ORCID Profile
0000-0002-7896-1237
Current Organisations
Smithsonian Institution
,
Smithsonian Conservation Biology Institute
Does something not look right? The information on this page has been harvested from data sources that may not be up to date. We continue to work with information providers to improve coverage and quality. To report an issue, use the Feedback Form.
Publisher: American Association for the Advancement of Science (AAAS)
Date: 09-06-2023
Abstract: COVID-19 lockdowns in early 2020 reduced human mobility, providing an opportunity to disentangle its effects on animals from those of landscape modifications. Using GPS data, we compared movements and road avoidance of 2300 terrestrial mammals (43 species) during the lockdowns to the same period in 2019. In idual responses were variable with no change in average movements or road avoidance behavior, likely due to variable lockdown conditions. However, under strict lockdowns 10-day 95th percentile displacements increased by 73%, suggesting increased landscape permeability. Animals’ 1-hour 95th percentile displacements declined by 12% and animals were 36% closer to roads in areas of high human footprint, indicating reduced avoidance during lockdowns. Overall, lockdowns rapidly altered some spatial behaviors, highlighting variable but substantial impacts of human mobility on wildlife worldwide.
Publisher: Springer Science and Business Media LLC
Date: 27-05-2023
DOI: 10.1038/S41467-023-38901-Y
Abstract: New satellite remote sensing and machine learning techniques offer untapped possibilities to monitor global bio ersity with unprecedented speed and precision. These efficiencies promise to reveal novel ecological insights at spatial scales which are germane to the management of populations and entire ecosystems. Here, we present a robust transferable deep learning pipeline to automatically locate and count large herds of migratory ungulates (wildebeest and zebra) in the Serengeti-Mara ecosystem using fine-resolution (38-50 cm) satellite imagery. The results achieve accurate detection of nearly 500,000 in iduals across thousands of square kilometers and multiple habitat types, with an overall F1-score of 84.75% (Precision: 87.85%, Recall: 81.86%). This research demonstrates the capability of satellite remote sensing and machine learning techniques to automatically and accurately count very large populations of terrestrial mammals across a highly heterogeneous landscape. We also discuss the potential for satellite-derived species detections to advance basic understanding of animal behavior and ecology.
Publisher: Wiley
Date: 26-07-2021
DOI: 10.1111/ELE.13848
Abstract: The forage maturation hypothesis (FMH) states that energy intake for ungulates is maximised when forage biomass is at intermediate levels. Nevertheless, metabolic allometry and different digestive systems suggest that resource selection should vary across ungulate species. By combining GPS relocations with remotely sensed data on forage characteristics and surface water, we quantified the effect of body size and digestive system in determining movements of 30 populations of hindgut fermenters (equids) and ruminants across biomes. Selection for intermediate forage biomass was negatively related to body size, regardless of digestive system. Selection for proximity to surface water was stronger for equids relative to ruminants, regardless of body size. To be more generalisable, we suggest that the FMH explicitly incorporate contingencies in body size and digestive system, with small‐bodied ruminants selecting more strongly for potential energy intake, and hindgut fermenters selecting more strongly for surface water.
Publisher: Wiley
Date: 09-05-2022
DOI: 10.1111/GEB.13523
Abstract: Macroecological studies that require habitat suitability data for many species often derive this information from expert opinion. However, expert‐based information is inherently subjective and thus prone to errors. The increasing availability of GPS tracking data offers opportunities to evaluate and supplement expert‐based information with detailed empirical evidence. Here, we compared expert‐based habitat suitability information from the International Union for Conservation of Nature (IUCN) with habitat suitability information derived from GPS‐tracking data of 1,498 in iduals from 49 mammal species. Worldwide. 1998–2021. Forty‐nine terrestrial mammal species. Using GPS data, we estimated two measures of habitat suitability for each in idual animal: proportional habitat use (proportion of GPS locations within a habitat type), and selection ratio (habitat use relative to its availability). For each in idual we then evaluated whether the GPS‐based habitat suitability measures were in agreement with the IUCN data. To that end, we calculated the probability that the ranking of empirical habitat suitability measures was in agreement with IUCN's classification into suitable, marginal and unsuitable habitat types. IUCN habitat suitability data were in accordance with the GPS data ( 95% probability of agreement) for 33 out of 49 species based on proportional habitat use estimates and for 25 out of 49 species based on selection ratios. In addition, 37 and 34 species had a 50% probability of agreement based on proportional habitat use and selection ratios, respectively. We show how GPS‐tracking data can be used to evaluate IUCN habitat suitability data. Our findings indicate that for the majority of species included in this study, it is appropriate to use IUCN habitat suitability data in macroecological studies. Furthermore, we show that GPS‐tracking data can be used to identify and prioritize species and habitat types for re‐evaluation of IUCN habitat suitability data.
Location: United States of America
No related grants have been discovered for Jared Stabach.