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.

New satellite remote sensing and machine learning techniques offer untapped possibilities to monitor global biodiversity 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 individuals 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.

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. 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. 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. 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. 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.

Aim: Google Earth Engine (GEE) is a free Web‐based spatial analysis platform that requires only a web browser and an Internet connection to programmatically access and analyse data from its multi‐petabyte catalog of regularly updated satellite imagery (e.g. MODIS, Landsat, Sentinel) and other geospatial datasets. The high computing capacity of GEE can make computationally demanding analyses more accessible to researchers and practitioners, especially those with limited access to advanced computational resources. Here, we present a workflow in GEE to fit species distribution models, offering direct access to a multi‐petabyte catalog of raster products to obtain estimates of habitat suitability.Innovation: We implemented a workflow for species distribution modelling in GEE that includes importing species occurrence data into the GEE platform, selecting and preparing predictor variables, and performing model fitting with spatial or temporal split‐block cross‐validation techniques. We present three case studies that demonstrate: (i) a baseline SDM workflow that produces informative model predictions, (ii) a workflow that accounts for temporal variability in predictor variables to study changes in habitat suitability over time and (iii) a complex and computationally demanding analysis incorporating thousands of satellite images for modelling habitat suitability at high spatial resolution.Main Conclusions: Our SDM workflow allows users to benefit from the high speed and performance of GEE without the need for significant computing infrastructure. This workflow may be especially beneficial to researchers in countries where computing power is limited, as SDMs frequently require the download, storage and processing of large raster datasets. We also discuss key limitations of implementing SDMs in GEE, such as user memory limits and the lack of high‐level functions. We include a step‐by‐step guide for the general model workflow and for each of the case studies presented to facilitate its implementation.

Giraffe populations have declined in abundance by almost 40% over the last three decades, and the geographic ranges of the species (previously believed to be one, now defined as four species) have been significantly reduced or altered. With substantial changes in land uses, loss of habitat, declining abundance, translocations, and data gaps, the existing geographic range maps for giraffe need to be updated. We performed a review of existing giraffe range data, including aerial and ground observations of giraffe, existing geographic range maps, and available literature. The information we collected was discussed with and validated by subject‐matter experts. Our updates may serve to correct inaccuracies or omissions in the baseline map, or may reflect actual changes in the distribution of giraffe. Relative to the 2016 International Union for Conservation of Nature Red List Assessment range map, the updated geographic range maps show a 5.6% decline in the range area of all giraffe taxa combined. The ranges of Giraffa camelopardalis (northern giraffe) and Giraffa tippelskirchi (Masai giraffe) decreased in area by 37% (122432 km²) and 4.7% (20816 km²) respectively, whereas 14% (41696 km²) of the range of Giraffa reticulata (reticulated giraffe) had not been included in the original geographic range map and has now been added. The range of Giraffa giraffa (southern giraffe) showed little overall change; it increased by 0.1% (419 km²). Ranges were larger than previously reported in six of the 21 range countries (Botswana, Ethiopia, Mozambique, South Sudan, Tanzania, and Zimbabwe), had declined in seven (Cameroon, Central African Republic, Chad, Malawi, Niger, Uganda, and Zambia) and remained unchanged in seven (Angola, Democratic Republic of Congo, eSwatini, Namibia, Rwanda, Somalia, and South Africa). In Kenya, the ranges of both Giraffa tippelskirchi and Giraffa camelopardalis decreased, but the range of Giraffa reticulata was larger than previously believed. Our updated range maps increase existing knowledge, and are important for conservation planning for giraffe. However, since rapid infrastructure development throughout much of Africa is a driver of giraffe population declines, there is an urgent need for a continent‐wide, consistent and systematic giraffe survey to produce more accurate range maps, in order to inform conservation and policy planning.

Resident white‐bearded wildebeest (Connochaetes taurinus) have experienced widespread population declines across much of their range over the past few decades, the drivers of which are attributed to landscape changes. Despite the ecological significance of this decline, surprisingly little is known about the resource needs and habitat use of these animals. Using global positioning system data collected from 2010 to 2013, we assessed resource selection of wildebeest inhabiting three study areas in Kenya with varying degrees of natural and anthropogenic disturbance to identify potential behavioral mechanisms underlying potential landscape‐driven declines. Wildebeest were observed to consistently avoid anthropogenic features and dense woody cover, irrespective of season, suggestive of avoidance of landscape features that would likely be associated with increased predation risk. Wildebeest also avoided primary roads, particularly across the Athi‐Kaputiei Plains where human density and landscape alteration was greatest. The strongest response to normalized difference vegetation index was observed across the Amboseli Basin, the least productive and anthropogenically altered of our three study areas, leading to pronounced seasonal shifts in space use. Selection of natural and anthropogenic features was similar across the Mara and Athi‐Kaputiei Plains, with the exception of the response to roads which likely relates to differences in road use. We also observed strong shifts in space use between day and night periods, particularly in relation to anthropogenic features and likely related to human circadian activity patterns. The observed variability in selection provides detailed information to how wildebeest react to local environmental factors across landscapes, and provides insight to how landscape fragmentation amplifies habitat loss for wildebeest by driving spatial avoidance, a likely mechanism contributing to population declines in this species. The quantified responses of wildebeest to landscape features can aid future conservation management efforts and planning to sustain imperiled wildebeest populations.