Potential of satellite-derived ecosystem functional attributes to anticipate species range shifts

Alcaraz‐Segura, Domingo; Lomba, Ângela; Sousa-Silva, Rita; Nieto‐Lugilde, Diego; Alves, Paulo; Georges, Damien; Vicente, Joana R.; Honrado, João P.

doi:10.1016/j.jag.2016.12.009

Cited by 47 publications

(82 citation statements)

References 44 publications

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“…In fact, these RS‐derived functional variables have been suggested as essential biodiversity variables in SDMs to provide early warnings of range shifts and predictions of short‐term fluctuations in suitable conditions for multiple plant species (Alcaraz‐Segura et al. ).…”

Section: Discussionmentioning

confidence: 99%

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Integrating intraseasonal grassland dynamics in cross‐scale distribution modeling to support waterbird recovery plans

Regos

Vidal

Lorenzo

et al. 2019

Conservation Biology

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Despite much discussion about the utility of remote sensing for effective conservation, the inclusion of these technologies in species recovery plans remains largely anecdotal. We developed a modeling approach for the integration of local, spatially measured ecosystem functional dynamics into a species distribution modeling (SDM) framework in which other ecologically relevant factors are modeled separately at broad scales. To illustrate the approach, we incorporated intraseasonal water‐vegetation dynamics into a cross‐scale SDM for the Common Snipe (Gallinago gallinago), which is highly dependent on water and vegetation dynamics. The Common Snipe is an Iberian grassland waterbird characteristic of European agricultural meadows and a member of one of the most threatened bird guilds. The intraseasonal dynamics of water content of vegetation were measured using the standard deviation of the normalized difference water index time series computed from bimonthly images of the Sentinel‐2 satellite. The recovery plan for the Common Snipe in Galicia (northwestern Iberian Peninsula) provided an opportunity to apply our modeling framework. Model accuracy in predicting the species’ distribution at a regional scale (resulting from integration of downscaled climate projections with regional habitat–topographic suitability models) was very high (area under the curve [AUC] of 0.981 and Boyce's index of 0.971). Local water‐vegetation dynamic models, based exclusively on Sentinel‐2 imagery, were good predictors (AUC of 0.849 and Boyce's index of 0.976). The predictive power improved (AUC of 0.92 and Boyce's index of 0.98) when local model predictions were restricted to areas identified by the continental and regional models as priorities for conservation. Our models also performed well (AUC of 0.90 and Boyce's index of 0.93) when projected to updated water‐vegetation conditions. Our modeling framework enabled incorporation of key ecosystem processes closely related to water and carbon cycles while accounting for other factors ecologically relevant to endangered grassland waterbirds across different scales, allowed identification of priority areas for conservation, and provided an opportunity for cost‐effective recovery planning by monitoring management effectiveness from space.

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Section: Discussionmentioning

confidence: 99%

“…SDMs based on functional variables that are closely related to habitat dynamics and ecosystem processes may complement traditional sets of predictor variables describing overall climate conditions or static habitat features (Alcaraz‐Segura et al. ; Arenas‐Castro et al. ).…”

Section: Introductionmentioning

confidence: 99%

Integrating intraseasonal grassland dynamics in cross‐scale distribution modeling to support waterbird recovery plans

Regos

Vidal

Lorenzo

et al. 2019

Conservation Biology

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“…Satellite-derived EFAs can, therefore, be used as process-related predictors in biodiversity models, since they provide information about ecosystem processes and properties related to the carbon (e.g., vegetation greenness) and water cycles (e.g., evapotranspiration) or with energy balance (e.g., land surface temperature and albedo). In fact, satellite-derived EFAs are being also tested as candidate EBVs related to the carbon cycle, energy, and radiation balance capable of informing about ecosystem components linked to species conservation status [26,27]. Thus, the incorporation of EFAs into SDMs was found to increase their predictive power and transferability [28], offering the opportunity to cost-effectively monitor multiple endangered species [27,29], at different spatial and temporal scales [30].…”

Section: Introductionmentioning

confidence: 99%

Remotely Sensed Variables of Ecosystem Functioning Support Robust Predictions of Abundance Patterns for Rare Species

et al. 2019

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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.

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“…However, to improve our understanding of the land‐atmosphere interactions, we propose that it is also needed to include information about ecosystem functionality (Bond‐Lamberty et al, ; Petrakis et al, ; Reichstein et al, ). The addition of information on ecosystem functional attributes has enhanced biodiversity models (Alcaraz‐Segura et al, , ; Armas et al, ) and is useful for network design and assessment (Villarreal et al, ). We argue that including information related to environmental state factors along with ecosystem functional heterogeneity could complement bioclimatic information to define ESAs, since ecosystem‐level observations suggest that climate controls are insufficient to explain land‐atmosphere interactions (Reichstein et al, ).…”

Section: Discussionmentioning

confidence: 99%

“…Ecosystem functional traits can be represented by a land surface classification known as EFTs, conceptually defined as patches of the land surface that exchange matter and energy with the atmosphere in a similar way (Alcaraz‐Segura et al, ; Alcaraz‐Segura et al, ; Paruelo et al, ). In practice, EFTs are a yearly varying land surface classification based on satellite‐derived key ecosystem functional attributes (i.e., descriptors of primary productivity, seasonality, and phenology of carbon gains; Alcaraz‐Segura et al, ; Lee et al, ). These attributes are obtained from annual curves of spectral vegetation indices.…”

Section: Methodsmentioning

confidence: 99%

Optimizing an Environmental Observatory Network Design Using Publicly Available Data

et al. 2019

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There is a need to optimize resources for large‐scale environmental monitoring efforts, especially in developing countries. We tested a flexible framework to optimize the design (i.e., selection of study sites) of an environmental observatory network (EON) using publicly available data for Mexico. This country represents a challenge for designing EONs because of its megadiversity and large climate and ecological heterogeneity. We address three pervasive challenges for designing EONs: (1) How to characterize and delineate ecologically similar areas, (2) how to set geographic priorities to establish new representative study sites, and (3) how to assess the representativeness of current and potential new study sites. We used unsupervised cluster analysis to spatially delineate ecologically similar sampling domains. We identified the most representative sites within each domain using a conditioned Latin Hypercube‐sampling strategy. Finally, we demonstrated the applicability of this approach by assessing the spatial representativeness of the eddy covariance network in Mexico (i.e., MexFlux). At least 84 distributed sampling sites are needed to represent >45% of the spatial heterogeneity of gross primary productivity (GPP) dynamics (i.e., GPP_mean and GPP_cv) and evapotranspiration (ET) dynamics (i.e., ET_mean and ET_cv) at the national level. The current array of MexFlux only represents 3% of GPP and 5% of ET dynamics spatial variability at the national‐level, while the same number of sites organized under an optimal framework nearly doubled these estimates. Our framework is based on a data‐driven approach and publicly available sources of information, so it could be applied anywhere in the world.

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Potential of satellite-derived ecosystem functional attributes to anticipate species range shifts

Cited by 47 publications

References 44 publications

Integrating intraseasonal grassland dynamics in cross‐scale distribution modeling to support waterbird recovery plans

Integrating intraseasonal grassland dynamics in cross‐scale distribution modeling to support waterbird recovery plans

Remotely Sensed Variables of Ecosystem Functioning Support Robust Predictions of Abundance Patterns for Rare Species

Optimizing an Environmental Observatory Network Design Using Publicly Available Data

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