Conservation Agriculture and its contribution to the achievement of agri-environmental and economic challenges in Europe

Conservation Agriculture is an ecosystem approach to farming capable of providing solutions for numerous of the agri-environmental concerns in Europe. Certainly, most of the challenges addressed in the Common Agriculture Policy (CAP) could be tackled through Conservation Agriculture (CA). Not only the agri-environmental ones, but also those concerning farmer and rural communities’ prosperity. The optimisation of inputs and similar yields than conventional tillage, make Conservation Agriculture a profitable system compared to the tillage based agriculture. Whereas this sustainable agricultural system was conceived for protecting agrarian soils from its degradation, the numerous collateral benefits that emanate from soil conservation, i.e., climate change mitigation and adaptation, have raised Conservation Agriculture as one of the global emerging agrosciences, being adopted by an increasing number of farmers worldwide, including Europe.

Use of Coarse-resolution models of species' distributions to guide local conservation inferences

Distribution models are used increasingly for species conservation assessments over extensive areas, but the spatial resolution of the modeled data and, consequently, of the predictions generated directly from these models are usually too coarse for local conservation applications. Comprehensive distribution data at finer spatial resolution, however, require a level of sampling that is impractical for most species and regions. Models can be downscaled to predict distribution at finer resolutions, but this increases uncertainty because the predictive ability of models is not necessarily consistent beyond their original scale. We analyzed the performance of downscaled, previously published models of environmental favorability (a generalized linear modeling technique) for a restricted endemic insectivore, the Iberian desman (Galemys pyrenaicus), and a more widespread carnivore, the Eurasian otter ( Lutra lutra), in the Iberian Peninsula. The models, built from presence–absence data at 10 × 10 km resolution, were extrapolated to a resolution 100 times finer (1 × 1 km). We compared downscaled predictions of environmental quality for the two species with published data on local observations and on important conservation sites proposed by experts. Predictions were significantly related to observed presence or absence of species and to expert selection of sampling sites and important conservation sites. Our results suggest the potential usefulness of downscaled projections of environmental quality as a proxy for expensive and time-consuming field studies when the field studies are not feasible. This method may be valid for other similar species if coarse-resolution distribution data are available to define high-quality areas at a scale that is practical for the application of concrete conservation measures

Transferability of environmental favourability models in geographic space: The case of the Iberian desman (Galemys pyrenaicus) in Portugal and Spain

Transferring distribution models between different geographical areas may be problematic, as the performance of models outside their original scope is hard to predict. A modelling procedure is needed that gets the gist of the environmental descriptors of a distribution area, without either overfitting to the training data or overestimating the species’ distribution potential.We tested the transferability power of the favourability function, a generalized linear model, on the distribution of the Iberian desman (Galemys pyrenaicus) in the Iberian territories of Portugal and Spain.We also tested the effects of two of the main potential constraints on model transferability: the analysed ranges of the predictor variables, and the completeness of the species distribution data. We modelled 10 km×10km presence/absence data from Portugal and Spain separately, extrapolated each model to the other country, and compared predictions with observations. The Spanish model, despite arguably containing more false absences, showed good predictive ability in Portugal. The Portuguese model, whose predictors ranged between only a subset of the values observed in Spain, overestimated desman distribution when transferred.We discuss possible reasons for this differential model behaviour, and highlight the importance of this kind of models for prediction and conservation applications

Geographical and environmental correlates of big and small game in Andalusia (southern Spain)

In Andalusia, southern Spain, each game estate applies its own rules and presents its results in annual hunting reports, which have been mandatory for Spanish game estates since 1989. We used the information about hunting yields, included in 32134 annual hunting reports produced during the period 1993/94 to 2001/02 by 6049 game estates, to determine the current distribution of hunting yields of big and small game species in Andalusia. Using generalised linear models and a geographic information system, we determined the most favourable municipalities to big and small game, respectively, and delimited potential areas to attain good hunting yields for big and small game at a 1-km2 resolution. Municipalities and areas favourable to big game are mainly located in the Sierra Morena and the westernmost fringe of the Betic Range, while those favourable to small game occupy the upper Guadalquivir River valley. There is a clear segregation between big and small game species according to the physiography and land uses of the territory. Big game species are typical of Mediterranean woodland areas, while the most emblematic small game species prefer agricultural areas. Our results provide a territorial ordination of hunting yields in southern Spain and have several potential applications in strategic planning for hunting activities and biodiversity conservation in Andalusia that can be extrapolated to other regions.

Obtaining environmental favourability functions from logistic regression

Logistic regression is a statistical tool widely used for predicting species’ potential distributions starting from presence/absence data and a set of independent variables. However, logistic regression equations compute probability values based not only on the values of the predictor variables but also on the relative proportion of presences and absences in the dataset, which does not adequately describe the environmental favourability for or against species presence. A few strategies have been used to circumvent this, but they usually imply an alteration of the original data or the discarding of potentially valuable information. We propose a way to obtain from logistic regression an environmental favourability function whose results are not affected by an uneven proportion of presences and absences. We tested the method on the distribution of virtual species in an imaginary territory. The favourability models yielded similar values regardless of the variation in the presence/absence ratio. We also illustrate with the example of the Pyrenean desman’s (Galemys pyrenaicus) distribution in Spain. The favourability model yielded more realistic potential distribution maps than the logistic regression model. Favourability values can be regarded as the degree of membership of the fuzzy set of sites whose environmental conditions are favourable to the species, which enables applying the rules of fuzzy logic to distribution modelling. They also allow for direct comparisons between models for species with different presence/absence ratios in the study area. This makes themmore useful to estimate the conservation value of areas, to design ecological corridors, or to select appropriate areas for species reintroductions.

Modelling the distribution of Bonelli's eagle in Spain: Implications for conservation planning

Bonelli’s eagle, Hieraaetus fasciatus , has recently suffered a severe population decline and is currently endangered. Spain supports about 70% of the European population. We used stepwise logistic regression on a set of environmental, spatial and human variables to model Bonelli’s eagle distribution in the 5167 UTM 10 × 10 km quadrats of peninsular Spain. We obtained a model based on 16 variables, which allowed us to identify favourable and unfavourable areas for this species in Spain, as well as intermediate favourability areas. We assessed the stepwise progression of the model by comparing the model’s predictions in each step with those of the final model, and selected a parsimonious explanatory model based on three variables — slope, July temperature and precipitation — comprising 76% of the predictive capacity of the

Otter (Lutra lutra) distribution modeling at two resolution scales suited to conservation planning in the Iberian Peninsula

We used the results of the Spanish Otter Survey of 1994–1996, a Geographic Information System and stepwise multiple logistic regression to model otter presence/absence data in the continental Spanish UTM 10 10-km squares. Geographic situation, indicators of human activity such as highways and major urban centers, and environmental variables related with productivity, water availability, altitude, and environmental energy were included in a logistic model that correctly classified about 73% of otter presences and absences. We extrapolated the model to the adjacent territory of Portugal, and increased the model’s spatial resolution by extrapolating it to 1 1-km squares in the whole Iberian Peninsula. The model turned out to be rather flexible, predicting, for instance, the species to be very restricted to the courses of rivers in some areas, and more widespread in others. This allowed us to determine areas where otter populations may be more vulnerable to habitat changes or harmful human interventions. # 2003 Elsevier Ltd. All rights reserved.

Spatial, environmental and human influences on the distribution of otter (Lutra lutra) in the Spanish provinces

In a previous survey of otters ( Lutra lutra L. 1758) in Spain, different causes were invoked to explain the frequency of the species in each province. To find common causes of the distribution of the otter in Spain, we recorded a number of spatial, environmental and human variables in each Spanish province. We then performed a stepwise linear multiple regression of the proportion of positive sites of otter in the Spanish provinces separately on each of the three groups of variables. Geographic longitude, January air humidity, soil permeability and highway density were the variables selected. A linear regression of the proportion of otter presence on these variables explained 62.4% of the variance. We then used the selected variables in a partial regression analysis to specify which proportions of the variation are explained exclusively by spatial, environmental and human factors, and which proportions are attributable to interactions between these components. Pure environmental effects accounted for only 5.5% of the variation, while pure spatial and pure human effects explained 18% and 9.7%, respectively. Shared variation among the components totalled 29.2%, of which 10.9% was explained by the interaction between environmental and spatial factors. Human factors explained globally less variance than spatial and environmental ones, but the pure human influence was higher than the pure environmental one. We concluded that most of the variation in the proportion of occurrences of otter in Spanish provinces is spatially structured, and that environmental factors have more influence on otter presence than human ones; however, the human influence on otter distribution is less structured in space, and thus can be more disruptive. This effect of large infrastructures on wild populations must be taken into account when planning large-scale conservation policies

Soil quality assessment in conservation agriculture systems

Soil is a key resource that provides the basis of food production and sustains and delivers several ecosystems services including regulating and supporting services such as water and climate regulation, soil formation and the cycling of nutrients carbon and water. During the last decades, population growth, dietary changes and the subsequent pressure on food production, have caused severe damages on soil quality as a consequence of intensive, high input-based agriculture. While agriculture is supposed to maintain and steward its most important resource base, it compromises soil quality and fertility through its impact on erosion, soil organic matter and biodiversity decline, compaction, etc., and thus the necessary yield increases for the next decades. New or improved cropping systems and agricultural practices are needed to ensure a sustainable use of this resource and to fully take the advantages of its associated ecosystem services. Also, new and better soil quality indicators are crucial for fast and in-field soil diagnosis to help farmers decide on the best management practices to adopt under specific pedo-climatic conditions. Conservation Agriculture and its fundamental principles: minimum (or no) soil disturbance, permanent organic soil cover and crop rotation /intercropping certainly figure among the possibilities capable to guarantee sustainable soil management. The iSQAPER project – Interactive Soil Quality Assessment in Europe and China for Agricultural Productivity and Environmental Resilience – is tackling this problem with the development of a Soil Quality application (SQAPP) that links soil and agricultural management practices to soil quality indicators and will provide an easy-to-use tool for farmers and land managers to judge their soil status. The University of Évora is the leader of WP6 - Evaluating and demonstrating measures to improve Soil Quality. In this work package, several promising soil and agricultural management practices will be tested at selected sites and evaluated using the set of soil quality indicators defined for the SQAPP tool. The project as a whole and WP6 in specific can contribute to proof and demonstrate under different pedoclimatic conditions the impact of Conservation Agriculture practices on soil quality and function as was named the call under which this project was submitted.

Using modeling tools for implementing feasible land use and nature conservation governance systems in small islands e The Pico Island (Azores) case-study

The present study deals with the development of systematic conservation planning as management instrument in small oceanic islands, ensuring open systems of governance, and able to integrate an informed and involved participation of the stakeholders. Marxan software was used to define management areas according a set of alternative land use scenarios considering different conservation and management paradigms. Modeled conservation zones were interpreted and compared with the existing protected areas allowing more fused information for future trade-outs and stakeholder's involvement. The results, allowing the identification of Target Management Units (TMU) based on the consideration of different development scenarios proved to be consistent with a feasible development of evaluation approaches able to support sound governance systems. Moreover, the detailed geographic identification of TMU seems to be able to support participated policies towards a more sustainable management of the entire island

An R package for simulating metapopulation dynamics and range expansion under environmental change

The metapopulation paradigm is central in ecology and conservation biology to understand the dynamics of spatially-structured populations in fragmented landscapes. Metapopulations are often studied using simulation modelling, and there is an increasing demand of user-friendly software tools to simulate metapopulation responses to environmental change. Here we describe the MetaLandSim R package, mwhich integrates ideas from metapopulation and graph theories to simulate the dynamics of real and virtual metapopulations. The package offers tools to (i) estimate metapopulation parameters from empirical data, (ii) to predict variation in patch occupancy over time in static and dynamic landscapes, either real or virtual, and (iii) to quantify the patterns and speed of metapopulation expansion into empty landscapes. MetaLandSim thus provides detailed information on metapopulation processes, which can be easily combined with land use and climate change scenarios to predict metapopulation dynamics and range expansion for a variety of taxa and ecological systems.