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Open access is often contentious in the scientific community, but its implications for conservation are under‐discussed or omitted entirely from scientific discourse. Access to literature is a key factor impeding implementation of conservation research, and many open access models and concepts that are little‐known by most conservation researchers may facilitate implementation. Conservation professionals working outside academic institutions should have more access to research so that conservation is better supported by current science. In this perspective, I present elements missing from current discussions of open access and suggest potential pathways for journal publishers and researchers to make conservation publications more open. There are many promising avenues for open access to play a larger role in conservation research, including archiving pre‐prints and post‐prints, more permissive “green” open access policies, and increasing access to older articles. Collectively supporting open access practices will benefit our profession and the species we are working to protect.

Resource selection functions (RSFs) are among the most commonly used statistical tools in both basic and applied animal ecology. They are typically parameterized using animal tracking data, and advances in animal tracking technology have led to increasing levels of autocorrelation between locations in such data sets. Because RSFs assume that data are independent and identically distributed, such autocorrelation can cause misleadingly narrow confidence intervals and biased parameter estimates. Data thinning, generalized estimating equations and step selection functions (SSFs) have been suggested as techniques for mitigating the statistical problems posed by autocorrelation, but these approaches have notable limitations that include statistical inefficiency, unclear or arbitrary targets for adequate levels of statistical independence, constraints in input data and (in the case of SSFs) scale-dependent inference. To remedy these problems, we introduce a method for likelihood weighting of animal locations to mitigate the negative consequences of autocorrelation on RSFs. In this study, we demonstrate that this method weights each observed location in an animal's movement track according to its level of non-independence, expanding confidence intervals and reducing bias that can arise when there are missing data in the movement track. Ecologists and conservation biologists can use this method to improve the quality of inferences derived from RSFs. We also provide a complete, annotated analytical workflow to help new users apply our method to their own animal tracking data using the ctmm R package.

An animal's home‐range plays a fundamental role in determining its resource use and overlap with conspecifics, competitors and predators, and is therefore a common focus of movement ecology studies. Autocorrelated kernel density estimation addresses many of the shortcomings of traditional home‐range estimators when animal tracking data are autocorrelated, but other challenges in home‐range estimation remain. One such issue is known as ‘spillover bias’, in which home‐range estimates do not respect impassable movement boundaries (e.g. shorelines and fences), and occurs in all forms of kernel density estimation. While several approaches to addressing spillover bias are used when estimating home ranges, these approaches introduce bias throughout the remaining home‐range area, depending on the amount of spillover removed, or are otherwise inaccessible to most ecologists. Here, we introduce local corrections to home‐range kernels to mitigate spillover bias in (autocorrelated) kernel density estimation in the continuous time movement model (ctmm) package, and demonstrate their performance using simulations with known home‐range extents and distributions, and a real‐world case study. Simulation results showed that local corrections minimized bias in bounded home‐range area estimates, and resulted in more accurate distributions when compared with commonly used post hoc corrections, particularly at small–intermediate sample sizes. Comparison of the impacts of local vs. post hoc corrections to bounded home‐ranges estimated from lake trout (Salvelinus namaycush) demonstrated that local corrections constrained the redistribution of probability mass within the remaining home‐range area, resulting in proportionally smaller home‐range areas compared with when post hoc corrections are used.

ContextWarmer weather caused by climate change poses increasingly serious threats to the persistence of many species, but animals can modify behavior to mitigate at least some of the threats posed by warmer temperatures. Identifying and characterizing how animals modify behavior to avoid the negative consequences of acute heat will be crucial for understanding how animals will respond to warmer temperatures in the future.ObjectivesWe studied the extent to which moose (Alces alces), a species known to be sensitive to heat, mitigates heat on hot summer days via multiple different behaviors: (1) reduced movement, (2) increased visitation to shade, (3) increased visitation to water, or (4) a combination of these behaviors.MethodsWe used GPS telemetry and a step-selection function to analyze movement and habitat selection by moose in northeastern Minnesota, USA.ResultsMoose reduced movement, used areas of the landscape with more shade, and traveled nearer to mixed forests and bogs during periods of heat. Moose used shade far more than water to ameliorate heat, and the most pronounced changes in behavior occurred between 15 and 20 °C.ConclusionsResearch characterizing the behaviors animals use to facilitate thermoregulation will aid conservation of heat-sensitive species in a warming world. The modeling framework presented in this study is a promising method for evaluating the influence of temperature on movement and habitat selection.

Homeothermy requires increased metabolic rates as temperatures decline below the thermoneutral zone, so homeotherms typically select microhabitats within or near their thermoneutral zones during periods of inactivity. However, many mammals and birds are heterotherms that relax internal controls on body temperature and go into torpor when maintaining a high, stable body temperature, which is energetically costly. Such heterotherms should be less tied to microhabitats near their thermoneutral zones and, because heterotherms spend more time in torpor and expend less energy at colder temperatures, heterotherms may even select microhabitats in which temperatures are well below their thermoneutral zones. We studied how temperature and daily torpor influence the selection of microhabitats (i.e., diurnal roosts) by a heterothermic bat (Myotis thysanodes). We (1) quantified the relationship between ambient temperature and daily duration of torpor, (2) simulated daily energy expenditure over a range of microhabitat temperatures, and (3) quantified the influence of microhabitat temperature on microhabitat selection. In addition, warm microhabitats substantially reduced the energy expenditure of simulated homeothermic bats, and heterothermic bats modulated their use of daily torpor to maintain a constant level of energy expenditure across microhabitats of different temperatures. Daily torpor expanded the range of energetically economical microhabitats, such that microhabitat selection was independent of microhabitat temperature. Our work adds to a growing literature documenting the functions of torpor beyond its historical conceptualization as a last-resort measure to save energy during periods of extended or acute energetic stress.

Diverse communities of large mammalian herbivores (LMH), once widespread, are now rare. LMH exert strong direct and indirect effects on community structure and ecosystem functions, and measuring these effects is important for testing ecological theory and for understanding past, current, and future environmental change. This in turn requires long-termexperimental manipulations, owing to the slow and often nonlinear responses of populations and assemblages to LMH removal. Moreover, the effects of particular species or body-size classes within diverse LMH guilds are difficult to pinpoint, and the magnitude and even direction of these effects often depends on environmental context. Since 2008, we have maintained the Ungulate Herbivory Under Rainfall Uncertainty (UHURU) experiment, a series of size-selective LMH exclosures replicated across a rainfall/productivity gradient in a semiarid Kenyan savanna. The goals of the UHURU experiment are to measure the effects of removing successively smaller size classes of LMH (mimicking the process of size-biased extirpation) and to establish how these effects are shaped by spatial and temporal variation in rainfall. The UHURU experiment comprises three LMH-exclusion treatments and an unfenced control, applied to nine randomized blocks of contiguous 1-ha plots (n = 36). The fenced treatments are MEGA (exclusion of megaherbivores, elephant and giraffe), MESO (exclusion of herbivores ≥40 kg), and TOTAL (exclusion of herbivores ≥5 kg). Each block is replicated three times at three sites across the 20-km rainfall gradient, which has fluctuated over the course of the experiment. The first 5 years of data were published previously (Ecological Archives E095-064) and have been used in numerous studies. Since that publication, we have (1) continued to collect data following the original protocols, (2) improved the taxonomic resolution and accuracy of plant and small-mammal identifications, and (3) begun collecting several new data sets. Here, we present updated and extended raw data from the first 12 years of the UHURU experiment (2008–2019). Data include daily rainfall data throughout the experiment; annual surveys of understory plant communities; annual censuses of woody-plant communities; annual measurements of individually tagged woody plants; monthly monitoring of flowering and fruiting phenology; every-other-month small-mammal mark–recapture data; and quarterly largemammal dung surveys. There are no copyright restrictions; notification of when and how data are used is appreciated and users of UHURU data should cite this data paper when using the data.

The Santa Catalina Mountains are an iconic member of the Madrean Sky Islands, rising above Tucson, Arizona, USA, where the Catalina Highway connects Sonoran desertscrub to stands of conifer forest nearly 2,800 meters in elevation. As one of the ~54 forested mountain areas in this system, the Santa Catalinas host unique biotic communities relative to the surrounding lowlands. However, most of these sky islands lack the surveys of resident small mammals (either historical or recent) needed for studying biodiversity in the context of changing climate and habitat use. From 2021 to 2023, we surveyed 10 localities on the north and south slopes of the Santa Catalina Mountains using holistic sampling methods to document terrestrial small mammal diversity and preserve multiple tissue types. Here we summarize these new collections relative to previous voucher specimens and human observations, identifying gaps for future work to address. Our survey recorded the presence of 15 species, preserved 150 voucher specimens paired with a suite of flash-frozen tissues, and non-lethally sampled another 219 individuals (ear tissue, feces, ectoparasites, and measurements) to provide populational data from sites where vouchering occurred. Despite the road accessibility and long history of sampling in the Santa Catalina Mountains, our surveys extended the known elevational range for 8 species, including the first known specimen of from the area. Our use of a transect-based survey design, which maximizes species diversity across biotic communities, paired with holistic specimen preservation techniques, provides a model for surveying patterns of population genetic and parasite sharing relationships across other Madrean Sky Islands, bridging a ~40 year lull in specimen preservation while adding new data dimensions that promote integrative studies of small mammal biodiversity. With more complete sampling, other mountains will offer promising replicates for studying eco-evolutionary impacts of the region's episodic habitat connectivity.

The Mammal Diversity Database (MDD) is an open-access resource providing up-to-date taxonomic, nomenclatural, and geographic data for global mammal species. Since its launch in 2018, the MDD has transformed the traditionally static process of updating mammalian taxonomy into regular online releases reflecting the latest published research. To build on this foundation, we here present version 2.0 of the MDD (MDD2), which catalogues 6,759 living and recently extinct mammal species, representing net increases of 4.1% and 24.8% over MDD version 1.0 and , 3rd edition (MSW3), respectively. Additionally, we identify a net increase of 68.8% (+2,754; 3,149 splits + de novo, 395 lumps) species since 1980 at a rate of ~65 species/year based on past totals from 14 mammalian compendia, leading to projections of ~7,084 species by 2030 and ~8,382 by 2050 if these trends continue. Key updates in MDD2 include: (i) codings of US state, country, continent, and biogeographic realm geographic categories for each species; (ii) a comprehensive nomenclatural dataset for 50,230 valid and synonymous species-rank names, curated with type locality and specimen information for the first time; and (iii) integration between the MDD and the databases Hesperomys and Batnames for greater data accuracy and completeness. These updates bridge critical gaps in the taxonomic and nomenclatural information needed for ongoing revisions and assessments of mammalian species diversity. Using these data, we evaluate temporal and geographic trends over the past 267 years, identifying four major time periods of change in mammalian taxonomy and nomenclature: (i) the initial monographic description of traditionally charismatic species (1758-1880); (ii) the peak of descriptive taxonomy, describing subspecies, and publishing in journals (1881-1939); (iii) the shift toward revisionary taxonomy and polytypic species (1940-1999); and (iv) the current technology-driven period of integrative revisionary taxonomy (2000-present). Geographically, new species recognition since MSW3 has been concentrated in equatorial, mountainous, and island regions, highlighting areas of high mammal endemism (e.g., Madagascar, Philippines, Andes, East Africa, Himalayas, Atlantic Forests). However, gaps in 21st century taxonomic activity are identified in West and Central Africa, India, and some parts of Indonesia. Currently lagging conservation assessments are alarming, with 25% of the MDD2-recognized mammal species allocated to the 'understudied' conservation threat categories of Data Deficient (11%) or Not Evaluated (14%), underscoring the need for greater taxonomic integration with conservation organizations. Governance advancements in MDD2 include the establishment of external taxonomic subcommittees to guide data collection and curation, a rewritten website that improves access and scalability, a cross-platform application that provides offline access, and new partnerships to continue linking MDD data to global biodiversity infrastructure. By providing up-to-date mammalian taxonomic and nomenclatural data-including links to the text of original name descriptions, type localities, and type specimen collections-the MDD provides an integrative resource for mammalogists and conservationists to more easily track the status of their study organisms.

Because of this, researchers are working to develop new ways for researchers, research institutions, research funders, and journals to overcome this problem (Peng 2011, Fiedler et al. 2012, Sandve et al. 2013, Stodden et al. 2013). Because replicating studies with new independent data is expensive, rarely published in high‐impact journals, and sometimes even methodologically impossible, computationally reproducible research (most often termed simply “reproducible research”) is often suggested as a pathway for increasing our ability to assess the validity and rigor of scientific results (Peng 2011). [...]it also protects researchers from accusations of research misconduct due to analytical errors, because it is unlikely that researchers would openly share fraudulent code and data with the rest of the research community. [...]reproducible research increases paper citation rates (Piwowar et al. 2007, McKiernan et al. 2016) and allows other researchers to cite code and data in addition to publications. [...]reproducible research allows others to protect themselves from your mistakes.

Diverse communities of large mammalian herbivores (LMH), once widespread, are now rare. LMH exert strong direct and indirect effects on community structure and ecosystem functions, and measuring these effects is important for testing ecological theory and for understanding past, current, and future environmental change. This in turn requires long‐term experimental manipulations, owing to the slow and often nonlinear responses of populations and assemblages to LMH removal. Moreover, the effects of particular species or body‐size classes within diverse LMH guilds are difficult to pinpoint, and the magnitude and even direction of these effects often depends on environmental context. Since 2008, we have maintained the Ungulate Herbivory Under Rainfall Uncertainty (UHURU) experiment, a series of size‐selective LMH exclosures replicated across a rainfall/productivity gradient in a semiarid Kenyan savanna. The goals of the UHURU experiment are to measure the effects of removing successively smaller size classes of LMH (mimicking the process of size‐biased extirpation) and to establish how these effects are shaped by spatial and temporal variation in rainfall. The UHURU experiment comprises three LMH‐exclusion treatments and an unfenced control, applied to nine randomized blocks of contiguous 1‐ha plots (n = 36). The fenced treatments are MEGA (exclusion of megaherbivores, elephant and giraffe), MESO (exclusion of herbivores ≥40 kg), and TOTAL (exclusion of herbivores ≥5 kg). Each block is replicated three times at three sites across the 20‐km rainfall gradient, which has fluctuated over the course of the experiment. The first 5 years of data were published previously (Ecological Archives E095‐064) and have been used in numerous studies. Since that publication, we have (1) continued to collect data following the original protocols, (2) improved the taxonomic resolution and accuracy of plant and small‐mammal identifications, and (3) begun collecting several new data sets. Here, we present updated and extended raw data from the first 12 years of the UHURU experiment (2008–2019). Data include daily rainfall data throughout the experiment; annual surveys of understory plant communities; annual censuses of woody‐plant communities; annual measurements of individually tagged woody plants; monthly monitoring of flowering and fruiting phenology; every‐other‐month small‐mammal mark–recapture data; and quarterly large‐mammal dung surveys. There are no copyright restrictions; notification of when and how data are used is appreciated and users of UHURU data should cite this data paper when using the data.