Open AccessLinking animal movement and remote sensing – mapping resource suitability from a remote sensing perspective
Author(s) -
Remelgado Ruben,
Leutner Benjamin,
Safi Kamran,
Sonnenschein Ruth,
Kuebert Carina,
Wegmann Martin
Publication year - 2018
Publication title -
remote sensing in ecology and conservation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.191
H-Index - 21
ISSN - 2056-3485
DOI - 10.1002/rse2.70
Subject(s) - remote sensing , context (archaeology) , resource (disambiguation) , foraging , environmental science , population , spatial contextual awareness , computer science , ecology , geography , biology , computer network , demography , archaeology , sociology
Optical remote sensing is an important tool in the study of animal behavior providing ecologists with the means to understand species–environment interactions in combination with animal movement data. However, differences in spatial and temporal resolution between movement and remote sensing data limit their direct assimilation. In this context, we built a data‐driven framework to map resource suitability that addresses these differences as well as the limitations of satellite imagery. It combines seasonal composites of multiyear surface reflectances and optimized presence and absence samples acquired with animal movement data within a cross‐validation modeling scheme. Moreover, it responds to dynamic, site‐specific environmental conditions making it applicable to contrasting landscapes. We tested this framework using five populations of White Storks ( Ciconia ciconia ) to model resource suitability related to foraging achieving accuracies from 0.40 to 0.94 for presences and 0.66 to 0.93 for absences. These results were influenced by the temporal composition of the seasonal reflectances indicated by the lower accuracies associated with higher day differences in relation to the target dates. Additionally, population differences in resource selection influenced our results marked by the negative relationship between the model accuracies and the variability of the surface reflectances associated with the presence samples. Our modeling approach spatially splits presences between training and validation. As a result, when these represent different and unique resources, we face a negative bias during validation. Despite these inaccuracies, our framework offers an important basis to analyze species–environment interactions. As it standardizes site‐dependent behavioral and environmental characteristics, it can be used in the comparison of intra‐ and interspecies environmental requirements and improves the analysis of resource selection along migratory paths. Moreover, due to its sensitivity to differences in resource selection, our approach can contribute toward a better understanding of species requirements.