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The ability of ecological niche models (ENMs) to produce robust predictions for different time frames (i.e. temporal transferability) may be hindered by a lack of ecologically relevant predictors. Model performance may also be affected by species traits, which may reflect different responses to processes controlling species distribution. In this study, we tested four primary hypotheses involving the role of species traits and environmental predictors in ENM performance and transferability. We compared the predictive accuracy of ENMs based upon (1) climate, (2) land-use/cover (LULC) and (3) ecosystem functional attributes (EFAs), and (4) the combination of these factors for 27 bird species within and beyond the time frame of model calibration. The combination of these factors significantly increased both model performance and transferability, highlighting the need to integrate climate, LULC and EFAs to improve biodiversity projections. However, the overall model transferability was low (being only acceptable for less than 25% of species), even under a hierarchical modelling approach, which calls for great caution in the use of ENMs to predict bird distributions under global change scenarios. Our findings also indicate that positive effects of species traits on predictive accuracy within model calibration are not necessarily translated into higher temporal transferability.

Global environmental changes are rapidly affecting species' distributions and habitat suitability worldwide, requiring a continuous update of biodiversity status to support effective decisions on conservation policy and management. In this regard, satellite-derived Ecosystem Functional Attributes (EFAs) offer a more integrative and quicker evaluation of ecosystem responses to environmental drivers and changes than climate and structural or compositional landscape attributes. Thus, EFAs may hold advantages as predictors in Species Distribution Models (SDMs) and for implementing multi-scale species monitoring programs. Here we describe a modelling framework to assess the predictive ability of EFAs as Essential Biodiversity Variables (EBVs) against traditional datasets (climate, land-cover) at several scales. We test the framework with a multi-scale assessment of habitat suitability for two plant species of conservation concern, both protected under the EU Habitats Directive, differing in terms of life history, range and distribution pattern (Iris boissieri and Taxus baccata). We fitted four sets of SDMs for the two test species, calibrated with: interpolated climate variables; landscape variables; EFAs; and a combination of climate and landscape variables. EFA-based models performed very well at the several scales (AUCmedian from 0.881±0.072 to 0.983±0.125), and similarly to traditional climate-based models, individually or in combination with land-cover predictors (AUCmedian from 0.882±0.059 to 0.995±0.083). Moreover, EFA-based models identified additional suitable areas and provided valuable information on functional features of habitat suitability for both test species (narrowly vs. widely distributed), for both coarse and fine scales. Our results suggest a relatively small scale-dependence of the predictive ability of satellite-derived EFAs, supporting their use as meaningful EBVs in SDMs from regional and broader scales to more local and finer scales. Since the evaluation of species' conservation status and habitat quality should as far as possible be performed based on scalable indicators linking to meaningful processes, our framework may guide conservation managers in decision-making related to biodiversity monitoring and reporting schemes., Competing Interests: The authors have declared that no competing interests exist.

Background: Climate change has been widely accepted as one of the major threats for global biodiversity and understanding its potential effects on species distribution is crucial to optimise conservation planning in future scenarios under global change. Integrating detailed climatic data across spatial and temporal scales into species distribution modelling can help to predict potential changes in biodiversity. Consequently, this type of data can be useful for developing efficient biodiversity management and conservation planning. The provision of such data becomes even more important in highly biodiverse regions, currently suffering from climatic and landscape changes. The Transboundary Biosphere Reserve of Meseta Ibérica (BRMI; Portugal-Spain) is one of the most relevant reserves for wildlife in Europe. This highly diverse region is of great ecological and socio-economical interest, suffering from synergistic processes of rural land abandonment and climatic instabilities that currently threaten local biodiversity. Aiming to optimise conservation planning in the Reserve, we provide a complete dataset of historical and future climate models (1 x 1 km) for the BRMI, used to build a series of distribution models for 207 vertebrate species. These models are projected for 2050 under two climate change scenarios. The climatic suitability of 52% and 57% of the species are predicted to decrease under the intermediate and extreme climatic scenarios, respectively. These models constitute framework data for improving local conservation planning in the Reserve, which should be further supported by implementing climate and land-use change factors to increase the accuracy of future predictions of species distributions in the study area. New information: Herein, we provide a complete dataset of state-of-the-art historical and future climate model simulations, generated by global-regional climate model chains, with climatic variables resolved at a high spatial resolution (1 × 1 km) over the Transboundary Biosphere Reserve of Meseta Ibérica. Additionally, a complete series of distribution models for 207 species (168 birds, 24 reptiles and 15 amphibians) under future (2050) climate change scenarios is delivered, which constitute framework data for improving local conservation planning in the reserve. [ABSTRACT FROM AUTHOR]

Highlights • Landscape seasonality and its spatial heterogeneity influence passerine richness. • The importance of landscape functional variability for conservation is highlighted. • Remote sensing provides useful measures of landscape functional dynamics. • Shifts in land management can affect seasonality and drive passerine diversity loss. Abstract The importance of environmental heterogeneity for biodiversity across scales is widely recognized in ecological theory and profusely supported by evidence. However, our understanding of the effects of spatiotemporal patterns of landscape functional properties on biodiversity is still rather limited. We examined the relationship between common passerine species richness and ecosystem functioning dynamics, namely seasonality, measured by satellite remote sensing. We focused on rural landscapes of a mountain National Park in Portugal undergoing rapid reshaping from agro-pastoral mosaics to early successional landscapes. We applied multi-model inference to compare the hypothesis of landscape seasonality as a driver of species richness with three competing hypotheses representing structural habitat heterogeneity, disturbance, and availability of food resources. We found support for landscape seasonality and its spatial heterogeneity in explaining passerine richness in mountain rural landscapes. Conversely, no significant support of the remaining hypotheses was found. These results highlight the role of ecosystem functioning variability in space and time. They also stress the importance of considering species-energy relationships for conservation at the landscape level. Specifically, they provide support and guidance to the identification of meaningful functional attributes of the landscape that shape its biodiversity. Our results further demonstrate the utility of remote sensing approaches and products to measure those attributes and follow their trends through time. Spatially-explicit measures of energy variability, such as the functional amplitude between winter and summer retrieved from earth observations, can link global socio-environmental change to species' responses and support the inclusion of landscape seasonality on conservation and monitoring frameworks. [ABSTRACT FROM AUTHOR]

Context: Current shifts in biodiversity are driven by multiple processes of environmental and landscape change. Particularly, land use/land cover (LU/LC) dynamics are among the major drivers of biodiversity loss worldwide.Objectives: In this study we aim to explore the applicability of a new modelling framework to predict top predators' responses to LU/LC changes.Methods: The framework integrates remote-sensing based predictors, statistical inference, stochastic-dynamic simulations and spatial projections in a common and interactive approach. From an ecological modelling perspective, the main innovation of our approach lies on the integration of (1) biomass of birds of prey as an upper trophic indicator of the community characteristics that emerge from the habitat quality across multiple scales of organization and (2) fine-scale biophysical attributes to add a new level of understanding about the role of local LU/LC drivers influencing those emergent biodiversity patterns.Results: Based on species data from published atlases this approach allowed transposing species biomass to finer resolutions, overcoming the lack of detailed information for the study area. Our demonstrative case study revealed a disruptive effect of ongoing LU/LC changes in the spatio-temporal distribution of top predators' biomass, suggesting the possibility of an emergent disturbance pattern in habitat suitability and community stability. Comparative analysis between simulations and independent field data revealed a promising model performance.Conclusions: Our modelling approach highlights the importance of integrating local LU/LC functional dynamics to predict key trophic' responses, considered as pertinent ecological indicators for biodiversity management under realistic' future changing regional scenarios. [ABSTRACT FROM AUTHOR]

Forests are among the most important ecosystems for the provision of hydrological services. These include water supply and water damage mitigation, in the dimensions of quantity, timing and quality. Although the hydrological role of forests is well documented in the literature, a conceptual framework integrating these three dimensions is still missing. In this study, a comprehensive conceptual framework to improve the assessment of hydrological services provided by forests was developed. In addition, the framework was tested by an illustration for northern Portugal, a region with both Mediterranean and Atlantic climatic influences. The TEEB (The Economics of Ecosystems and Biodiversity) framework of ecosystem services was adapted to the relation between forests and water. Then, this new framework was complemented with a set of spatially-explicit indicators that quantify the supply and demand of hydrological services. In addition, the implications of the framework were discussed in the context of the social-ecological systems, using the DPSIR (Drivers, Pressures, State, Impacts, and Responses) model. Finally, the framework and the indicators were illustrated for northern Portugal using the water supply (quantity) and soil erosion control as examples. Results show that the proposed conceptual framework is a useful tool to support land planning and forest management, adapting the provision of hydrological services to the regional biophysical and social conditions. The test of the framework across a heterogeneous region suggests that a spatially explicit combination of system property, function, service and benefit indicators can be an effective way of analysing and managing the supply and demand of the hydrological services. [ABSTRACT FROM AUTHOR]

Abstract: Biodiversity, soil, water, air, climate, landscape and other components are typically analyzed in Environmental Impact Assessment (EIA) when looking for the effects of a given project on the biological and ecological components of the environment. Strategic Environmental Assessment (SEA) tends to follow the same general pattern, when looking for the environmental effects of plans and programs. In both cases, ecosystem services are often not explicitly considered. We developed a framework to analyze environmental assessment (EA) practice and infer about ecosystem services based on existing evidence from EIA and SEA reports and supporting information. Our framework addresses the relations between ecosystem services and the environmental factors assessed in EIA and SEA, considering the relevant conceptual frameworks such as the ES cascade model and the DPSIR framework applied to ES provision. We base our proposals on results from a preliminary meta-analysis of recent EIA and SEA reports from several types of plans and projects in Portugal, in which implicit assessments of ES were clearly predominant. We discuss the implications of those results and the opportunities to infer about ecosystem services, and conclude on the need for more dedicated and explicit efforts to ecosystem services assessment in EIA and SEA. [Copyright &y& Elsevier]

Abstract: Maintaining farmland biodiversity in Europe under scenarios of agricultural intensification is a keystone challenge of nature conservation. The recruitment of species from the regional pool to local landscape mosaics and individual patches is known to be determined by multi-scale ecological filters. Here we aimed at clarifying the relative importance of the physical environment, land use and landscape structure, and species traits, as filters of landscape-level plant species diversity in intensive farmland. Vascular plant species diversity was surveyed in 18 dairy farmland mosaics along a gradient of agricultural specialisation in Northern Portugal. Plant species were grouped according to their life strategy, biogeographic origin, and synecological preferences. Species richness was found to be highest in lowland areas, where warmer climate and nutrient-rich soils contribute to balance the potential negative effects of intensive farming. Multiple predictors, related to physical environment (e.g. climate), land use (e.g. crop area), and landscape structure (e.g. mean patch size), were found to influence diversity patterns, even under the homogenizing effects of agricultural intensification. Dissimilarity models discriminated distinct types of responses, with patterns for biogeographic and synecological groups of species being better predicted by landscape based models. In contrast, a dominant role of physical predictors was observed in explaining diversity patterns for plant strategies. Overall, our results confirmed that physical environmental gradients, land use, landscape structure, and species traits interact in determining landscape-level plant diversity patterns. Such patterns may influence agro-ecosystem responses to environmental changes, and thus should be considered in the development of agri-environmental policies and monitoring schemes. [ABSTRACT FROM AUTHOR]

A letter to the editor about monitoring global biodiversity is presented.