Suchergebnisse

We acknowledge the use of MODIS imagery obtained from NASA's Land Processes Distributed Active Archive Center (LP DAAC), available free of charge. The authors would like to thank the anonymous reviewers for providing comments and suggestions that helped to improve the quality of the original manuscript. ; Wildfire disturbances can cause modifications in different dimensions of ecosystem functioning, i.e., the flows of matter and energy. There is an increasing need for methods to assess such changes, as functional approaches offer advantages over those focused solely on structural or compositional attributes. In this regard, remote sensing can support indicators for estimating a wide variety of effects of fire on ecosystem functioning, beyond burn severity assessment. These indicators can be described using intra-annual metrics of quantity, seasonality, and timing, called Ecosystem Functioning Attributes (EFAs). Here, we propose a satellite-based framework to evaluate the impacts, at short to medium term (i.e., from the year of fire to the second year after), of wildfires on four dimensions of ecosystem functioning: (i) primary productivity, (ii) vegetation water content, (iii) albedo, and (iv) sensible heat. We illustrated our approach by comparing inter-annual anomalies in satellite-based EFAs in the northwest of the Iberian Peninsula, from 2000 to 2018. Random Forest models were used to assess the ability of EFAs to discriminate burned vs. unburned areas and to rank the predictive importance of EFAs. Together with effect sizes, this ranking was used to select a parsimonious set of indicators for analyzing the main effects of wildfire disturbances on ecosystem functioning, for both the whole study area (i.e., regional scale), as well as for four selected burned patches with different environmental conditions (i.e., local scale). With both high accuracies (area under the receiver operating characteristic curve (AUC) > 0.98) and effect sizes (Cohen's |d| > 0.8), we found important effects on all four dimensions, especially on primary productivity and sensible heat, with the best performance for quantity metrics. Different spatiotemporal patterns of wildfire severity across the selected burned patches for different dimensions further highlighted the importance of considering the multi-dimensional effects of wildfire disturbances on key aspects of ecosystem functioning at different timeframes, which allowed us to diagnose both abrupt and lagged effects. Finally, we discuss the applicability as well as the potential advantages of the proposed approach for more comprehensive assessments of fire severity. ; Portuguese national funds through FCT-Foundation for Science and Technology, I.P., under the GreenRehab project PCIF/RPG/0077/2017 ; Junta de Andalucia P18-RT-1927 ; European Union Funds for Regional Development ; Project DETECTOR A-RNM-256-UGR18 ; European Commission ; Portuguese Foundation for Science and Technology European Commission ; Ministry of Education, Culture, Sports, Science and Technology, Japan (MEXT) European Commission ; European Social Fund, within the 2014-2020 EU Strategic Framework, through FCT SFRH/BD/99469/2014 ; Individual Scientific Employment Stimulus Program (2017), through FCT CEECIND/02331/2017

Supplementary information accompanies this paper at https://doi.org/10.1038/s41598-019-40766-5. ; 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. ; This research was developed as part of the project ECOPOTENTIAL, which received funding from the European Union's Horizon 2020 Research and Innovation Programme under agreement No. 641762. We thank everyone who contributed to the fieldwork: Xosé Pardavila, Adrián Lamosa (Sorex, Ecoloxía e Medio Ambiente SL), Marta Arenas, Alberto Toupa and Fernando Martínez-Freiría. Field surveys were funded by the project INTERREG-POCTEC ('NATURA Xurés-Gerês'). A.R. was financially supported by the Xunta de Galicia (post-doctoral fellowship ED481B2016/084-0). Miquel Ninyerola and Meritxell Batalla (UAB) generated climate variables from data provided by the Spanish Meteorological Agency and the Spanish Ministry of Marine and Rural Environment within the MONTES-Consolider project (CSD2008-00040).

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. EFAbased 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, EFAbased 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. ; This research was developed as part of the ECOPOTENTIAL project financed by European Union's Horizon 2020 research and innovation program under grant agreement No. 641762. SAC, DAS and JPH received funding from the ECOPOTENTIAL project. JG was supported by FCT (Portuguese Science Foundation) through PhD grant SFRH/BD/90112/2012. DAS received funding from Ministerio de Educación, Cultura y Deporte, JC2015-00316 grant, and Ministerio de Ciencia e Innovación, CGL2014-61610-EXP project.

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 ...

Global environmental changes are affecting both the distribution and abundance of species at an unprecedented rate. To assess these effects, species distribution models (SDMs) have been greatly developed over the last decades, while species abundance models (SAMs) have generally received less attention even though these models provide essential information for conservation management. With population abundance defined as an essential biodiversity variable (EBV), SAMs could offer spatially explicit predictions of species abundance across space and time. Satellite-derived ecosystem functioning attributes (EFAs) are known to inform on processes controlling species distribution, but they have not been tested as predictors of species abundance. In this study, we assessed the usefulness of SAMs calibrated with EFAs (as process-related variables) to predict local abundance patterns for a rare and threatened species (the narrow Iberian endemic 'Gerês lily' Iris boissieri; protected under the European Union Habitats Directive), and to project inter-annual fluctuations of predicted abundance. We compared the predictive accuracy of SAMs calibrated with climate (CLI), topography (DEM), land cover (LCC), EFAs, and combinations of these. Models fitted only with EFAs explained the greatest variance in species abundance, compared to models based only on CLI, DEM, or LCC variables. The combination of EFAs and topography slightly increased model performance. Predictions of the inter-annual dynamics of species abundance were related to inter-annual fluctuations in climate, which holds important implications for tracking global change effects on species abundance. This study underlines the potential of EFAs as robust predictors of biodiversity change through population size trends. The combination of EFA-based SAMs and SDMs would provide an essential toolkit for species monitoring programs. ; This work has been carried out within the H2020 project ECOPOTENTIAL: Improving Future Ecosystem Benefits Through Earth Observations (http://www.ecopotential-project.eu). The project has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement No. 641762. S.A.-C., D.A.-S., and J.H. received funding from the ECOPOTENTIAL project. A.R. was financially supported by the Xunta de Galicia, Spain (post-doctoral fellowship ED481B2016/084-0). J.F.G. was funded by the Individual Scientific Employment Stimulus Program (2017) by the Portuguese Foundation for Science and Technology (FCT CEEC-2017).

Global environmental changes are affecting both the distribution and abundance of species at an unprecedented rate. To assess these effects, species distribution models (SDMs) have been greatly developed over the last decades, while species abundance models (SAMs) have generally received less attention even though these models provide essential information for conservation management. With population abundance defined as an essential biodiversity variable (EBV), SAMs could offer spatially explicit predictions of species abundance across space and time. Satellite-derived ecosystem functioning attributes (EFAs) are known to inform on processes controlling species distribution, but they have not been tested as predictors of species abundance. In this study, we assessed the usefulness of SAMs calibrated with EFAs (as process-related variables) to predict local abundance patterns for a rare and threatened species (the narrow Iberian endemic 'Gerês lily' Iris boissieri; protected under the European Union Habitats Directive), and to project inter-annual fluctuations of predicted abundance. We compared the predictive accuracy of SAMs calibrated with climate (CLI), topography (DEM), land cover (LCC), EFAs, and combinations of these. Models fitted only with EFAs explained the greatest variance in species abundance, compared to models based only on CLI, DEM, or LCC variables. The combination of EFAs and topography slightly increased model performance. Predictions of the inter-annual dynamics of species abundance were related to inter-annual fluctuations in climate, which holds important implications for tracking global change effects on species abundance. This study underlines the potential of EFAs as robust predictors of biodiversity change through population size trends. The combination of EFA-based SAMs and SDMs would provide an essential toolkit for species monitoring programs. [Note: this resource includes the output dataset]

The growth in data availability and in the number of users from non-spatial disciplines has increased the concern with spatial data management and spatial data quality. This reality contextualizes the need and importance of data quality management throughout the spatial data cycle related to data editing and sharing in the context of knowledge networks. The development of standard concepts, procedures and tools may foster important advances in the improvement of spatial dataset production, use and management practices. The implementation of environmental monitoring programs implies a growing number and diversity of users with specific capabilities and responsibilities. In this context, spatial data quality evaluation and management promotes communication, optimizes processes of analysis and spatial modelling, and in this sense improves research and political and technical decisionmaking and action. This paper focuses on the methods and tools currently available to evaluate the external quality of datasets for environmental and ecological monitoring. A novel framework is presented that allows integrating and interconnecting spatial data quality evaluation with metadata geoportals in WebGIS platforms, facilitating evaluation by users with often limited expertise in this field. The advances achieved in this research highlight the relevance of developing capacities for different users to improve data collection, data models, spatial data processing and modelling, but also the need to inform and report on spatial data quality using adequate tools.

The growth in data availability and in the number of users from non-spatial disciplines has increased the concern with spatial data management and spatial data quality. This reality contextualizes the need and importance of data quality management throughout the spatial data cycle related to data editing and sharing in the context of knowledge networks. The development of standard concepts, procedures and tools may foster important advances in the improvement of spatial dataset production, use and management practices. The implementation of environmental monitoring programs implies a growing number and diversity of users with specific capabilities and responsibilities. In this context, spatial data quality evaluation and management promotes communication, optimizes processes of analysis and spatial modelling, and in this sense improves research and political and technical decisionmaking and action. This paper focuses on the methods and tools currently available to evaluate the external quality of datasets for environmental and ecological monitoring. A novel framework is presented that allows integrating and interconnecting spatial data quality evaluation with metadata geoportals in WebGIS platforms, facilitating evaluation by users with often limited expertise in this field. The advances achieved in this research highlight the relevance of developing capacities for different users to improve data collection, data models, spatial data processing and modelling, but also the need to inform and report on spatial data quality using adequate tools.

In their paper entitled "Wildfires as an ecosystem service", Pausas and Keeley (2019) summarize the benefits generated by – as well as the evolutionary and socioecological importance of – wildfires for humankind. Although we recognize the importance of wildfires in such a context, we argue that presenting wildfire per se as an ecosystem service is conceptually incorrect and can be misleading for policy makers and resource managers. Throughout their paper, the authors repeatedly refer to (wild)fire as a potential provider of multiple ecosystem services (and not as an ecosystem service itself, as indicated in their article's title). We believe that this is more than a dispute over semantics, for such a contradiction could lead to misperceptions about the definition of the term " ecosystem services", which is especially concerning in light of its real-world applications to fire management. ; ÂS received support from the Portuguese Foundation for Science and Technology (FCT) through PhD grant SFRH/BD/ 132838/2017, funded by the Ministry of Science, Technology and Higher Education, and by the European Social Fund–Operational Program Human Capital within the 2014–2020 EU Strategic Framework. AR is funded by Xunta de Galicia (post-doctoral fellowship ED481B2016/084-0). This research was developed as part of the project FirESmart (PCIF/MOG/0083/2017), which received funding from the FCT. The authors declare that they have no conflicts of interest. ; info:eu-repo/semantics/publishedVersion

'Regime shift' has emerged as a key concept in the environmental sciences. The concept has roots in complexity science and its ecological applications, and is increasingly applied to intertwined social and ecological phenomena. Yet what exactly is a regime shift? We explore this question at three nested levels. First, we propose a broad, contingent, multi-perspective epistemological basis for the concept, seeking to build bridges between its complexity theory origins and critiques from science studies, political ecology, and environmental history. Second, we define the concept in a way that is consistent with this epistemology, building on previous work on speed, scale, stickiness, and interrelationships, but also emphasising human perceptions and rhetorical uses of the notion. Third, we propose a novel typology of the ways in which the regime shift concept is used in analysing social–environmental phenomena in geography and beyond. These uses are categorised along two axes. On the one side, we distinguish between description of past or present changes and normative prescriptions for the future. On the other side, we distinguish between whether the focus is on material shifts (social and ecological) or conceptual shifts (discourses and ideas). We illustrate the typology with reference to social–environmental changes in landscapes around the world that are dominated by plantations or the widespread naturalisation of Australian Acacia species. We conclude that the regime shift concept is a boundary object with value as both an analytical and communicative tool in addressing social–environmental challenges.

AbstractAgricultural abandonment is a major driver of change in rural landscapes. Assumed to provide beneficial results to the environment and the conservation of biota, rural abandonment triggers landscape and biotic homogenization and loss of valuable species and habitats. This article focuses on the ecological effects and conservation challenges of shifts in extensive grazing regimes on marginal pastureland of Mediterranean mountains. We conceptualize a navigated socioecological transition toward conservation‐oriented management after the collapse of historical land systems. The article provides examples from the LIFE+ project "Higro," developed in mountainous protected areas in Portugal, of how management for conservation could sustain disturbance‐dependent habitats. We argue that actively and regularly managing large habitat areas should be envisaged as a short‐term approach to limit the immediate effects of rural abandonment. A gradual integration of conservation targets with other activities in changing rural economies is necessary to foster long‐term conservation of species and habitats, building on the link between conservation‐oriented habitat management and ecosystem services in rural landscapes. Conservation goals should run alongside recovery of social systems and innovation applied to traditional sources of income. This parallel development would contribute to building up social‐ecological resilience by maintaining a diversity of social and ecological capital in rural areas.

Ecological monitoring programmes are designed to detect and measure changes in biodiversity and ecosystems. In the case of biological invasions, they can contribute to anticipating risks and adaptively managing invaders. However, monitoring is often expensive because large amounts of data might be needed to draw inferences. Thus, careful planning is required to ensure that monitoring goals are realistically achieved. Species distribution models (SDM s) can provide estimates of suitable areas to invasion. Predictions from these models can be applied as inputs in optimization strategies seeking to identify the optimal extent of the networks of areas required for monitoring risk of invasion under current and future environmental conditions. A hierarchical framework is proposed herein that combines SDM s, scenario analysis and cost analyses to improve invasion assessments at regional and local scales. We illustrate the framework with Acacia dealbata Link. (Silver‐wattle) in northern Portugal. The framework is general and applicable to any species. We defined two types of monitoring networks focusing either on the regional‐scale management of an invasion, or management focus within and around protected areas. For each one of these two schemes, we designed a hierarchical framework of spatial prioritization using different information layers (e.g. SDM s, habitat connectivity, protected areas). We compared the performance of each monitoring scheme against 100 randomly generated models. In our case study, we found that protected areas will be increasingly exposed to invasion by A. dealbata due to climate change. Moreover, connectivity between suitable areas for A. dealbata is predicted to increase. Monitoring networks that we identify were more effective in detecting new invasions and less costly to management than randomly generated models. The most cost‐efficient monitoring schemes require 18% less effort than the average networks across all of the 100 tested options. Synthesis and applications . The proposed framework achieves cost‐effective monitoring networks, enabling the interactive exploration of different solutions and the combination of quantitative information on network performance with orientations that are rarely incorporated in a decision support system. The framework brings invasion monitoring closer to European legislation and management needs while ensuring adaptability under rapid climate and environmental change.

Identifying the key factors driving invasion processes is crucial for designing and implementing appropriate management strategies. In fact, the importance of (model-based) prevention and early detection was highlighted in the recent European Union regulation on Invasive Alien Species. Models based on abundance estimates for different age/size classes would represent a significant improvement relative to the more usual models based only on species' occurrence data. Here, we evaluate the relative contribution of different environmental drivers to the spatial patterns of abundance of several height classes (or life-stages) of invasive tree populations at the regional scale, using a data-driven hierarchical modelling approach. A framework for modelling life-stages to obtain spatial projections of their potential occurrence or abundance has not been formalized before. We used Acacia dealbata (Silver-wattle) as a test species in northwest of Portugal, a heavily invaded region, and applied a multimodel inference to test the importance of various environmental drivers in explaining the abundance patterns of five plant height classes in local landscape mosaics. The ensemble of height classes is considered here as a proxy for population dynamics, life-stages and age of adult trees. In this test with A. dealbata, we used detailed field data on population height structure and calibrated an independent model for each height class. We found evidence to support our hypothesis that the distribution of height classes is mostly influenced by distinct factors operating at different scales. The spatial projections which resulted from several height class models provide an overview of population structure and invasion dynamics considering various life-stages, that is widely used in biodiversity and invasion research. The approach proposed here provides a framework to guide forest management to deal more effectively with plant invasions. It allows to test the effects of key invasion factors (depending on the focal species and on data availability) and supports the spatial identification of suitable areas for invasive species' occurrence while also accounting for the structural complexity of invasive species populations, thereby anticipating future invasion dynamics. The approach thus constitutes a step forward for establishing management actions at appropriate spatial scales and for focusing on earlier stages of invasion and their respective driving factors (regeneration niche), thereby enhancing the efficiency of control actions on major forest invaders.