Ant diversity shows a variety of patterns across elevational gradients, though the patterns and drivers have not been evaluated comprehensively. In this systematic review and reanalysis, we use published data on ant elevational diversity to detail the observed patterns and to test the predictions and interactions of four major diversity hypotheses: thermal energy, the mid-domain effect, area, and the elevational climate model. Of sixty-seven published datasets from the literature, only those with standardized, comprehensive sampling were used. Datasets included both local and regional ant diversity and spanned 80° in latitude across six biogeographical provinces. We used a combination of simulations, linear regressions, and non-parametric statistics to test multiple quantitative predictions of each hypothesis. We used an environmentally and geometrically constrained model as well as multiple regression to test their interactions. Ant diversity showed three distinct patterns across elevations: most common were hump-shaped mid-elevation peaks in diversity, followed by low-elevation plateaus and monotonic decreases in the number of ant species. The elevational climate model, which proposes that temperature and precipitation jointly drive diversity, and area were partially supported as independent drivers. Thermal energy and the mid-domain effect were not supported as primary drivers of ant diversity globally. The interaction models supported the influence of multiple drivers, though not a consistent set. In contrast to many vertebrate taxa, global ant elevational diversity patterns appear more complex, with the best environmental model contingent on precipitation levels. Differences in ecology and natural history among taxa may be crucial to the processes influencing broad-scale diversity patterns.

The rush to assess species' responses to anthropogenic climate change (CC) has underestimated the importance of interannual population variability (PV). Researchers assume sampling rigor alone will lead to an accurate detection of response regardless of the underlying population fluctuations of the species under consideration. Using population simulations across a realistic, empirically based gradient in PV, we show that moderate to high PV can lead to opposite and biased conclusions about CC responses. Between pre- and post-CC sampling bouts of modeled populations as in resurvey studies, there is: (i) A 50% probability of erroneously detecting the opposite trend in population abundance change and nearly zero probability of detecting no change. (ii) Across multiple years of sampling, it is nearly impossible to accurately detect any directional shift in population sizes with even moderate PV. (iii) There is up to 50% probability of detecting a population extirpation when the species is present, but in very low natural abundances. (iv) Under scenarios of moderate to high PV across a species' range or at the range edges, there is a bias toward erroneous detection of range shifts or contractions. Essentially, the frequency and magnitude of population peaks and troughs greatly impact the accuracy of our CC response measurements. Species with moderate to high PV (many small vertebrates, invertebrates, and annual plants) may be inaccurate 'canaries in the coal mine' for CC without pertinent demographic analyses and additional repeat sampling. Variation in PV may explain some idiosyncrasies in CC responses detected so far and urgently needs more careful consideration in design and analysis of CC responses.

Aim Species richness is often strongly correlated with climate. The most commonly invoked mechanism for this climate‐richness relationship is the more‐individuals‐hypothesis (MIH), which predicts a cascading positive influence of climate on plant productivity, food resources, total number of individuals, and species richness. We test for a climate‐richness relationship and an underlying MIH mechanism, as well as testing competing hypotheses including positive effects of habitat diversity and heterogeneity, and the species‐area effect. Location Colorado Rocky Mountains, USA: two elevational gradients in the Front Range and San Juan Mountains. Methods We conducted standardized small mammal surveys at 32 sites to assess diversity and population sizes. We estimated vegetative and arthropod food resources as well as various aspects of habitat structure by sampling 20 vegetation plots and 40 pitfall traps per site. Temperature, precipitation and net primary productivity (NPP) were assessed along each gradient. Regressions and structural equation modelling were used to test competing diversity hypotheses and mechanistic links predicted by the MIH. Results We detected 3,922 individuals of 37 small mammal species. Mammal species richness peaked at intermediate elevations, as did productivity, whereas temperature decreased and precipitation increased with elevation. We detected strong support for a productivity‐richness relationship, but no support for the MIH mechanism. Food and mammal population sizes were unrelated to NPP or mammal species richness. Furthermore, mammal richness was unrelated to habitat diversity, habitat heterogeneity, or elevational area. Main conclusions Sites with high productivity contain high mammal species richness, but a mechanism other than a contemporary MIH underlies the productivity–diversity relationship. Possibly a mechanism based on evolutionary climatic affiliations. Protection of productive localities and mid‐elevations are the most critical for preserving small mammal richness, but may be decoupled from trends in population sizes, food resources, or habitat structure.

Understanding the forces that shape the distribution of biodiversity across spatial scales is central in ecology and critical to effective conservation. To assess effects of possible richness drivers, we sampled ant communities on four elevational transects across two mountain ranges in Colorado, USA, with seven or eight sites on each transect and twenty repeatedly sampled pitfall trap pairs at each site each for a total of 90 d. With a multi‐scale hierarchical Bayesian community occupancy model, we simultaneously evaluated the effects of temperature, productivity, area, habitat diversity, vegetation structure, and temperature variability on ant richness at two spatial scales, quantifying detection error and genus‐level phylogenetic effects. We fit the model with data from one mountain range and tested predictive ability with data from the other mountain range. In total, we detected 105 ant species, and richness peaked at intermediate elevations on each transect. Species‐specific thermal preferences drove richness at each elevation with marginal effects of site‐scale productivity. Trap‐scale richness was primarily influenced by elevation‐scale variables along with a negative impact of canopy cover. Soil diversity had a marginal negative effect while daily temperature variation had a marginal positive effect. We detected no impact of area, land cover diversity, trap‐scale productivity, or tree density. While phylogenetic relationships among genera had little influence, congeners tended to respond similarly. The hierarchical model, trained on data from the first mountain range, predicted the trends on the second mountain range better than multiple regression, reducing root mean squared error up to 65%. Compared to a more standard approach, this modeling framework better predicts patterns on a novel mountain range and provides a nuanced, detailed evaluation of ant communities at two spatial scales.

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The largest and tallest mountain range in the contiguous United States, the Southern Rocky Mountains, has warmed considerably in the past several decades due to anthropogenic climate change. Herein we examine how 47 mammal elevational ranges (27 rodent and 4 shrew species) have changed from their historical distributions (1886-1979) to their contemporary distributions (post 2005) along 2,400-m elevational gradients in the Front Range and San Juan Mountains of Colorado. Historical elevational ranges were based on more than 4,580 georeferenced museum specimen and publication records. Contemporary elevational ranges were based on 7,444 records from systematic sampling efforts and museum specimen records. We constructed Bayesian models to estimate the probability a species was present, but undetected, due to undersampling at each 50-m elevational bin for each time period and mountain range. These models leveraged individual-level detection probabilities, the number and patchiness of detections across 50-m bands of elevation, and a decaying likelihood of presence from last known detections. We compared 95% likelihood elevational ranges between historical and contemporary time periods to detect directional change. Responses were variable as 26 mammal ranges changed upward, 6 did not change, 11 changed downward, and 4 were extirpated locally. The average range shift was 131 m upward, while exclusively montane species shifted upward more often (75%) and displayed larger average range shifts (346 m). The best predictors of upper limit and total directional change were species with higher maximum latitude in their geographic range, montane affiliation, and the study mountain was at the southern edge of their geographic range. Thus, mammals in the Southern Rocky Mountains serve as harbingers of more changes to come, particularly for montane, coldadapted species in the southern portion of their ranges.

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Ecological models are constrained by the availability of high‐quality data at biologically appropriate resolutions and extents. Modeling a species' affinity or aversion with a particular land cover class requires data detailing that class across the full study area. Data sets with detailed legends (i.e., high thematic resolution) and/or high accuracy often sacrifice geographic extent, while large‐area data sets often compromise on the number of classes and local accuracy. Consequently, ecologists must often restrict their study extent to match that of the more precise data set, or ignore potentially key land cover associations to study a larger area. We introduce a hierarchical Bayesian model to capitalize on the thematic resolution and accuracy of a regional land cover data set, and on the geographic breadth of a large area land cover data set. For the full extent (i.e., beyond the regional data set), the model predicts systematic discrepancies of the large‐area data set with the regional data set, and divides an aggregated class into two more specific classes detailed by the regional data set. We illustrate the application of our model for mapping eastern white pine (Pinus strobus) forests, an important timber species that also provides habitat for an invasive shrub in the northeastern United States. We use the National Land Cover Database (NLCD), which covers the full study area but includes only generalized forest classes, and the NH GRANIT land cover data set, which maps White Pine Forest and has high accuracy, but only exists within New Hampshire. We evaluate the model at coarse (20 km2) and fine (2 km2) resolutions, with and without spatial random effects. The hierarchical model produced improved maps of compositional land cover for the full extent, reducing inaccuracy relative to NLCD while partitioning a White Pine Forest class out of the Evergreen Forest class. Accuracy was higher with spatial random effects and at the coarse resolution. All models improved upon simply partitioning Evergreen Forest in NLCD based on the predicted distribution of white pine. This flexible statistical method helps ecologists leverage localized mapping efforts to expand models of species distributions, population dynamics, and management strategies beyond the political boundaries that frequently delineate land cover data sets.