Summary1. Despite efforts in data collection, missing values are commonplace in life-history trait databases. Because these values typically are not missing randomly, the common practice of removing missing data not only reduces sample size, but also introduces bias that can lead to incorrect conclusions. Imputing missing values is a potential solution to this problem. Here, we evaluate the performance of four approaches for estimating missing values in trait databases (K-nearest neighbour (kNN), multivariate imputation by chained equations (mice), missForest and Phylopars), and test whether imputed datasets retain underlying allometric relationships among traits. 2. Starting with a nearly complete trait dataset on the mammalian order Carnivora (using four traits), we artificially removed values so that the percent of missing values ranged from 10% to 80%. Using the original values as a reference, we assessed imputation performance using normalized root mean squared error. We also evaluated whether including phylogenetic information improved imputation performance in kNN, mice, and missForest (it is a required input in Phylopars). Finally, we evaluated the extent to which the allometric relationship between two traits (body mass and longevity) was conserved for imputed datasets by looking at the difference (bias) between the slope of the original and the imputed datasets or datasets with missing values removed.3. Three of the tested approaches (mice, missForest and Phylopars), resulted in qualitatively equivalent imputation performance, and all had significantly lower errors than kNN. Adding phylogenetic information into the imputation algorithms improved estimation of missing values for all tested traits. The allometric relationship between body mass and longevity was conserved when up to 60% of data were missing, either with or without phylogenetic information, depending on the approach. This relationship was less biased in imputed datasets compared to datasets with missing values removed, especially when more than 30% of values were missing. 4. Imputations provide valuable alternatives to removing missing observations in trait databases as they produce low errors and retain relationships among traits. Although we must continue to prioritize data collection on species traits, imputations can provide a valuable solution for conducting macroecological and evolutionary studies using life-history trait databases.

As human population and resource demands continue to grow, biodiversity conservation has never been more critical. About one-quarter of all mammals are in danger of extinction, and more than half of all mammal populations are in decline. A major priority for conservation science is to understand the ecological traits that predict extinction risk and the interactions among those predictors that make certain species more vulnerable than others. Here, using a new database of nearly 4,500 mammal species, we use decisiontree models to quantify the multiple interacting factors associated with extinction risk. We show that the correlates of extinction risk vary widely across mammals and that there are unique pathways to extinction for species with different lifestyles and combinations of traits. We find that risk is relative and that all kinds of mammals, across all body sizes, can be at risk depending on their specific ecologies. Our results increase the understanding of extinction processes, generate simple rules of thumb that identify species at greatest risk, and highlight the potential of decision-tree analyses to inform conservation efforts.conservation ͉ biodiversity ͉ body size ͉ IUCN Red List ͉ decision tree C ertain ecological traits, such as small geographic range, low population density, slow life history, and large body size are known to correlate strongly with extinction risk in mammals, and the importance of these traits can vary among different clades of mammals (1-5). Large body size, in particular, is a well-known predictor of both past and present human-related extinctions (4, 6, 7). Although the identification of these correlates of extinction has been an important first step in guiding conservation priorities, it is critical to understand how multiple ecological factors interact to predict risk across species that differ by orders of magnitude in body size, area of geographic range, abundance, life history, niche characteristics, and other traits. For example, it is not enough to know that species with small geographic ranges tend to be at greater risk; rather, we need to know how range size interacts with other ecological traits to make certain species with small ranges more vulnerable than others. By understanding how multiple key ecological predictors interact, we are able to identify the species at greatest risk and also to understand what makes them vulnerable. Additionally, to help avert the losses of populations and species of mammals (8-10), there is a real need for conservation scientists to provide results that are directly relevant and are easily interpretable for conservation practice. In this paper, we draw on a large dataset and methodological approach to build on current knowledge of extinction risk in mammals. Using a decision-tree modeling framework we (i) identify interactions among multiple ecological traits that lead to different pathways to extinction across mammals and (ii) use our model to codify simple rules of thumb that can be used to guide conservation.Decision-Tree Model...

The world's grassland ecosystems are shaped in part by a key functional group of social, burrowing, herbivorous mammals. Through herbivory and ecosystem engineering they create distinctive and important habitats for many other species, thereby increasing biodiversity and habitat heterogeneity across the landscape. They also help maintain grassland presence and serve as important prey for many predators. However, these burrowing mammals are facing myriad threats, which have caused marked decreases in populations of the best‐studied species, as well as cascading declines in dependent species and in grassland habitat. To prevent or mitigate such losses, we recommend that grasslands be managed to promote the compatibility of burrowing mammals with human activities. Here, we highlight the important and often overlooked ecological roles of these burrowing mammals, the threats they face, and future management efforts needed to enhance their populations and grassland ecosystems.

The world's oceans are undergoing profound changes as a result of human activities. However, the consequences of escalating human impacts on marine mammal biodiversity remain poorly understood. The International Union for the Conservation of Nature (IUCN) identifies 25% of marine mammals as at risk of extinction, but the conservation status of nearly 40% of marine mammals remains unknown due to insufficient data. Predictive models of extinction risk are crucial to informing present and future conservation needs, yet such models have not been developed for marine mammals. In this paper, we: (i) used powerful machinelearning and spatial-modeling approaches to understand the intrinsic and extrinsic drivers of marine mammal extinction risk; (ii) used this information to predict risk across all marine mammals, including IUCN "Data Deficient" species; and (iii) conducted a spatially explicit assessment of these results to understand how risk is distributed across the world's oceans. Rate of offspring production was the most important predictor of risk. Additional predictors included taxonomic group, small geographic range area, and small social group size. Although the interaction of both intrinsic and extrinsic variables was important in predicting risk, overall, intrinsic traits were more important than extrinsic variables. In addition to the 32 species already on the IUCN Red List, our model identified 15 more species, suggesting that 37% of all marine mammals are at risk of extinction. Most at-risk species occur in coastal areas and in productive regions of the high seas. We identify 13 global hotspots of risk and show how they overlap with human impacts and Marine Protected Areas.International Union for the Conservation of Nature Red List | threatened and endangered species | life history | random forest models O ceans occupy 71% of the earth's surface and harbor much of its biodiversity. Despite the vast expanse of the oceans, no area remains unaffected by humans (1). Human activities are polluting, warming, and acidifying the oceans, melting sea ice, overharvesting fisheries, and altering entire food webs (1-4). Fisheries bycatch causes deaths of more than 650,000 marine mammals each year (5). Overfishing has depleted food supplies by reducing fish populations by 50-90%, and industrial-scale krill harvesting will likely further deplete food resources (6-8). In addition, polar oceans are warming at rates twice as fast as the global average (3); this has already altered whale migrations, reduced benthic prey populations, and caused declines in seals and polar bears (Ursus maritimus) whose lifestyles are dependent on sea ice (9). The International Union for the Conservation of Nature (IUCN) Red List currently classifies 25% (32 of 128 species) of marine mammals as threatened with extinction. Examination of the threats on the basis of the Red List shows that nearly half of all species are threatened by two or more human impacts, with pollution being the most pervasive, followed by fishing, invasive species, develop...

34 Aim 35Evaluating the relative roles of biological traits and environmental factors that predispose 36 species to an elevated risk of extinction is of fundamental importance to macroecology. Results 54Range size was the most important predictor of extinction risk, reflecting the high frequency 55 of reptiles assessed under range-based IUCN criteria. Habitat specialists occupying accessible 56 ranges were at a greater risk of extinction: although these factors never contributed more than 57 10% to the variance in extinction risk, they showed significant interactions with range size. 58Predictive power of our global models ranged between 23 and 29%. The general overall 59 pattern remained the same among geographic, taxonomic and threat-specific data subsets. Main conclusions 62Proactive conservation requires shortcuts to identify species at high risk of extinction.

Conservation priorities that are based on species distribution, endemism, and vulnerability may underrepresent biologically unique species as well as their functional roles and evolutionary histories. To ensure that priorities are biologically comprehensive, multiple dimensions of diversity must be considered. Further, understanding how the different dimensions relate to one another spatially is important for conservation prioritization, but the relationship remains poorly understood. Here, we use spatial conservation planning to (i) identify and compare priority regions for global mammal conservation across three key dimensions of biodiversity-taxonomic, phylogenetic, and traits-and (ii) determine the overlap of these regions with the locations of threatened species and existing protected areas. We show that priority areas for mammal conservation exhibit low overlap across the three dimensions, highlighting the need for an integrative approach for biodiversity conservation. Additionally, currently protected areas poorly represent the three dimensions of mammalian biodiversity. We identify areas of high conservation priority among and across the dimensions that should receive special attention for expanding the global protected area network. These highpriority areas, combined with areas of high priority for other taxonomic groups and with social, economic, and political considerations, provide a biological foundation for future conservation planning efforts. complementarity | phylogenetic dimension | spatial conservation prioritization | taxonomic dimension | trait dimension

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Aim Whether the gradients of global diversity conform to equilibrium or non‐equilibrium dynamics remains an unresolved question in ecology and evolution. Here, we evaluate four prominent hypotheses which invoke either equilibrium (more individuals, niche diversity) or non‐equilibrium dynamics (diversification rate, evolutionary time) to explain species richness and functional diversity of mammals worldwide. Location Global. Methods We combine structural equation modelling with simulations to examine whether species richness and functional diversity are in equilibrium with environmental conditions (climate, productivity) or whether they vary with non‐equilibrium factors (diversification rates, evolutionary time). We use the newest and most inclusive phylogenetic, distributional and trait data for mammals. Results We find that species richness and functional diversity are decoupled across multiple regions of the world. While species richness correlates closely with environmental conditions, functional diversity depends mostly on non‐equilibrium factors (evolutionary time to overcome niche conservatism). Moreover, functional diversity plateaus with species richness, such that species‐rich regions (especially the Neotropics) host many species that are apparently functionally redundant. Main conclusions We conclude that species richness depends on environmental factors while functional diversity depends on the evolutionary history of the region. Our work further challenges the classic notion that highly productive regions host more species because they offer a great diversity of ecological niches. Instead, they suggest that productive regions offer more resources, which allow more individuals, populations and species to coexist within a region, even when the species are apparently functionally redundant (the more individuals hypothesis). Together these findings demonstrate how ecological (the total amount of resources) and evolutionary factors (time to overcome niche conservatism) might have interacted to generate the striking diversity of mammals and their life histories.