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

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

Genomic analysis reveals limited hybridization among three giraffe species in Kenya

Background: In the speciation continuum, the strength of reproductive isolation varies, and species boundaries are blurred by gene flow. Interbreeding among giraffe (Giraffa spp.) in captivity is known, and anecdotal reports of natural hybrids exist. In Kenya, Nubian (G. camelopardalis camelopardalis), reticulated (G. reticulata), and Masai giraffe sensu stricto (G. tippelskirchi tippelskirchi) are parapatric, and thus, the country might be a melting pot for these taxa. We analyzed 128 genomes of wild giraffe, 113 newly sequenced, representing these three taxa. Results: We found varying levels of Nubian ancestry in 13 reticulated giraffe sampled across the Laikipia Plateau most likely reflecting historical gene flow between these two lineages. Although comparatively weaker signs of ancestral gene flow and potential mitochondrial introgression from reticulated into Masai giraffe were also detected, estimated admixture levels between these two lineages are minimal. Importantly, contemporary gene flow between East African giraffe lineages was not statistically significant. Effective population sizes have declined since the Late Pleistocene, more severely for Nubian and reticulated giraffe. Conclusions: Despite historically hybridizing, these three giraffe lineages have maintained their overall genomic integrity suggesting effective reproductive isolation, consistent with the previous classification of giraffe into four species

Data from: Genomic analysis reveals limited hybridization among three giraffe species in Kenya

The data deposited here was generated by and reported in Coimbra et al. (2023). SNP calling and linkage pruning snp_calling_per_species.tar.gz: includes a genotype likelihoods (GL) file estimated with ANGSD for each giraffe species. sampled_ld.tar.gz: contains a random sample of estimated pairwise r2 values for each species used to fit linkage disequilibrium (LD) decay curves. ld_pruned_snps.tar.gz: contains an LD-pruned ANGSD GL file per species. snp_calling_combined.tar.gz: includes a single LD-pruned ANGSD GL file comprising all sampled individuals of the three giraffe species analyzed in this study. Relatedness relatedness.tar.gz: contains the input and output files used with NGSremix to estimate relatedness among giraffe in the dataset. snp_calling_combined_unrelated.tar.gz: includes a single LD-pruned ANGSD GL file comprising all unrelated individuals of the three giraffe species analyzed in this study. Population structure and admixture pcangsd.tar.gz: contains the covariance matrix generated by PCAngsd. ngsadmix.tar.gz: includes run likelihood lists for each K value ranging from 1 to 11, as well as the admixture proportions (stored in '.qopt' files) inferred from the run with the highest log-likelihood for each K in NGSadmix. evaladmix.tar.gz: contains the pairwise correlation of residuals between individuals estimated with evalAdmix for the NGSadmix runs with the highest log-likelihood run for each K. SNP-based phylogenomic inference snp_phylogeny.tar.gz: contains the input PHYLIP file and the IQ-TREE output tree and log files. Phylogeny of mitochondrial genomes mtdna_phylogeny.tar.gz: includes the 13 mitochondrial protein-coding gene alignments, the partitions file, and the IQ-TREE output tree and log files. Inference of migration events admixture_graphs.tar.gz: contains the TreeMix / OrientAGraph input file ('treemix.frq.strat.gz'), the output files for all TreeMix and OrientAGraph runs, and the OptM summary table of TreeMix runs ('optm.tsv'). Test for introgression dsuite_introgression.tar.gz: includes the input VCF, the admixture graph topology reconstructed by OrientAGraph, and the Dsuite output files for the estimation of Patterson's D, f4-ratio, and f-branch statistics. Contemporary migration rates ba3-snps.tar.gz: contains the input and output files for the BA3-SNPs-autotune and BA3-SNPs runs. Demographic reconstruction demographic_inference.tar.gz: includes the SFS files generated with ANGSD and realSFS and the StairwayPlot2 blueprint and output files. Other: metadata.csv: a companion file containing sample information used in conjunction with R scripts to plot the figures in the paper

Genomic analysis reveals limited hybridization among three giraffe species in Kenya

Background: In the speciation continuum the strength of reproductive isolation varies, and species boundaries are blurred by gene flow. Interbreeding among giraffe (Giraffa spp.) in captivity is known and anecdotal reports of natural hybrids exist. In Kenya, Nubian (G. camelopardalis camelopardalis), reticulated (G. reticulata), and Masai giraffe sensu stricto (G. tippelskirchi tippelskirchi) are parapatric, and thus the country might be a melting pot for these taxa. We analyzed 128 genomes of wild giraffe, 113 newly sequenced, representing these three taxa. Results: We found varying levels of Nubian ancestry in 13 reticulated giraffe sampled across the Laikipia Plateau most likely reflecting historical gene flow between these two lineages. Although comparatively weaker signs of ancestral gene flow and potential mitochondrial introgression from reticulated into Masai giraffe were also detected, estimated admixture levels between these two lineages are minimal. Importantly, contemporary gene flow between East African giraffe lineages was not statistically significant. Effective population sizes have declined since the Late Pleistocene, more severely for Nubian and reticulated giraffe. Conclusions: Despite historically hybridizing, these three giraffe lineages have maintained their overall genomic integrity suggesting effective reproductive isolation, consistent with the previous classification of giraffe into four species

Moving through the mosaic: Identifying critical linkage zones for large herbivores across a multiple‐use African landscape

Context: Reduced connectivity across grassland ecosystems can impair their functional heterogeneity and negatively impact large herbivore populations. Maintaining landscape connectivity across human-dominated rangelands is therefore a key conservation priority. Objective: Integrate data on large herbivore occurrence and species richness with analyses of functional landscape connectivity to identify important areas for maintaining or restoring connectivity for large herbivores. Methods: The study was conducted on a landscape with a mosaic of multiple land uses in Laikipia County, Kenya. We used occupancy estimates for four herbivore species [African elephant (Loxodonta africana), reticulated giraffe (Giraffa reticulata), plains zebra (Equus quagga), and Grevy’s zebra (Equus grevyi)] and species richness estimates derived from aerial surveys to create resistance surfaces to movement for single species and a multi-species assemblage, respectively. We validated single-species resistance surfaces using telemetry data. We used circuit theory and least cost-path analyses to model linkage zones across the landscape and prioritize areas for connectivity restoration. Results: Resistance layers approximated the movements of our focal species. Results for single-species and multi-species connectivity models were highly correlated (rp > 0.9), indicating similar spatial patterns of functional connectivity between individual species and the larger herbivore assemblage. We identified critical linkage zones that may improve permeability to large-herbivore movements. Conclusion: Our analysis highlights the utility of aerial surveys in modeling landscape connectivity and informing conservation management when animal movement data are scarce. Our results can guide management decisions, providing valuable information to evaluate the trade-offs between improving landscape connectivity and safeguarding livelihoods with electrified fences across rangelands

Updated geographic range maps for giraffe, Giraffa spp., throughout sub‐Saharan Africa, and implications of changing distributions for conservation

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

Camera settings and biome influence the accuracy of citizen science approaches to camera trap image classification

Scientists are increasingly using volunteer efforts of citizen scientists to classify images captured by motion-activated trail cameras. The rising popularity of citizen science reflects its potential to engage the public in conservation science and accelerate processing of the large volume of images generated by trail cameras. While image classification accuracy by citizen scientists can vary across species, the influence of other factors on accuracy is poorly understood. Inaccuracy diminishes the value of citizen science derived data and prompts the need for specific best-practice protocols to decrease error. We compare the accuracy between three programs that use crowdsourced citizen scientists to process images online: Snapshot Serengeti, Wildwatch Kenya, and AmazonCam Tambopata. We hypothesized that habitat type and camera settings would influence accuracy. To evaluate these factors, each photograph was circulated to multiple volunteers. All volunteer classifications were aggregated to a single best answer for each photograph using a plurality algorithm. Subsequently, a subset of these images underwent expert review and were compared to the citizen scientist results. Classification errors were categorized by the nature of the error (e.g., false species or false empty), and reason for the false classification (e.g., misidentification). Our results show that Snapshot Serengeti had the highest accuracy (97.9%), followed by AmazonCam Tambopata (93.5%), then Wildwatch Kenya (83.4%). Error type was influenced by habitat, with false empty images more prevalent in open-grassy habitat (27%) compared to woodlands (10%). For medium to large animal surveys across all habitat types, our results suggest that to significantly improve accuracy in crowdsourced projects, researchers should use a trail camera set up protocol with a burst of three consecutive photographs, a short field of view, and determine camera sensitivity settings based on in situ testing. Accuracy level comparisons such as this study can improve reliability of future citizen science projects, and subsequently encourage the increased use of such data

Camera settings and biome influence the accuracy of citizen science approaches to camera trap image classification.

Scientists are increasingly using volunteer efforts of citizen scientists to classify images captured by motion-activated trail cameras. The rising popularity of citizen science reflects its potential to engage the public in conservation science and accelerate processing of the large volume of images generated by trail cameras. While image classification accuracy by citizen scientists can vary across species, the influence of other factors on accuracy is poorly understood. Inaccuracy diminishes the value of citizen science derived data and prompts the need for specific best-practice protocols to decrease error. We compare the accuracy between three programs that use crowdsourced citizen scientists to process images online: Snapshot Serengeti, Wildwatch Kenya, and AmazonCam Tambopata. We hypothesized that habitat type and camera settings would influence accuracy. To evaluate these factors, each photograph was circulated to multiple volunteers. All volunteer classifications were aggregated to a single best answer for each photograph using a plurality algorithm. Subsequently, a subset of these images underwent expert review and were compared to the citizen scientist results. Classification errors were categorized by the nature of the error (e.g., false species or false empty), and reason for the false classification (e.g., misidentification). Our results show that Snapshot Serengeti had the highest accuracy (97.9%), followed by AmazonCam Tambopata (93.5%), then Wildwatch Kenya (83.4%). Error type was influenced by habitat, with false empty images more prevalent in open-grassy habitat (27%) compared to woodlands (10%). For medium to large animal surveys across all habitat types, our results suggest that to significantly improve accuracy in crowdsourced projects, researchers should use a trail camera set up protocol with a burst of three consecutive photographs, a short field of view, and determine camera sensitivity settings based on in situ testing. Accuracy level comparisons such as this study can improve reliability of future citizen science projects, and subsequently encourage the increased use of such data

Genomic analysis reveals limited hybridization among three giraffe species in Kenya

In the speciation continuum, the strength of reproductive isolation varies, and species boundaries are blurred by gene flow. Interbreeding among giraffe (Giraffa spp.) in captivity is known, and anecdotal reports of natural hybrids exist. In Kenya, Nubian (G. camelopardalis camelopardalis), reticulated (G. reticulata), and Masai giraffe sensu stricto (G. tippelskirchi tippelskirchi) are parapatric, and thus, the country might be a melting pot for these taxa. We analyzed 128 genomes of wild giraffe, 113 newly sequenced, representing these three taxa.We found varying levels of Nubian ancestry in 13 reticulated giraffe sampled across the Laikipia Plateau most likely reflecting historical gene flow between these two lineages. Although comparatively weaker signs of ancestral gene flow and potential mitochondrial introgression from reticulated into Masai giraffe were also detected, estimated admixture levels between these two lineages are minimal. Importantly, contemporary gene flow between East African giraffe lineages was not statistically significant. Effective population sizes have declined since the Late Pleistocene, more severely for Nubian and reticulated giraffe.Despite historically hybridizing, these three giraffe lineages have maintained their overall genomic integrity suggesting effective reproductive isolation, consistent with the previous classification of giraffe into four species