Combining species distribution models and moderate resolution satellite information to guide conservation programs for reticulated giraffe

Abstract

The conservation of threatened and rare species in remote areas often presents two challenges: there may be unknown populations that have not yet been documented and there is a need to identify suitable habitat to translocate individuals and help populations recover. This is the case of the reticulated giraffe (Giraffa reticulata), a species of high conservation priority for which: (a) there may be unknown populations in remote areas, and (b) detailed maps of suitable habitat available within its range are lacking. We implemented a species distribution modeling (SDM) workflow in Google Earth Engine, combining GPS telemetry data of 31 reticulated giraffe with Landsat 8 OLI, Advanced Land Observing Satellite Phased Arrayed L‐band Synthetic Aperture Radar, and surface ruggedness layers to predict suitable habitat at 30‐m spatial resolution across the potential range of the species. Models had high predictive power, with a mean AUC‐PR of 0.88 (SD: 0.02; range: 0.86–0.91), mean sensitivity of 0.85 (SD: 0.04; range: 0.80–0.91), and mean precision was 0.81 (SD: 0.02; range: 0.79–0.83). Model predictions were also consistent with two independent validation datasets, with higher predicted suitable habitat values at known occurrence locations than at a random set of locations (P < 0.01). Our model predicted a total of 5519 km2 of potentially suitable habitat in Kenya, 963 km2 in Ethiopia, and 147 km2 in Somalia. Our results indicate that is possible to combine moderate spatial resolution imagery with telemetry data to guide conservation programs of threatened terrestrial species. We provide a free web app where managers can visualize and interact with the 30 m resolution map to help guide future surveys to search for existing populations and to inform future reintroduction assessments. We present all analysis code as a framework that could be adapted for other species across the globe.

Land-cover change in Cuba and implications for the area of distribution of a specialist’s host-plant

“…GEE, is a cloud geospatial processing platform on Google computational infrastructure, used in different studies with spatial and temporal scales ( Gorelick et al, 2017 ). This platform has been used in studies exploring vegetation succession ( Adagbasa & Mukwada, 2022 ), species distribution ( Crego, Stabach & Connette, 2022 ; Crego et al, 2023 ), and to characterize large landscapes ( Rippel et al, 2023 ). GEE has been also used to map deforestation and forest degradation ( Shimizu et al, 2022 ; Wimberly et al, 2022 ), predict effects of climate change ( Workie & Debella, 2018 ; Shiff, Lensky & Bonfil, 2021 ), assess the impacts of wildfires ( dos Santos et al, 2023 ; Parra et al, 2023 ), and detect changes in land use and land cover ( Phan, Kuch & Lehnert, 2020 ; Tassi et al, 2021 ; González-González, Clerici & Quesada, 2022 ; Biswas et al, 2023 ).…”

Human footprint and rainfall shape Masai giraffe's habitat suitability and connectivity in a multiple‐use landscape

“…We included elevation and surface roughness as terrain characteristics that are important drivers of habitat selection for large herbivores (Killeen et al, 2014), specifically for giraffes (Crego et al, 2023;Kimuyu et al, 2021). We obtained elevation data from the Shuttle Radar Topography Mission (SRTM) at 30 m resolution (Hennig et al, 2001).…”

“…To standardize predictor resolutions (Deneu et al, 2022), all seven predictors were resampled to the 250 m pixel resolution using the default near neighbor method in GEE (see Appendix S1: Figures S1-S6). We checked that predictors were not highly correlated using Pearson test of correlation (r > 0.7); however, we retained the HH and HV bands although they were highly correlated (r = 0.9) as they provide different polarization bands (Santoro et al, 2009;Shimada et al, 2014;Yu & Saatchi, 2016) to the model prediction and have been observed to have varying influence on model predictions in other giraffe species (Crego et al, 2023). All spatial data processing was done in GEE, following the framework of Crego et al (2022).…”

“…Although habitat suitability does not necessarily account for habitat utilization (Scharf et al, 2018)-nor the presence of other biotic factors such as predation, competition, and poaching-our analysis, shows that suitability can be used as a proxy to determine current and potential habitat use by giraffes. As the survey was conducted during the dry season when it is perceived giraffes move wide and long distances due to spatial temporal variation in resource availability (Bond et al, 2023;Brown et al, 2023;Crego et al, 2023;Mcqualter et al, 2016), it does not account for temporal habitat variations. Thus, our results show that habitat suitability is higher in the protected areas and in community ranches that enable movement between protected areas, indicating dry season utilization of the conservancies and ranches in the southern part of Tsavo West NP and neighboring counties when resources vary spatially and temporarily.…”

“…Identifying suitable habitats and connecting linkage zones can be used in planning wildlife habitats (Riggio et al, 2022), especially in human-dominated landscapes such as Tsavo-Mkomazi. Presence data obtained from ground surveys, aerial surveys, and remotely sensed data have been used to model habitat suitability and connectivity (Crego et al, 2023).…”