Remote-sensing based approach to forecast habitat quality under climate change scenarios

Abstract

As climate change is expected to have a significant impact on species distributions, there is an urgent challenge to provide reliable information to guide conservation biodiversity policies. In addressing this challenge, we propose a remote sensing-based approach to forecast the future habitat quality for European badger, a species not abundant and at risk of local extinction in the arid environments of southeastern Spain, by incorporating environmental variables related with the ecosystem functioning and correlated with climate and land use. Using ensemble prediction methods, we designed global spatial distribution models for the distribution range of badger using presence-only data and climate variables. Then, we constructed regional models for an arid region in the southeast Spain using EVI (Enhanced Vegetation Index) derived variables and weighting the pseudo-absences with the global model projections applied to this region. Finally, we forecast the badger potential spatial distribution in the time period 2071–2099 based on IPCC scenarios incorporating the uncertainty derived from the predicted values of EVI-derived variables. By including remotely sensed descriptors of the temporal dynamics and spatial patterns of ecosystem functioning into spatial distribution models, results suggest that future forecast is less favorable for European badgers than not including them. In addition, change in spatial pattern of habitat suitability may become higher than when forecasts are based just on climate variables. Since the validity of future forecast only based on climate variables is currently questioned, conservation policies supported by such information could have a biased vision and overestimate or underestimate the potential changes in species distribution derived from climate change. The incorporation of ecosystem functional attributes derived from remote sensing in the modeling of future forecast may contribute to the improvement of the detection of ecological responses under climate change scenarios.

Assessing the multi-scale predictive ability of ecosystem functional attributes for species distribution modelling

“…Several studies e.g. [ 35 , 50 , 110 , 116 ], and our results, have confirmed that remotely sensed variables related to ecosystem functioning (e.g. EFAs) can be useful predictors for modelling protected species distribution and habitat suitability.…”

“…Overall, our results confirmed the known predictive ability of the satellite-derived EFAs at coarse resolutions [ 35 ], and expanded it to finer scales, even at the protected area level. This provides a local scale refined prediction of species potential distribution and habitat suitability that is closer to the scale required for management actions [ 6 , 109 , 110 ]. Such predictions could be further improved thanks to the availability of EFAs at even finer spatial resolutions (e.g., MODIS at ~250 m, LANDSAT at ~30m or Sentinel-2A at ~10m) and higher temporal resolutions (MODIS or the combination of LANDSAT plus Sentinel-2A), which is still a major constraint when using climate interpolated surfaces and land-cover data [ 35 ].…”

Identifying priority conservation areas in a Saharan environment by highlighting the endangered Cuvier’s Gazelle as a flagship species

“…In this study, we applied species distribution models, also known as habitat suitability models, a technique that combines information on species occurrence or abundance with environmental estimates and/or spatial characteristics [21][22][23] . Species distribution models are mainly used to address the potential effects of climate change on species distribution [24][25][26] but are also used to improve the understanding of ecological factors for conservation planning and to detect unknown potential distribution areas for rare species [27][28][29] . Our goal is to identify the main ecological features of the southernmost subset of the Cuvier's Gazelle population in the Sahara; for this purpose, we integrated data from a broad-scale field survey carried out from 2011 to 2014 and species distribution models to model habitat selection at the landscape scale, using a predictive distribution map.…”

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

“…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]. Still, despite these advantages, the predictive ability of EFAs in abundance models as well as to assess inter-annual population dynamics of rare species remains largely untested.…”