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Fig. 1 in A Pasture Of Big Ungulate Animals As Key Ecological Factor Influencing On The Fluctuation Of Natural Habitat Of Steppe Herbivorous Mammals
Fig. 1. The steppe marmot quantity dynamics in the 20th century (cattle against the steppe marmot) of the Chertkovskiy Region of Rostov.
Data from: A coherent biogeographic framework for Old World Neogene and Pleistocene mammals
<p>In order to understand mammalian evolution and compute a wide range of biodiversity indices, we commonly use spatial division adapted to ecological and evolutionary constraints called bioregion. While commonly conducted by neontologists, the establishment of bioregions in palaeontology is generally a secondary analysis, shaped on subjective time scales and areas specific to the investigated questions and groups. This heterogeneity, coupled with the scale-dependency of biodiversity indices, prevents the clear identification of macroecological and macroevolutionary trends for large taxonomic groups like extinct mammals. Here we tackle this issue by providing a coherent framework for Neogene and Pleistocene mammals of the Old World following two steps: (A) a temporal scale adapted to mammalian evolutionary history (i.e. evolutionary fauna) is defined by poly-cohort analysis, and (B) bioregions are then computed for each evolutionary fauna by clustering, ordination and intermediate approaches at multiples spatial scales (i.e. continental to regional) for Eurasia and Africa. Additionally, providing a coherent framework for a wide range of mammalian datasets, our results show: (1) the synchronous emergence and fall of five mammalian evolutionary faunas identified at chronological scales varying from the epoch to the geological stage; (2) a transition from a longitudinal to a latitudinal biogeographical structuring between the Miocene and Pliocene, especially in Europe; (3) the long-term affinity of southern Asian with African faunas, in sharp contrast with the modern Palearctic bioregion extension; and (4) the establishment of a vast Mediterranean bioregion from fragmented areas in late Miocene to its full extent in the Pleistocene.</p>
Extended Data Figure 7 in Host and viral traits predict zoonotic spillover from mammals
Extended Data Figure 7 | Global distribution of viral and host species richness for primates (order Primates). a, Observed total viral richness (for n = 71 host spp.); b, predicted total viral richness given maximum research effort; c, missing viruses or predicted minus observed total viral richness; d, observed zoonotic viral richness (n = 73);e, predicted zoonotic viral richness given maximum research effort; f, missing zoonoses or predicted minus observed zoonotic viral richness (same as included in Fig. 3e); g, global host species richness for Primates (n = 400); h, host species richness for Primates in our database (n = 98);i, primate species with no described viruses in the literature. Warmer colours (larger values) in c and f highlight areas predicted to be of greatest value for discovering novel viruses or novel viral zoonoses, respectively, in primates. Red/pink colours in panel i highlight areas with poor viral surveillance in primate species to date. Hatched regions represent areas where model predictions deviate systematically for the collection of species in that grid cell (see Methods).
Extended Data Figure 4 in Host and viral traits predict zoonotic spillover from mammals
Extended Data Figure 4 | Global distribution of viral and host species richness for wild carnivores (order Carnivora). a, Observed total viral richness (for n = 55 host spp.); b, predicted total viral richness given maximum research effort; c, missing viruses or predicted minus observed total viral richness; d, observed zoonotic viral richness (n = 55); e, predicted zoonotic viral richness given maximum research effort; f, missing zoonoses or predicted minus observed zoonotic viral richness (same as included in Fig. 3b); g, global host species richness for Carnivora (n = 276);h, host species richness for Carnivora in our database (n = 79); i, species of the order Carnivora with no described viruses in the literature. Warmer colours (larger values) in c and f highlight areas predicted to be of greatest value for discovering novel viruses or novel viral zoonoses, respectively, in carnivores. Red/pink colours in panel i highlight areas with poor viral surveillance in carnivore species to date. Hatched regions represent areas where model predictions deviate systematically for the collection of species in that grid cell (see Methods).
Extended Data Figure 3 in Host and viral traits predict zoonotic spillover from mammals
Extended Data Figure 3 | Global distribution of viral and host species richness for all wild mammals. a, Observed total viral richness (for n = 576 host spp.); b, predicted total viral richness given maximum research effort; c, missing viruses or predicted minus observed total viral richness; d, observed zoonotic viral richness (n = 584);e, predicted zoonotic viral richness given maximum research effort; f, missing zoonoses or predicted minus observed zoonotic viral richness (same as included in Fig. 3a); g, global mammal species richness (n = 5,290); h, mammal richness for species in our database (n = 753);i, mammal species with no described viruses in the literature. Warmer colours (larger values) in panels c and f highlight areas predicted to be of greatest value for discovering novel viruses or novel viral zoonoses, respectively, in mammals. Red/pink colours in panel i highlight areas with poor viral surveillance in mammal species to date. Hatched regions represent areas where model predictions deviate systematically for the collection of species in that grid cell (see Methods).
Figure 4 in Host and viral traits predict zoonotic spillover from mammals
Figure 4 | Traits that predict zoonotic potential of a virus. a, Box plot of maximum phylogenetic host breadth per virus (PHB, see methods) for each of 586 mammalian viruses, aggregated by 28 viral families. Individual points represent viral species, colour-coded by zoonotic status. Box plots coloured and sorted by the proportion of zoonoses in each viral family. b–d, Partial effect plots for the best-fit GAM to predict the zoonotic potential of a virus. b, Maximum PHB. Viruses that infect a phylogenetically broader range of hosts are more likely to be zoonotic. c, Research effort (log, number of PubMed citations per viral species). d, Whether or not a virus replicates in the cytoplasm or is vector-borne. Viral genome length and whether or not a virus is enveloped improved the overall predictive power but were non-significant and are not shown (see Extended Data Table 1).
Extended Data Figure 2 in Host and viral traits predict zoonotic spillover from mammals
Extended Data Figure 2 | Heat map of observed total viral richness by mammalian order and viral family. Dataset includes 754 mammalian species and 586 unique ICTV recognized viral species. Heat map aggregated by rows and columns to group taxa with similar levels of observed viral richness.
Figure 3 in Host and viral traits predict zoonotic spillover from mammals
Figure 3 | Global distribution of the predicted number of 'missing zoonoses' by order. Warmer colours highlight areas predicted to be of greatest value for discovering novel zoonotic viruses. a, All wild mammals (n = 584 spp. included in the best-fit model). b, Carnivores (order Carnivora, n = 55).c, Even-toed ungulates (order Cetartiodactyla, n = 70). d, Bats (order Chiroptera, n = 157).e, Primates (order Primates, n = 73). f, Rodents (order Rodentia, n = 183). Hatched regions represent areas where model predictions deviate systematically for the assemblage of species in that grid cell (approximately 18 km × 18 km, see Methods). Animal silhouettes from PhyloPic.
Extended Data Figure 6 in Host and viral traits predict zoonotic spillover from mammals
Extended Data Figure 6 | Global distribution of viral and host species richness for bats (order Chiroptera). a, Observed total viral richness (for n = 156 host spp.); b, predicted total viral richness given maximum research effort; c, missing viruses or predicted minus observed total viral richness; d, observed zoonotic viral richness (n = 157);e, predicted zoonotic viral richness given maximum research effort; f, missing zoonoses or predicted minus observed zoonotic viral richness (same as included in Fig. 3d); g, global host species richness for Chiroptera (n = 1117);h, host species richness for Chiroptera in our database (n = 192);i, species of the order Chiroptera with no described viruses in the literature. Warmer colours (larger values) in c and f highlight areas predicted to be of greatest value for discovering novel viruses or novel viral zoonoses, respectively, in bats. Red/pink colours in panel i highlight areas with poor viral surveillance in bat species to date. Hatched regions represent areas where model predictions deviate systematically for the collection of species in that grid cell (see Methods).
Extended Data Figure 1 in Host and viral traits predict zoonotic spillover from mammals
Extended Data Figure 1 | Conceptual model of zoonotic spillover, viral richness, and summary of models. a, Conceptual model of zoonotic spillover showing primary risk factors examined, colour-coded according to generalized additive models used. b, Conceptual model of observed, predicted, and actual viral richness in mammals. c, GAMs used in our study to address specific components of a and b, colour-coded by model. Variables listed with 'or' under each GAM covaried and were provided as competing terms in model selection, and those in bold were included in the best-fit model using all host–virus associations. Significant variables from each best-fit GAM are noted with an asterisk. Zoonotic viral spillover first depends on the underlying total viral richness in mammal populations and the ecological, taxonomic, and life-history traits that govern this diversity (GAM 1). Second, host- and virus-specific factors may facilitate viral spillover. We examine the relative importance of host phylogenetic distance to humans, ecological opportunity for contact, or other species-specific life-history and taxonomic traits (GAM 2), and identify viral traits associated with a higher likelihood of an observed virus being zoonotic (GAM 3). We estimate the total and zoonotic viral richness per host species using GAMs 1 and 2, and calculate the missing viruses and missing zoonoses under a scenario of increased research effort (b, Methods). Owing to imperfect surveillance in both humans and wildlife and biases in viral detection, there may be uncertainty in the exact proportion of viruses that are zoonotic (b, light grey), and also between the actual, or true, viral richness (dotted lines) and the predicted maximum viral richness per host (dashed line).
Extended Data Figure 9 in Host and viral traits predict zoonotic spillover from mammals
Extended Data Figure 9 | Order-level phylogenies showing residuals from zoonoses model. a–e, Subtrees from cytochrome b maximum likelihood phylogeny for 558 mammal species (constrained to order-level topology of mammal supertree) for bats (a), carnivores (b), even-toed ungulates (c), rodents (d) and primates (e). Species included have at least one described virus association and available genetic data. Wildlife species names and terminal branches are colour-coded by the residuals (predicted minus observed) from the best-fit GAM to predict the number of zoonotic viruses using all data. Species with residual values between −1 and 1 (black) are accurately predicted within one virus. Warm colours represent species with positive residuals (orange>1 to 3; red>3). Cool colours represent species with negative residuals (green <−1 to−3; blue<−3). Marine mammals, domestic animals, and species with missing data and not included in the best-fit models are shown in grey.
Figure 2 in Host and viral traits predict zoonotic spillover from mammals
Figure 2 | Host traits that predict total viral richness (top row) and proportion of zoonotic viruses (bottom row) per wild mammal species. Partial effect plots show the relative effect of each variable included in the best-fit GAM, given the effect of the other variables. Shaded circles represent partial residuals; shaded areas, 95% confidence intervals around mean partial effect. a–e, Best model for total viral richness includes: a, number of disease-related citations per host species (research effort, log); b, phylogenetic eigenvector regression (PVR) of body mass (log); c, geographic range area of each species (log km2); d, number of sympatric mammal species overlapping with at least 20% area of target species range; and e, mammalian orders. f–i, Best model for proportion of zoonoses includes: f, research effort (log); g, phylogenetic distance from humans (cytochrome b tree constrained to the topology of the mammal supertree28); h, ratio of urban to rural human population within species range; and i, three mammalian orders. Bats are the only order with a significantly larger proportion of zoonotic viruses than would be predicted by the other variables in the all-data model. Three additional mammalian orders, and whether or not a species is hunted, improved the overall predictive power of the best zoonotic virus model but were non-significant and are not shown (see Extended Data Table 1).
Extended Data Figure 8 in Host and viral traits predict zoonotic spillover from mammals
Extended Data Figure 8 | Global distribution of viral and host species richness for rodents (order Rodentia). a, Observed total viral richness (for n = 178 host spp.); b, predicted total viral richness given maximum research effort; c, missing viruses or predicted minus observed total viral richness; d, observed zoonotic viral richness (n = 183);e, predicted zoonotic viral richness given maximum research effort; f, missing zoonoses or predicted minus observed zoonotic viral richness (same as included in Fig. 3f); g, global host species richness for Rodentia (n = 2206);h, host species richness for Rodentia in our database (n = 221); i, rodent species with no described viruses in the literature. Warmer colours (larger values) in c and f highlight areas predicted to be of greatest value for discovering novel viruses or novel viral zoonoses, respectively, in wild rodents. Red/pink colours in panel i highlight areas with poor viral surveillance in rodent species to date. Hatched regions represent areas where model predictions deviate systematically for the collection of species in that grid cell (see Methods).
Extended Data Figure 5 in Host and viral traits predict zoonotic spillover from mammals
Extended Data Figure 5 | Global distribution of viral and host species richness for wild even-toed ungulates (order Cetartiodactyla). a, Observed total viral richness (for n = 70 host spp.); b, predicted total viral richness given maximum research effort; c, missing viruses or predicted minus observed total viral richness; d, observed zoonotic viral richness (n = 70);e, predicted zoonotic viral richness given maximum research effort; f, missing zoonoses or predicted minus observed zoonotic viral richness (same as included in Fig. 3c); g, global host species richness for Cetartiodactyla (n = 229);h, host species richness for Cetartiodactyla in our database (n = 105);i, species of the order Cetartiodactyla with no described viruses in the literature. Warmer colours (larger values) in c and f highlight areas predicted to be of greatest value for discovering novel viruses or novel viral zoonoses, respectively, in even-toed ungulates. Red/pink colours in panel i highlight areas with poor viral surveillance in even-toed ungulates species to date. Hatched regions represent areas where model predictions deviate systematically for the collection of species in that grid cell (see Methods).
Climatic refugia and reduced extinction correlate with underdispersion in mammals and birds in Africa
Macroevolutionary patterns, often inferred from metrics of community relatedness, are often used to ascertain major evolutionary processes shaping communities. These patterns have been shown to be informative of biogeographic barriers, of habitat suitability and invasibility (especially with regards to environmental filtering), and of regions that function as evolutionary cradles (i.e., sources of diversification) or museums (i.e., regions of reduced extinction). Here, we analysed continental datasets of mammal and bird distributions to identify primary drivers of community evolution on the African continent for mostly-endothermic vertebrates. We find that underdispersion (i.e., relatively low phylogenetic diversity compared to species richness) closely correlates with specific ecoregions that have been identified as climatic refugia in the literature, regardless of whether these specific regions have been touted as cradles or museums. Using theoretical models of identical communities that differ only with respect to extinction rates, we find that even small suppressions of extinction rates can result in underdispersed communities, supporting the hypothesis that climatic stability can lead to underdispersion. We posit that large-scale patterns of under- and overdispersion between regions of similar species richness are more reflective of a particular region's extinction potential, and that the very nature of refugia can lead to underdispersion via the steady accumulation of species richness through diversification within the same ecoregion during climatic cycles. Thus, patterns of environmental filtering can be obfuscated by environments that coincide with biogeographic refugia, and considerations of regional biogeographic history are paramount for inferring macroevolutionary processes. --
Data and code for Winter conditions structure extratropical patterns of species richness of amphibians, birds, and mammals globally
<p>This repository contains the dataset analyzed in 'Winter conditions structure extratropical patterns of species richness of amphibians, birds, and mammals globally' - published in the journal Global Ecology and Biogeography - and the R code used to generate the correlations, generalized additive models, and related figures presented in the manuscript. Column descriptions for the data can be found in the associated README.txt file. Please refer to the manuscript for further detail on the variables and how they were derived.</p> <p>The Winter Indices (WIs) were derived using satellite remote sensing data from optical (MODIS, snow cover) and microwave (MEaSUREs freeze/thaw, frozen ground) sensors. The species richness maps were derived using IUCN range maps for individual species of amphibians, birds, and mammals (data requests can be made here: <a href="https://www.iucnredlist.org/resources/spatial-data-download">https://www.iucnredlist.org/resources/spatial-data-download</a>). Climatic varibales were derived from WorldClim v2.0 data, elevation from USGS GMTED2010 data, and primary productivity from the cumulative dynamic habitat index available here: <a href="http://silvis.forest.wisc.edu/maps-data/">http://silvis.forest.wisc.edu/maps-data/</a>.</p>
Data and analysis code for: Global protected areas seem insufficient to safeguard half of the world's mammals from human-induced extinction
<div> <p class="normal">Protected areas (PAs) are a cornerstone of global conservation and central to international plans to minimize global extinctions. During the coming century, global ecosystem destruction and fragmentation associated with increased human population and economic activity could make the <span class="PI"></span>long-term<span class="PI"></span> survival of most terrestrial vertebrates even more dependent on PAs. However, the capacity of the current global PA network to sustain species for the long term is unknown. Here, we explore this question for all <span class="PI"></span>nonvolant terrestrial mammals<span class="ins cts-1"> for which we found sufficient data</span>, ∼4,000 species. We first estimate the potential population size of each such mammal species in each PA and then use three different criteria to estimate if solely the current global network of PAs might be sufficient for their <span class="PI"></span>long-term<span class="PI"></span> survival. Our analyses suggest that current PAs may fail to provide robust protection for about half the species analyzed, including most species currently listed as threatened with extinction and a third of species not currently listed as threatened. Hundreds of mammal species appear to have no viable protected populations. Underprotected species were found across all body sizes, taxonomic groups, and geographic regions. <span class="PI"></span>Large-bodied<span class="PI"></span> mammals, endemic species, and those in <span class="PI"></span>high-biodiversity<span class="PI"></span> tropical regions were particularly poorly protected by existing PAs. As<span class="ins cts-1"> new</span> international biodiversity targets are formulated, our results suggest that the global network of PAs must be <span class="PI"></span>greatly expanded and most importantly that PAs must be located in diverse regions that encompass species not currently protected and must be large enough to ensure that protected species can persist for the long term.</p> </div> <p class="kwd-group"></p>
Data from: Human impacts on mammals in and around a protected area before, during, and after COVID‐19 lockdowns
<p>The dual-mandate for many protected areas (PAs) to simultaneously promote recreation and conserve biodiversity may be hampered by negative effects of recreation on wildlife. However, reports of these effects are not consistent, presenting a knowledge gap that hinders evidence-based decision-making. We used camera traps to monitor human activity and terrestrial mammals in Golden Ears Provincial Park and the adjacent Malcolm Knapp Research Forest near Vancouver, Canada, with the objective of discerning relative effects of various forms of recreation on cougars (Puma concolor), black bears (Ursus americanus), black-tailed deer (Odocoileus hemionus), snowshoe hares (Lepus americanus), coyotes (Canis latrans), and bobcats (Lynx rufus). Additionally, public closures of the study area associated with the COVD-19 pandemic offered an unprecedented period of human-exclusion through which to explore these effects. Using Bayesian generalized mixed-effects models, we detected negative effects of hikers (mean posterior estimate = -0.58, 95% credible interval (CI) -1.09 to -0.12) on weekly bobcat habitat use and negative effects of motorized vehicles (estimate = -0.28, 95% CI -0.61 to -0.05) on weekly black bear habitat use. We also found increased cougar detection rates in the PA during the COVID-19 closure (estimate = 0.007, 95% CI 0.005 to 0.009), but decreased cougar detection rates (estimate = -0.006, 95% CI -0.009 to -0.003) and increased black-tailed deer detection rates (estimate = 0.014, 95% CI 0.002 to 0.026) upon reopening of the PA. Our results emphasize that effects of human activity on wildlife habitat use and movement may be species- and/or activity-dependent, and that camera traps can be an invaluable tool for monitoring both wildlife and human activity, collecting data even when public access is barred. Further, we encourage PA managers seeking to promote both biodiversity conservation and recreation to assess trade-offs between these two goals in their PAs.</p>
Small mammals reduce distance-dependence and increase seed predation risk in tropical rainforest fragments
Seed predation and reduced predation risk with distance from conspecific trees are important influences on tree regeneration in tropical forests. Shifts in animal communities, such as an increase in rodents and other small mammals due to forest fragmentation, could alter patterns of seed predation and affect tree regeneration and community dynamics in forest fragments. We performed a field experiment on four native rainforest tree species in the Western Ghats, India, to test whether fragmentation increases seed predation by mammals and alters the distance-dependence of seed predation. We monitored seed predation within open and mammal-exclosure plots, near and far from the canopies of conspecific trees, in contiguous and fragmented forests. Seed predation of Cullenia exarillata, Ormosia travancorica, and Syzygium rubicundum was markedly higher in forest fragments, and more so within open plots than exclosures, while the predominantly insect-predated Acronychia pedunculata experienced similar predation in contiguous forests and fragments. Seed predation of C. exarillata and S. rubicundum was unrelated to distance from conspecific trees in open plots in both contiguous forests and fragments, in contrast to exclosures that showed marked near versus far differences in seed predation. Our findings suggest that by increasing overall seed predation risk and imposing similar seed predation risk near and far from adults variably across the tree species, small mammals could alter processes that shape tree diversity and species composition in fragmented tropical rainforests.
Decomposing niche components reveals simultaneous effects of opposite deterministic processes structuring alpine small mammal assembly
<p><span>Our knowledge of community assembly dynamics under multiple stressors is limited because of opposite niche-based processes, i.e., limiting similarity and habitat filtering could simultaneously occur, masking the overall patterns. Alpine biomes provide the ideal systems to explore the influences of co-occurrence processes as these communities usually face multiple stresses such as resources limitation and habitat constraints. However, the assembly processes of mammals in alpine areas have hardly been exclusively studied. Here, we</span> <span>aimed to address how different processes structured small mammal communities at the tree line transition zone, which represents one of the most distinct vegetation boundaries separating alpine from montane habitats. We compiled a regional dataset including species list, phylogeny, and functional traits from field collections across 18 mountains of southwest China and complemented them with published data sources. The traits were decomposed into different niche components to determine the respective effects of specific stressors. Phylogenetic and functional diversity indices representing evolutionary history, trait space, and pairwise species distance were calculated and compared with null expectations. Linear mixed-effect models were constructed to assess the increasing or decreasing tendencies of diversity values against increasing elevation. The results showed that phylogenetic and functional richness were strongly correlated with species richness, unlike the distance-based indices which were uncorrelated with species richness. There was no evidence found to support non-random phylogenetic or overall trait patterns. However, the resource acquisition niche tended to be more overdispersed (positive slopes), while the habitat affinity niche tended to be more clustered (negative slopes) as habitats became less productive and less vegetated. We conclude that limiting similarity and habitat filtering simultaneously structure small mammal communities in alpine areas. Altogether, the present study provides vital insights into the complexity of co-occurring assembly processes by niche decomposition, and highlights the importance of considering different diversity dimensions when assessing community structure.</span></p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.