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.

Animal movement behaviours are shaped by diverse factors, including resource availability and human impacts on the landscape. We generated home range estimates and daily movement rate estimates for 148 giraffe ( Giraffa spp . ) from all four species across Africa to evaluate the effects of environmental productivity and anthropogenic disturbance on space use. Using the continuous time movement modelling framework and a novel application of mixed effects meta-regression, we summarized overall giraffe space use and tested for the effects of resource availability and human impact on 95% autocorrelated kernel density estimate (AKDE) size and daily movement. The mean 95% AKDE was 356.4 km ² and the mean daily movement was 14.1 km, both with marginally significant differences across species. We found significant negative effects of resource availability, and significant positive effects of resource heterogeneity and protected area overlap on 95% AKDE size. There were significant negative effects of overall anthropogenic disturbance and positive effects of the heterogeneity of anthropogenic disturbance on daily movements and 95% AKDE size. Our results provide unique insights into the interactive effects of resource availability and anthropogenic development on the movements of a large-bodied browser and highlight the potential impacts of rapidly changing landscapes on animal space-use patterns.

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.