Movement, resource selection, and the physiological stress response of white-bearded wildebeest

Includes bibliographical references.2015 Summer.To view the abstract, please see the full text of the document

Enhancing animal movement analyses: spatiotemporal matching of animal positions with remotely sensed data using Google Earth Engine and R

Movement ecologists have witnessed a rapid increase in the amount of animal position data collected over the past few decades, as well as a concomitant increase in the availability of ecologically relevant remotely sensed data. Many researchers, however, lack the computing resources necessary to incorporate the vast spatiotemporal aspects of datasets available, especially in countries with less economic resources, limiting the scope of ecological inquiry. We developed an R coding workflow that bridges the gap between R and the multi-petabyte catalogue of remotely sensed data available in Google Earth Engine (GEE) to efficiently extract raster pixel values that best match the spatiotemporal aspects (i.e., spatial location and time) of each animal’s GPS position. We tested our approach using movement data freely available on Movebank (movebank.org). In a first case study, we extracted Normalized Difference Vegetation Index information from the MOD13Q1 data product for 12,344 GPS animal locations by matching the closest MODIS image in the time series to each GPS fix. Data extractions were completed in approximately 3 min. In a second case study, we extracted hourly air temperature from the ERA5-Land dataset for 33,074 GPS fixes from 12 different wildebeest (Connochaetes taurinus) in approximately 34 min. We then investigated the relationship between step length (i.e., the net distance between sequential GPS locations) and temperature and found that animals move less as temperature increases. These case studies illustrate the potential to explore novel questions in animal movement research using high-temporal-resolution, remotely sensed data products. The workflow we present is efficient and customizable, with data extractions occurring over relatively short time periods. While computing times to extract remotely sensed data from GEE will vary depending on internet speed, the approach described has the potential to facilitate access to computationally demanding processes for a greater variety of researchers and may lead to increased use of remotely sensed data in the field of movement ecology. We present a step-by-step tutorial on how to use the code and adapt it to other data products that are available in GEE

Spatiotemporal dynamics of wild herbivore species richness and occupancy across a savannah rangeland:Implications for conservation

Private lands are critical for maintaining biodiversity beyond protected areas. Across Kenyan rangelands, wild herbivores frequently coexist with people and their livestock. Human population and livestock numbers are projected to increase dramatically over the coming decades. Therefore, a better understanding of wildlife-livestock interactions and their consequences for biodiversity conservation on private lands is needed. We used a Bayesian hierarchical, multi-species and multi-year occupancy model on aerial survey data of 15 wild-herbivore species, spanning 15 years (2001–2016) to investigate a) spatiotemporal trends in species occurrence and richness across a mosaic of properties with different land uses in Laikipia County, central Kenya; and b) the effects of distance to water, vegetation and livestock relative abundance on species occurrence and richness. Although mean herbivore species richness varied little over time, we observed high spatial variation in species occurrence across Laikipia, mainly driven by negative effects of high livestock relative abundance. As expected, ‘wildlife friendly’ properties had higher herbivore species richness than other areas. However, high variability suggests that some pastoral properties support rich herbivore communities. The area occupied by five species with global conservation concerns (reticulated giraffe, Grevy's zebra, Beisa Oryx, Defassa waterbuck and gerenuk) and for which Laikipia County is one of the last refuges was <50% across years. We conclude that ‘wildlife friendly’ properties remain crucial for conservation, although some pastoralist areas offer suitable habitats for wild herbivores. Effective management of stocking rates is critical for maintaining ecosystems able to sustain livestock and wildlife on private lands, ensuring protection for endangered species

Drivers of site fidelity in ungulates

1. While the tendency to return to previously visited locations—termed ‘site fidelity’—is common in animals, the cause of this behaviour is not well understood. One hypothesis is that site fidelity is shaped by an animal's environment, such that animals living in landscapes with predictable resources have stronger site fidelity. Site fidelity may also be conditional on the success of animals’ recent visits to that location, and it may become stronger with age as the animal accumulates experience in their landscape. Finally, differences between species, such as the way memory shapes site attractiveness, may interact with environmental drivers to modulate the strength of site fidelity. 2. We compared inter‐year site fidelity in 669 individuals across eight ungulate species fitted with GPS collars and occupying a range of environmental conditions in North America and Africa. We used a distance‐based index of site fidelity and tested hypothesized drivers of site fidelity using linear mixed effects models, while accounting for variation in annual range size. 3. Mule deer Odocoileus hemionus and moose Alces alces exhibited relatively strong site fidelity, while wildebeest Connochaetes taurinus and barren‐ground caribou Rangifer tarandus granti had relatively weak fidelity. Site fidelity was strongest in predictable landscapes where vegetative greening occurred at regular intervals over time (i.e. high temporal contingency). Species differed in their response to spatial heterogeneity in greenness (i.e. spatial constancy). Site fidelity varied seasonally in some species, but remained constant over time in others. Elk employed a ‘win‐stay, lose‐switch’ strategy, in which successful resource tracking in the springtime resulted in strong site fidelity the following spring. Site fidelity did not vary with age in any species tested. 4. Our results provide support for the environmental hypothesis, particularly that regularity in vegetative phenology shapes the strength of site fidelity at the inter‐annual scale. Large unexplained differences in site fidelity suggest that other factors, possibly species‐specific differences in attraction to known sites, contribute to variation in the expression of this behaviour. 5. Understanding drivers of variation in site fidelity across groups of organisms living in different environments provides important behavioural context for predicting how animals will respond to environmental change

Providing baseline data for conservation–Heart rate monitoring in captive scimitar-horned oryx

Heart rate biologging has been successfully used to study wildlife responses to natural and human-caused stressors (e.g., hunting, landscape of fear). Although rarely deployed to inform conservation, heart rate biologging may be particularly valuable for assessing success in wildlife reintroductions. We conducted a case study for testing and validating the use of subcutaneous heart rate monitors in eight captive scimitar-horned oryx (Oryx dammah), a once-extinct species that is currently being restored to the wild. We evaluated biologger safety and accuracy while collecting long-term baseline data and assessing factors explaining variation in heart rate. None of the biologgers were rejected after implantation, with successful data capture for 16–21 months. Heart rate detection accuracy was high (83%–99%) for six of the individuals with left lateral placement of the biologgers. We excluded data from two individuals with a right lateral placement because accuracies were below 60%. Average heart rate for the six scimitar-horned oryx was 60.3 ± 12.7 bpm, and varied by about 12 bpm between individuals, with a minimum of 31 bpm and a maximum of 188 bpm across individuals. Scimitar-horned oryx displayed distinct circadian rhythms in heart rate and activity. Heart rate and activity were low early in the morning and peaked near dusk. Circadian rhythm in heart rate and activity were relatively unchanged across season, but hourly averages for heart rate and activity were higher in spring and summer, respectively. Variation in hourly heart rate averages was best explained by a combination of activity, hour, astronomical season, ambient temperature, and an interaction term for hour and season. Increases in activity appeared to result in the largest changes in heart rate. We concluded that biologgers are safe and accurate and can be deployed in free-ranging and reintroduced scimitar-horned oryx. In addition to current monitoring practices of reintroduced scimitar-horned oryx, the resulting biologging data could significantly aid in 1) evaluating care and management action prior to release, 2) characterizing different animal personalities and how these might affect reintroduction outcomes for individual animals, and 3) identifying stressors after release to determine their timing, duration, and impact on released animals. Heart rate monitoring in released scimitar-horned oryx may also aid in advancing our knowledge about how desert ungulates adapt to extreme environmental variation in their habitats (e.g., heat, drought)

Landscape dynamics (landDX) an open-access spatial-temporal database for the Kenya-Tanzania borderlands

The savannas of the Kenya-Tanzania borderland cover >100,000 km2 and is one of the most important regions globally for biodiversity conservation, particularly large mammals. The region also supports >1 million pastoralists and their livestock. In these systems, resources for both large mammals and pastoralists are highly variable in space and time and thus require connected landscapes. However, ongoing fragmentation of (semi-)natural vegetation by smallholder fencing and expansion of agriculture threatens this social-ecological system. Spatial data on fences and agricultural expansion are localized and dispersed among data owners and databases. Here, we synthesized data from several research groups and conservation NGOs and present the first release of the Landscape Dynamics (landDX) spatial-temporal database, covering ~30,000 km2 of southern Kenya. The data includes 31,000 livestock enclosures, nearly 40,000 kilometres of fencing, and 1,500 km2 of agricultural land. We provide caveats and interpretation of the different methodologies used. These data are useful to answer fundamental ecological questions, to quantify the rate of change of ecosystem function and wildlife populations, for conservation and livestock management, and for local and governmental spatial planning.The South Rift Association of Land Owners (specifically grants from the European Union and the Lion Recovery Fund), BigLife Foundation, Esri Conservation Program, Mara Elephant Project, Microsoft AI4Earth programme, the Carlsberg Foundation Semper Ardens project MegaPast2Future, and the VILLUM Investigator project “Biodiversity Dynamics in a Changing World” funded by VILLUM FONDEN, the European Research Council (ERC) project ANTHEA.http://www.nature.com/scientificdataam2023Mammal Research InstituteZoology and Entomolog

Evaluating expert-based habitat suitability information of terrestrial mammals with GPS-tracking data

Aim 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 individuals from 49 mammal species. Location Worldwide. Time period 1998-2021. Major taxa studied Forty-nine terrestrial mammal species. Methods Using GPS data, we estimated two measures of habitat suitability for each individual animal: proportional habitat use (proportion of GPS locations within a habitat type), and selection ratio (habitat use relative to its availability). For each individual 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. Results 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. Main conclusions 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

Body size and digestive system shape resource selection by ungulates : a cross-taxa test of the forage maturation hypothesis

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.DATA AVAILABILITY STATEMENT : The dataset used in our analyses is available via Dryad repository (https://doi.org/10.5061/dryad.jsxksn09f) following a year-long embargo from publication of the manuscript. The coordinates associated with mountain zebra data are not provided in an effort to protect critically endangered black rhino (Diceros bicornis) locations. Interested researchers can contact the data owner (Minnesota Zoo) directly for inquiries.https://wileyonlinelibrary.com/journal/elehj2022Mammal Research InstituteZoology and Entomolog

Behavioral responses of terrestrial mammals to COVID-19 lockdowns

DATA AND MATERIALS AVAILABILITY : The full dataset used in the final analyses (33) and associated code (34) are available at Dryad. A subset of the spatial coordinate datasets is available at Zenodo (35). Certain datasets of spatial coordinates will be available only through requests made to the authors due to conservation and Indigenous sovereignty concerns (see table S1 for more information on data use restrictions and contact information for data requests). These sensitive data will be made available upon request to qualified researchers for research purposes, provided that the data use will not threaten the study populations, such as by distribution or publication of the coordinates or detailed maps. Some datasets, such as those overseen by government agencies, have additional legal restrictions on data sharing, and researchers may need to formally apply for data access. Collaborations with data holders are generally encouraged, and in cases where data are held by Indigenous groups or institutions from regions that are under-represented in the global science community, collaboration may be required to ensure inclusion.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. Individual 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.The Radboud Excellence Initiative, the German Federal Ministry of Education and Research, the National Science Foundation, Serbian Ministry of Education, Science and Technological Development, Dutch Research Council NWO program “Advanced Instrumentation for Wildlife Protection”, Fondation Segré, RZSS, IPE, Greensboro Science Center, Houston Zoo, Jacksonville Zoo and Gardens, Nashville Zoo, Naples Zoo, Reid Park Zoo, Miller Park, WWF, ZCOG, Zoo Miami, Zoo Miami Foundation, Beauval Nature, Greenville Zoo, Riverbanks zoo and garden, SAC Zoo, La Passarelle Conservation, Parc Animalier d’Auvergne, Disney Conservation Fund, Fresno Chaffee zoo, Play for nature, North Florida Wildlife Center, Abilene Zoo, a Liber Ero Fellowship, the Fish and Wildlife Compensation Program, Habitat Conservation Trust Foundation, Teck Coal, and the Grand Teton Association. The collection of Norwegian moose data was funded by the Norwegian Environment Agency, the German Ministry of Education and Research via the SPACES II project ORYCS, the Wyoming Game and Fish Department, Wyoming Game and Fish Commission, Bureau of Land Management, Muley Fanatic Foundation (including Southwest, Kemmerer, Upper Green, and Blue Ridge Chapters), Boone and Crockett Club, Wyoming Wildlife and Natural Resources Trust, Knobloch Family Foundation, Wyoming Animal Damage Management Board, Wyoming Governor’s Big Game License Coalition, Bowhunters of Wyoming, Wyoming Outfitters and Guides Association, Pope and Young Club, US Forest Service, US Fish and Wildlife Service, the Rocky Mountain Elk Foundation, Wyoming Wild Sheep Foundation, Wild Sheep Foundation, Wyoming Wildlife/Livestock Disease Research Partnership, the US National Science Foundation [IOS-1656642 and IOS-1656527, the Spanish Ministry of Economy, Industry and Competitiveness, and by a GRUPIN research grant from the Regional Government of Asturias, Sigrid Rausing Trust, Batubay Özkan, Barbara Watkins, NSERC Discovery Grant, the Federal Aid in Wildlife Restoration act under Pittman-Robertson project, the State University of New York, College of Environmental Science and Forestry, the Ministry of Education, Youth and Sport of the Czech Republic, the Ministry of Agriculture of the Czech Republic, Rufford Foundation, an American Society of Mammalogists African Graduate Student Research Fund, the German Science Foundation, the Israeli Science Foundation, the BSF-NSF, the Ministry of Agriculture, Forestry and Food and Slovenian Research Agency (CRP V1-1626), the Aage V. Jensen Naturfond (project: Kronvildt - viden, værdier og værktøjer), the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy, National Centre for Research and Development in Poland, the Slovenian Research Agency, the David Shepherd Wildlife Foundation, Disney Conservation Fund, Whitley Fund for Nature, Acton Family Giving, Zoo Basel, Columbus, Bioparc de Doué-la-Fontaine, Zoo Dresden, Zoo Idaho, Kolmården Zoo, Korkeasaari Zoo, La Passarelle, Zoo New England, Tierpark Berlin, Tulsa Zoo, the Ministry of Environment and Tourism, Government of Mongolia, the Mongolian Academy of Sciences, the Federal Aid in Wildlife Restoration act and the Illinois Department of Natural Resources, the National Science Foundation, Parks Canada, Natural Sciences and Engineering Research Council, Alberta Environment and Parks, Rocky Mountain Elk Foundation, Safari Club International and Alberta Conservation Association, the Consejo Nacional de Ciencias y Tecnología (CONACYT) of Paraguay, the Norwegian Environment Agency and the Swedish Environmental Protection Agency, EU funded Interreg SI-HR 410 Carnivora Dinarica project, Paklenica and Plitvice Lakes National Parks, UK Wolf Conservation Trust, EURONATUR and Bernd Thies Foundation, the Messerli Foundation in Switzerland and WWF Germany, the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie Actions, NASA Ecological Forecasting Program, the Ecotone Telemetry company, the French National Research Agency, LANDTHIRST, grant REPOS awarded by the i-Site MUSE thanks to the “Investissements d’avenir” program, the ANR Mov-It project, the USDA Hatch Act Formula Funding, the Fondation Segre and North American and European Zoos listed at http://www.giantanteater.org/, the Utah Division of Wildlife Resources, the Yellowstone Forever and the National Park Service, Missouri Department of Conservation, Federal Aid in Wildlife Restoration Grant, and State University of New York, various donors to the Botswana Predator Conservation Program, data from collared caribou in the Northwest Territories were made available through funds from the Department of Environment and Natural Resources, Government of the Northwest Territories. The European Research Council Horizon2020, the British Ecological Society, the Paul Jones Family Trust, and the Lord Kelvin Adam Smith fund, the Tanzania Wildlife Research Institute and Tanzania National Parks. The Eastern Shoshone and Northern Arapahoe Fish and Game Department and the Wyoming State Veterinary Laboratory, the Alaska Department of Fish and Game, Kodiak Brown Bear Trust, Rocky Mountain Elk Foundation, Koniag Native Corporation, Old Harbor Native Corporation, Afognak Native Corporation, Ouzinkie Native Corporation, Natives of Kodiak Native Corporation and the State University of New York, College of Environmental Science and Forestry, and the Slovenia Hunters Association and Slovenia Forest Service. F.C. was partly supported by the Resident Visiting Researcher Fellowship, IMéRA/Aix-Marseille Université, Marseille. This work was partially funded by the Center of Advanced Systems Understanding (CASUS), which is financed by Germany’s Federal Ministry of Education and Research (BMBF) and by the Saxon Ministry for Science, Culture and Tourism (SMWK) with tax funds on the basis of the budget approved by the Saxon State Parliament. This article is a contribution of the COVID-19 Bio-Logging Initiative, which is funded in part by the Gordon and Betty Moore Foundation (GBMF9881) and the National Geographic Society.https://www.science.org/journal/sciencehj2023Mammal Research InstituteZoology and Entomolog

Modeling habitat suitability for Grey Crowned-cranes (Balearica regulorum gibbericeps) throughout Uganda

Grey Crowned-cranes occur throughout the mixed wetland-grassland habitats of Eastern and Southern Africa. Due primarily to loss of habitat, however, the species is in swift decline over much of its historic range. We present a prediction of habitat suitability throughout Uganda using a Maxent modeling approach, combining presence-only field data collected over the last few decades (1970 -2006) with remote sensing and climate derived variables. We ran six feature type models, with the Auto feature type model having the best fit to the data (AUC = 0.912). Our results provide detailed information regarding the characteristics of habitats used and highlight specific areas of high habitat suitability for the species. While wetlands were certainly important in the prediction (9.2% contribution), other variables (namely temperature seasonality) were more important within the model (19.5%). Areas of high habitat suitability (defined as &gt; 0.6 probability of presence) accounted for only a small amount of the total area throughout the country (5.8 -6.9%), and were mainly found in the Southwestern corner of the country and along the Albert Nile River. These data provide a statistical basis for extrapolating into areas that have not been surveyed and provide valuable information for the future conservation of the species