906 Biodiversity in Ecosystems - Linking Structure and Function

Control Conventional Organic Integrated Biodynamic

population [9, 36].

Organic farming systems are generally associated with increased biological activity and increased below-ground biodiversity. The main impacts on biological fertility do not result from the systems per se, but are related to the amount and quality of the soil organic matter that is used in the farming system, as well as the disruptions of soil habitat using different tillage tools. Even within the constraints of organic farming practices, it is possible for farmers to make changes to management practices using less tillage, which will tend to improved soil biological quality [32]. An important part of soil biodiversity is arbuscular mycorrhizal fungi, which can provide several benefits to plants and ecosystems. Organic farming enhances arbuscular mycorrhizal fungi, communities of which are similar in organically managed fields and in semi-natural species-rich grasslands; however, significantly less communities are found in conventionally managed fields. Their richness increased significantly over time since conversion to organic agriculture [33]. Soil microorganisms and other parts of soil biota including earthworms are also important drivers of soil fertility. Organic farming is based on the principle of the maintenance and enhancement of soil life and natural soil fertility, soil stability and soil biodiversity for preventing and combating soil compaction and soil erosion, and for the nourishing of plants primarily through the soil ecosystem [8]. Furthermore, our research results investigating the number and mass of earthworms as an indicator of soil biodiversity confirmed the effects of different production systems (conventional, integrated, organic, biodynamic) on the population of earthworms following the harvesting of different crops [9].

The studied production systems significantly influenced total earthworm population (Table 2) and small earthworms [36]. Both were shown to be higher in number in the biodynamic and organic production systems compared to the control, conventional and integrated production systems. When compared to control plots, as well as those managed without fertilizers and plant protection agents, there were roughly 2.7 and 2.5 times more small earthworms in biodynamic and organic production systems, respectively. In the same manner, the total earthworm population in the biodynamic production system was 207% and in the organic production system, 193% of this was counted for the control treatments. Similarly, the beneficial effect of organic farming on earthworms has been emphasized by other investiga‐ tions [34, 35]. The abundance of earthworms, as well as their total body mass, was affected by plant species occurring in crop rotation. Oil pumpkins were revealed to have a beneficial effect on earthworms. There was also a significant production system and plant species interaction concerning the population of small earthworms [36]. In addition to a production system, tillage is also a major driver for altering communities of earthworms and microorganisms in arable soils. The use of reduced tillage provides an approach for eco-intensification by enhancing inherent soil biota functions in organic arable farming [37].

### **3.4. Some other ecosystem services connected to biodiversity**

Biodiversity, as one of the most important ecosystem services of organic farming, is firmly connected to biocontrol and pollination services [2]. While the field of organic crop production has increased globally, the potential interactions between pest management in organic and

conventionally managed systems have to date received little attention [38]. Organic agriculture improves biodiversity at the field level, but potential interactions with the surrounding landscape and the potential effects on ecosystem services are less well known. Predation of aphids was the highest in organic fields in mixed landscapes and lower in more uniform surroundings. The results of comparing 153 farms from five countries showed that organic agriculture improved the biodiversity of plants and birds in all landscapes, but only in more diverse surroundings did it improve the potential for biological control. Contradictory results showed the necessity for taking into consideration production methods (organic vs. conven‐ tional) and regional landscape complexity for developing agri-environmental schemes for the future [39]. Organic farming is one of the most successful agri environmental schemes, as humans benefit from high quality food and farmers from higher prices for their products; additionally, this approach often successfully protects biodiversity. Based on the assessment of 30 triticale fields (15 organic vs. 15 conventional) and the comparison of five conventional fields that were treated with insecticides and 10 non-treated conventional fields, it was found out that organic fields had five times higher plant species richness and about 20 times higher pollinator species richness compared to conventional fields. In contrast, the abundance of cereal aphids was five times lower in organic fields, while predator abundances were three times higher and predator-prey ratios 20 times higher in organic fields, indicating a signifi‐ cantly higher potential for biological pest control in organic fields [40]. Aphid density was also significantly lower in organic wheat fields compared to conventional fields, based on the assessment of 216 wheat fields during a two-year study [41]. Another positive impact of crop genetic diversity where wheat is concerned was found on below (collembola) and above‐ ground arthropod (spiders and predatory carbides) diversity at the field scale, which may be the result of a wider variety of food resources or more complex crop architecture. Increasing crop genetic diversity can therefore be an easy-to-implement scheme for benefiting farmland biodiversity [42].

Despite decades of European policy to ban harmful pesticides, the negative effects of pesticides on wild plant and animal species are nonetheless present and can be observed through losses pertaining to biodiversity. Chemical pesticides minimize opportunities for biological pest control. If there is an aim for biodiversity to be restored in Europe, opportunities should be created for crop production utilizing biodiversity-based ecosystem services such as biological pest control; what is needed is a Europe-wide shift towards farming employing the minimal use of pesticides over large areas, not only on organic farming areas [43]. Insecticide treatment in conventional fields had only a short-term effect on aphid densities, while later in the season, aphid abundances were even higher and predator abundances lower in treated compared to untreated conventional fields. Preventative insecticide application in conventional fields has only short-term effects on aphid densities but long-term negative effects on biological pest control. Therefore, conventional farmers should restrict insecticide applications to situations where thresholds for pest densities have been reached. Organic farming increases biodiversity, including important functional groups like plants, pollinators and predators, which in turn enhance natural pest control [40].

Biodiversity supplies multiple ecosystem services to agriculture. In addition to the potential for biological pest control, pollination problems are a topic now also being addressed in EU agriculture policy [43]. Declines in insect-pollinated plants and their pollinators have been reported as a result of agricultural intensification [44]. Reducing farming intensity with conventionally managed leys does not seem to be as effective as organic farming for delivering crop pollination services [45]. The abundance of pollinators was more than 100 times higher on organic fields. Plant and pollinator species richness, as well as predator abundances and predator-prey ratios, were higher at field edges compared to field centres, highlighting the importance of field edges for ecosystem services [40].

conventionally managed systems have to date received little attention [38]. Organic agriculture improves biodiversity at the field level, but potential interactions with the surrounding landscape and the potential effects on ecosystem services are less well known. Predation of aphids was the highest in organic fields in mixed landscapes and lower in more uniform surroundings. The results of comparing 153 farms from five countries showed that organic agriculture improved the biodiversity of plants and birds in all landscapes, but only in more diverse surroundings did it improve the potential for biological control. Contradictory results showed the necessity for taking into consideration production methods (organic vs. conven‐ tional) and regional landscape complexity for developing agri-environmental schemes for the future [39]. Organic farming is one of the most successful agri environmental schemes, as humans benefit from high quality food and farmers from higher prices for their products; additionally, this approach often successfully protects biodiversity. Based on the assessment of 30 triticale fields (15 organic vs. 15 conventional) and the comparison of five conventional fields that were treated with insecticides and 10 non-treated conventional fields, it was found out that organic fields had five times higher plant species richness and about 20 times higher pollinator species richness compared to conventional fields. In contrast, the abundance of cereal aphids was five times lower in organic fields, while predator abundances were three times higher and predator-prey ratios 20 times higher in organic fields, indicating a signifi‐ cantly higher potential for biological pest control in organic fields [40]. Aphid density was also significantly lower in organic wheat fields compared to conventional fields, based on the assessment of 216 wheat fields during a two-year study [41]. Another positive impact of crop genetic diversity where wheat is concerned was found on below (collembola) and above‐ ground arthropod (spiders and predatory carbides) diversity at the field scale, which may be the result of a wider variety of food resources or more complex crop architecture. Increasing crop genetic diversity can therefore be an easy-to-implement scheme for benefiting farmland

1928 Biodiversity in Ecosystems - Linking Structure and Function

Despite decades of European policy to ban harmful pesticides, the negative effects of pesticides on wild plant and animal species are nonetheless present and can be observed through losses pertaining to biodiversity. Chemical pesticides minimize opportunities for biological pest control. If there is an aim for biodiversity to be restored in Europe, opportunities should be created for crop production utilizing biodiversity-based ecosystem services such as biological pest control; what is needed is a Europe-wide shift towards farming employing the minimal use of pesticides over large areas, not only on organic farming areas [43]. Insecticide treatment in conventional fields had only a short-term effect on aphid densities, while later in the season, aphid abundances were even higher and predator abundances lower in treated compared to untreated conventional fields. Preventative insecticide application in conventional fields has only short-term effects on aphid densities but long-term negative effects on biological pest control. Therefore, conventional farmers should restrict insecticide applications to situations where thresholds for pest densities have been reached. Organic farming increases biodiversity, including important functional groups like plants, pollinators and predators, which in turn

Biodiversity supplies multiple ecosystem services to agriculture. In addition to the potential for biological pest control, pollination problems are a topic now also being addressed in EU

biodiversity [42].

enhance natural pest control [40].

Pollination systems within intensive grassland communities may be different from those in arable systems. Results from comparing plant community composition among 10 pairs of organic and conventional dairy farms indicate that organic management increases plant richness in field centres, but that landscape complexity exerts a strong influence on both organic and conventional field edges. Insect-pollinated forb richness showed positive rela‐ tionships to landscape complexity, reflecting what has been documented for bees and other pollinators [44].

Hedges provide important nesting, feeding and sheltering sites for birds in agricultural areas, while organic farming also enhances the environments of farmland birds [15, 18, 46]. However, little is known about how the interaction of (the amount of) hedges and variables pertaining to the organic management of the landscape scale affects birds. Birds were surveyed in the fields and in the adjoining hedges on conventional and organic winter wheat fields and meadows. More bird species occurred in organic than in conventional fields, regardless of land-use type. Hedge length had a much stronger effect on bird richness than organic farming practice. The interaction of landscape complexity and hedge length was found to be connected. Hedge length enhanced bird richness only in the case of simple landscapes. In more complex landscapes, the local effect of hedge length levelled off, because bird richness was high even without local hedges. Adding hedges or introducing organic farming practices should be primarily promoted in simple landscapes, where it particularly makes a difference for biodiversity [46].

The effect of organic farming differs depending on the scale of uptake of a particular landscape. The local effect of organic farming was found to be consistently strong, with higher diversity in borders adjoining organic fields, most likely due to the lack of herbicides used on organically managed farmland. In addition to the proportion of semi-natural habitat, which is important for farmland biodiversity, the management practice of cropland can also influence diversity in semi-natural habitats. Forb richness, which was evaluated as an agri-environmental indicator for biodiversity was also higher within borders situated in landscapes with a high proportion of organic land, irrespective of local management; this was possibly as a result of the dispersal of primarily annual plant species from the organically managed fields into the borders (mass effect). Farming practice at a local and a landscape scale can independently influence plant species richness, indicating that organic farming can also influence diversity at larger spatial scales, as well as outside organically managed land [47]. Organic farming enhances species richness and the abundance of many common taxa, but its effects are often species specific, as well as trait or context dependant. Landscape enhances or reduces the positive effects of organic farming, or acts through interactions where the surrounding landscape affects biodiversity differently on organic and conventional farms [48].

Around the world, small farms are those that practice high-diversity agriculture. Small farmers often choose to cultivate several varieties of the same crop; additionally and perhaps more importantly, different farmers in a given locality often cultivate different varieties. On the other hand large farms usually sow a single variety over a wide area [49]. Small farms may in this way have an indirect, positive effect on biodiversity, since these farms normally have smaller land parcels and thus more field edges, which are relatively species-rich. Although the average organic farm is bigger in the EU than its conventional counterparts [50] and in some cases is "conventionalized", organic farming is nonetheless generally viewed as small farms. The world's majority of food is produced by smallholder farmers who grow over 70% of all our food. Organic farming on small farms leads to an increase in food production and to greater benefits for the ecosystem by improving soil organic matter, reducing erosion and increasing biodiversity. At the same time, organic farming also allows farmers to receive higher prices for their value-added produce and provide them with opportunities to export to markets niche [51]. The report of a study focusing on farming systems in Africa showed that it is possible to set broad priorities for agricultural intensification based on the organic principles of health, ecology, fairness and caring for the earth. Ecological principles and technologies can be used to support farmers in obtaining food security and improving their livelihoods without destroying the local indigenous biodiversity [52].

### **3.5. Agri-environmental payments and farmers' attitudes towards biodiversity**

Agricultural intensification has caused significant declines in biodiversity, while the profound intensification of European agricultural practices in the past number of decades continues. This is due to decreasing crop diversity, simplification of cropping methods, the use of fertilizers and pesticides and the homogenization of landscapes, all of which have negative effects on biodiversity in agricultural areas. Agricultural management practices can have a substantial positive impact on the conservation of the EUs wild flora and fauna. Agrienvironmental schemes including organic farming are thought to benefit biodiversity. Agrienvironmental payments are part of Common agriculture policy, which promotes the multifunctional role of farming as a provider of food products and a steward of diverse landscapes, as well as the cultural and natural heritage of rural areas. Furthermore, in the future, according to the EU regulation 1305/2013, each member state has to introduce agrienvironmental measures for enhancing biodiversity and the preservation of high nature value farming and forestry systems [53]. Ecosystem services payments must be based on a standar‐ dized and transparent assessment of the goods and services provided. This is especially relevant in the context of EU agri-environmental programmes, but also for organic-food companies that foster environmental services on their contractor farms [54].

Agri-environmental schemes have been introduced to minimize the effects of agricultural intensification and enhance farmland biodiversity, but evaluations have produced inconsis‐ tent results [47]. Biodiversity is in different countries supported by different measures (i.e., strips and hedges, crop rotation, autochthone varieties, Nature 2000 measures), as is organic farming, which enhances the species richness and abundance of above and below soil taxa [15-20]. Traditional farming contributes to the safeguarding of certain natural or semi-natural habitats. Many valuable habitats and the presence of species have a direct interdependence with agriculture (e.g., many bird species nest and feed on farmland). Two major changes have contributed to upsetting the delicate balance between agriculture and biodiversity: (i) special‐ ization and intensification of certain production methods (such as the use of more chemicals and heavy machinery); (ii) marginalization or abandonment of traditional land management, a key factor in preserving certain habitats and site-specific biodiversity. In some EU member states, land abandonment and the withdrawal of traditional management may become a threat to biodiversity on farmland. Therefore, preventing these processes is a key action for halting the loss of biodiversity. The Common agricultural policy addresses the preservation of habitats and biodiversity by specific rural development measures targeted at the preservation of habitats and biodiversity (agri-environmental and Nature 2000 payments), as well as require‐ ments included in the scope of cross compliance for birds and habitats [55].

Around the world, small farms are those that practice high-diversity agriculture. Small farmers often choose to cultivate several varieties of the same crop; additionally and perhaps more importantly, different farmers in a given locality often cultivate different varieties. On the other hand large farms usually sow a single variety over a wide area [49]. Small farms may in this way have an indirect, positive effect on biodiversity, since these farms normally have smaller land parcels and thus more field edges, which are relatively species-rich. Although the average organic farm is bigger in the EU than its conventional counterparts [50] and in some cases is "conventionalized", organic farming is nonetheless generally viewed as small farms. The world's majority of food is produced by smallholder farmers who grow over 70% of all our food. Organic farming on small farms leads to an increase in food production and to greater benefits for the ecosystem by improving soil organic matter, reducing erosion and increasing biodiversity. At the same time, organic farming also allows farmers to receive higher prices for their value-added produce and provide them with opportunities to export to markets niche [51]. The report of a study focusing on farming systems in Africa showed that it is possible to set broad priorities for agricultural intensification based on the organic principles of health, ecology, fairness and caring for the earth. Ecological principles and technologies can be used to support farmers in obtaining food security and improving their livelihoods without

destroying the local indigenous biodiversity [52].

194 10 Biodiversity in Ecosystems - Linking Structure and Function

**3.5. Agri-environmental payments and farmers' attitudes towards biodiversity**

companies that foster environmental services on their contractor farms [54].

Agri-environmental schemes have been introduced to minimize the effects of agricultural intensification and enhance farmland biodiversity, but evaluations have produced inconsis‐ tent results [47]. Biodiversity is in different countries supported by different measures (i.e., strips and hedges, crop rotation, autochthone varieties, Nature 2000 measures), as is organic farming, which enhances the species richness and abundance of above and below soil taxa [15-20]. Traditional farming contributes to the safeguarding of certain natural or semi-natural

Agricultural intensification has caused significant declines in biodiversity, while the profound intensification of European agricultural practices in the past number of decades continues. This is due to decreasing crop diversity, simplification of cropping methods, the use of fertilizers and pesticides and the homogenization of landscapes, all of which have negative effects on biodiversity in agricultural areas. Agricultural management practices can have a substantial positive impact on the conservation of the EUs wild flora and fauna. Agrienvironmental schemes including organic farming are thought to benefit biodiversity. Agrienvironmental payments are part of Common agriculture policy, which promotes the multifunctional role of farming as a provider of food products and a steward of diverse landscapes, as well as the cultural and natural heritage of rural areas. Furthermore, in the future, according to the EU regulation 1305/2013, each member state has to introduce agrienvironmental measures for enhancing biodiversity and the preservation of high nature value farming and forestry systems [53]. Ecosystem services payments must be based on a standar‐ dized and transparent assessment of the goods and services provided. This is especially relevant in the context of EU agri-environmental programmes, but also for organic-food Agri-environmental payments to farmers for the conversion from conventional to organic farming or remaining inorganic should encourage them to participate in schemes, thereby responding to the increasing demand by society for the use of environmentally-friendly farm practices and also for high standards of animal welfare, as is the case in organic farming. In order to increase synergy in biodiversity and the benefits delivered by the organic farming, other measures should also be promoted and supported among organic farmers in order to cover larger areas or other protected areas, e.g., Nature 2000 [53].

In agricultural landscapes, farmers have a large impact on biodiversity through the manage‐ ment decisions and agricultural practices that are used on their farms. Farmers' perceptions of biodiversity and its different values influence their willingness to apply biodiversity-friendly farming practices. Organic and conventional farmers' perceptions of the different values of biodiversity were analysed across three European countries. Farmers' perceptions of biodi‐ versity were strongly connected to their everyday lives and linked to farming practices. In addition to recognizing the importance of variety, species and habitat diversity, farmers also acknowledged wider landscape processes and attached value to the complexity of ecological systems. It was found that organic farmers tended to have a more complex and philosophical approach to biodiversity, with little differences being observed between these farmers; conventional farmers, on the other hand, exhibited more differences among themselves. Furthermore, ethical and social values were important for all farmers, but economic value was more important for conventional farmers, which has an impact on their behaviour [56].

Based on a survey among organic and conventional farmers, it was concluded that they had similar attitudes to farming results and to the environment; however, organic farmers were better informed about environmental issues and carried out more environmentally-friendly practices and behaviours. More biodiversity was found on environmentally-friendly orien‐ tated farms and less on high production-orientated farms. Organic farmers with more positive attitudes to the environment and who were better informed about environmental topics had higher biodiversity on their farms compared to others. Although there were disparities between attitudes and actual behaviours in relation to the environment among organic farmers sharing similar attitudes to conventional farmers, they were more prepared to inform them‐ selves about and carry out environmentally-friendly farming. Results of the comparison study showed that biodiversity benefitted more from organic farming and environmentally- oriented farmers, and that there is an important link between farmers' environmental attitudes and knowledge and the beneficial effects of organic farming on biodiversity [57].

Farmers strongly acknowledged ethical and social biodiversity values. This suggests that soft policy tools can also foster biodiverse-sensitive farming methods that are complementary to mainstream monetary incentives [55]. As farmers receive a majority of agri-environmental payments, they can be more involved in data generation and conservation management. Farm size is very important in terms of the amount of payments that are provided per hectare and for improving biodiversity on a bigger scale. A standardized model for measuring on-farm biodiversity does not yet exist in practice. Performance indicators should be focused on and farmers should be included in generating this information. A framework is needed for assessment of the results and for management measures that can be employed on farms. Another requirement is ease of application, which encompasses the simplicity of gathering input data and its clarity to those farmers who will apply it [54]. Conservation-oriented thinking and better environmental education among farmers should be encouraged for those who already participate in an agri-environmental scheme and even more so amongst new‐ comers. In this way, the benefits of the agri-environmental schemes for the environment can be maximized [57].

An open source farm assessment system was prepared for the assessment of biodiversity including biotopes, species, biotope connectivity and the influence of land use. Interviews with the test farmers showed that the assessment methods can be implemented on farms and that they were understood by farmers [54].

## **4. Conclusions**

The analysed data showed that in the past decades, the specialization and intensification of agriculture production methods have had negative effects on biodiversity. The future holds the challenge of designing more sustainable farming systems that are productive and maintain or enhance the provision of ecosystem services, including biodiversity. The significantly positive effect of organic farming on biodiversity compared to conventional farming was noticed in 80% of cases; in 16%, differences were unclear and less biodiversity was found in 4% of comparisons [15, 17, 18], where seven to 10 biodiversity indicators were taken into account. Small farms in particular may have an indirect positive effect on biodiversity. These farms generally have smaller land parcels and thus more field edges, which are relatively species-rich.

We can conclude that the benefits of organic farming on biodiversity are as follows:

**i.** Organic farming increased species richness by about 30% and had a greater effect on biodiversity, as the percentage of the landscape consisting of arable fields increased. It was found that organic fields had up to five times higher plant species richness compared to conventional fields. For example, plant and butterfly species richness was up to 20% higher on organic farms and butterfly abundance was about 60% higher. After the conversion from conventional to organic farming abundance of butterflies was increased for 100%. Organic farming enhanced arbuscular mycorrhi‐ zal fungi and its communities. This was similar in organically managed fields and in semi-natural species rich grasslands, but significantly fewer communities were found in conventionally managed fields. Their richness increased significantly over time from the point of a conversion to organic agriculture.

showed that biodiversity benefitted more from organic farming and environmentally- oriented farmers, and that there is an important link between farmers' environmental attitudes and

Farmers strongly acknowledged ethical and social biodiversity values. This suggests that soft policy tools can also foster biodiverse-sensitive farming methods that are complementary to mainstream monetary incentives [55]. As farmers receive a majority of agri-environmental payments, they can be more involved in data generation and conservation management. Farm size is very important in terms of the amount of payments that are provided per hectare and for improving biodiversity on a bigger scale. A standardized model for measuring on-farm biodiversity does not yet exist in practice. Performance indicators should be focused on and farmers should be included in generating this information. A framework is needed for assessment of the results and for management measures that can be employed on farms. Another requirement is ease of application, which encompasses the simplicity of gathering input data and its clarity to those farmers who will apply it [54]. Conservation-oriented thinking and better environmental education among farmers should be encouraged for those who already participate in an agri-environmental scheme and even more so amongst new‐ comers. In this way, the benefits of the agri-environmental schemes for the environment can

An open source farm assessment system was prepared for the assessment of biodiversity including biotopes, species, biotope connectivity and the influence of land use. Interviews with the test farmers showed that the assessment methods can be implemented on farms and that

The analysed data showed that in the past decades, the specialization and intensification of agriculture production methods have had negative effects on biodiversity. The future holds the challenge of designing more sustainable farming systems that are productive and maintain or enhance the provision of ecosystem services, including biodiversity. The significantly positive effect of organic farming on biodiversity compared to conventional farming was noticed in 80% of cases; in 16%, differences were unclear and less biodiversity was found in 4% of comparisons [15, 17, 18], where seven to 10 biodiversity indicators were taken into account. Small farms in particular may have an indirect positive effect on biodiversity. These farms generally have smaller land parcels and thus more field edges, which are relatively

We can conclude that the benefits of organic farming on biodiversity are as follows:

**i.** Organic farming increased species richness by about 30% and had a greater effect on

biodiversity, as the percentage of the landscape consisting of arable fields increased. It was found that organic fields had up to five times higher plant species richness compared to conventional fields. For example, plant and butterfly species richness

knowledge and the beneficial effects of organic farming on biodiversity [57].

be maximized [57].

**4. Conclusions**

species-rich.

they were understood by farmers [54].

196 12 Biodiversity in Ecosystems - Linking Structure and Function

**ii.** The occurrence of weed species was significantly higher in the organic production of white cabbage and red beet compared to integrated and conventional production. The biodiversity index was significantly higher in organic production compared to the conventional method, 0.86 vs. 0.66 for cabbages and 0.81 vs. 0.59 for red beets. Conventional and integrated production systems tended to be similar both in terms of the intensity of management and regarding within-field biodiversity; however, organic production tended to support greater density, species number and biological diversity compared to other investigated production systems.

Earthworms were more abundant on organically managed fields. In organic and biodynamic farming plots, the number of earthworms was on average two times higher compared to integrated, conventional and control plots.

**iii.** Biodiversity as one of the most important ecosystem services of organic farming is firmly connected to biocontrol and pollination services, which are enhanced when using no or less chemicals. The abundance of cereal aphids was five times lower in organic fields, while predator abundances were 20 times higher in organic fields, indicating a significantly higher potential for biological pest control in organic fields. Organic fields had 20 times higher pollinator species richness compared to conven‐ tional fields. Pollinators and predator abundance was higher at field edges compared to field centres, highlighting the importance of field edges for ecosystem services. Edges provide important nesting, feeding and sheltering sites for birds in agricultural areas. Thus, organic farming enhances farmland birds.

Overall, organic agriculture appears to perform better than conventional farming and provides important environmental advantages such as halting the use of harmful chemicals and their spread in the environment and along the trophic chain, reducing water use, as well as reducing carbon and ecological footprints. As we have underscored, organic farming fulfils the promise to protect biodiversity better than conventional farming. However, in the European commis‐ sion document, The EU Biodiversity Strategy to 2020 [1], organic farming is not even men‐ tioned, while in the European Parliament resolution regarding the strategy [6], organic farming is mentioned only once in the context of a call for a strengthening of Pillar II and for drastic improvements to the environmental focus of that pillar, and to the effectiveness of its agrienvironmental measures. Supporting farmers to convert their properties to organic land and to maintain organic farming within the scope of agri-environmental schemes as a part of Common agriculture policy can have a significant impact on biodiversity as a result of management decisions farmers apply to their agricultural land.

## **Acknowledgements**

The results presented in this paper are in part an output of the national research project J4-9532: "The quality of food dependent on the agricultural production method", funded by the Ministry of Higher Education, Science and Technology of the Republic of Slovenia and Slovenian Research Agency.

## **Author details**

Martina Bavec\* and Franc Bavec

\*Address all correspondence to: martina.bavec@um.si

University of Maribor, Faculty of Agriculture and Life Sciences, Hoče/Maribor, Slovenia

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Martina Bavec\*

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**Provisional chapter Chapter 9**
