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Landscape dynamics are widely thought to govern the tempo and mode of continental radiations, yet the effects of river network rearrangements on dispersal and lineage diversification remain poorly understood. We integrated an unprecedented occurrence dataset of 4,967 species with a newly compiled, time-calibrated phylogeny of South American freshwater fishes—the most species-rich continental vertebrate fauna on Earth—to track the evolutionary processes associated with hydrogeographic events over 100 Ma. Net lineage diversification was heterogeneous through time, across space, and among clades. Five abrupt shifts in net diversification rates occurred during the Paleogene and Miocene (between 30 and 7 Ma) in association with major landscape evolution events. Net diversification accelerated from the Miocene to the Recent (c. 20 to 0 Ma), with Western Amazonia having the highest rates of in situ diversification, which led to it being an important source of species dispersing to other regions. All regional biotic interchanges were associated with documented hydrogeographic events and the formation of biogeographic corridors, including the Early Miocene (c. 23 to 16 Ma) uplift of the Serra do Mar and Serra da Mantiqueira and the Late Miocene (c. 10 Ma) uplift of the Northern Andes and associated formation of the modern transcontinental Amazon River. The combination of high diversification rates and extensive biotic interchange associated with Western Amazonia yielded its extraordinary contemporary richness and phylogenetic endemism. Our results support the hypothesis that landscape dynamics, which shaped the history of drainage basin connections, strongly affected the assembly and diversification of basin-wide fish faunas.

Given the importance of species diversity as a tool for assessing recovery during forest regeneration and active restoration, robust approaches for assessing changes in tree species diversity over time are urgently needed. We assessed changes in tree species diversity during natural regeneration over 12–20 years in eight 1-ha monitoring plots in NE Costa Rica, six second-growth forests and two old-growth reference forests. We used diversity profiles to show successional trajectories in measures of observed, asymptotic and standardized tree diversity and evenness as well as sample completeness. We randomly subsampled 1-ha plot data to evaluate how well smaller spatial subsamples would have captured temporal trajectories. Annual surveys in eight 1-ha plots were missing substantial numbers of rare or infrequent species. Older second-growth sites showed consistent declines in tree diversity, whereas younger sites showed fluctuating patterns or increases. Subsample areas of 0.5 ha or greater were sufficient to infer the diversity of abundant species, but smaller subsamples failed to capture temporal trajectories of species richness and yielded positively biased estimates of evenness. In tropical forest regions with high levels of diversity, species diversity from small sample plots should be assessed using methods that incorporate abundance information and that standardize for sample coverage. This article is part of the theme issue ‘Understanding forest landscape restoration: reinforcing scientific foundations for the UN Decade on Ecosystem Restoration’.

With ever-growing data availability and computational power at our disposal, we now have the capacity to use process-explicit models more widely to reveal the ecological and evolutionary mechanisms responsible for spatiotemporal patterns of biodiversity. Most research questions focused on the distribution of diversity cannot be answered experimentally, because many important environmental drivers and biological constraints operate at large spatiotemporal scales. However, we can encode proposed mechanisms into models, observe the patterns they produce in virtual environments, and validate these patterns against real-world data or theoretical expectations. This approach can advance understanding of generalizable mechanisms responsible for the distributions of organisms, communities, and ecosystems in space and time, advancing basic and applied science. We review recent developments in process-explicit models and how they have improved knowledge of the distribution and dynamics of life on Earth, enabling biodiversity to be better understood and managed through a deeper recognition of the processes that shape genetic, species, and ecosystem diversity.

Progressive habitat transformation causes global changes in landscape biodiversity patterns, but can be hard to quantify. Rarefaction/extrapolation approaches can quantify within-habitat biodiversity, but may not be useful for cases in which one habitat type is progressively transformed into another habitat type. To quantify biodiversity patterns in such transformed landscapes, we use Hill numbers to analyse individual-based species abundance data or replicated, sample-based incidence data. Given biodiversity data from two distinct habitat types, when a specified proportion of original habitat is transformed, our approach utilises a proportional mixture of two within-habitat rarefaction/extrapolation curves to analytically predict biodiversity changes, with bootstrap confidence intervals to assess sampling uncertainty. We also derive analytic formulas for assessing species composition (i.e. the numbers of shared and unique species) for any mixture of the two habitat types. Our analytical and numerical analyses revealed that species unique to each habitat type are the most important determinants of landscape biodiversity patterns.

Humans currently collectively use thousands of languages1,2. The number of languages in a given region (i.e. language “richness”) varies widely3–7. Understanding the processes of diversification and homogenization that produce these patterns has been a fundamental aim of linguistics and anthropology. Empirical research to date has identified various social, environmental, geographic, and demographic factors associated with language richness3. However, our understanding of causal mechanisms and variation in their effects over space has been limited by prior analyses focusing on correlation and assuming stationarity3,8. Here we use process-based, spatially-explicit stochastic models to simulate the emergence, expansion, contraction, fragmentation, and extinction of language ranges. We varied combinations of parameter settings in these computer-simulated experiments to evaluate the extent to which different processes reproduce observed patterns of pre-colonial language richness in North America. We find that the majority of spatial variation in language richness can be explained by models in which environmental and social constraints determine population density, random shocks alter population sizes more frequently at higher population densities, and population shocks are more frequently negative than positive. Language diversification occurs when populations split after reaching size limits, and when ranges fragment due to population contractions following negative shocks or due to contact with other groups that are expanding following positive shocks. These findings support diverse theoretical perspectives arguing that language richness is shaped by environmental and social conditions, constraints on group sizes, outcomes of contact among groups, and shifting demographics driven by positive innovations, such as new subsistence strategies, or negative events, such as war or disease.

Papua New Guinea is home to >10% of the world’s languages and rich and varied biocultural knowledge, but the future of this diversity remains unclear. We measured language skills of 6,190 students speaking 392 languages (5.5% of the global total) and modelled their future trends, using individual-level variables characterizing family language use, socio-economic conditions, student’s skills, and language traits. This approach showed that only 58% of the students, compared to 91% of their parents, were fluent in indigenous languages, while the trends in key drivers of language skills (language use at home, proportion of mixed-language families, urbanization, students’ traditional skills) predicted accelerating decline of fluency, to an estimated 26% in the next generation of students. Ethnobiological knowledge declined in close parallel with language skills. Varied medicinal plant uses known to the students speaking indigenous languages are replaced by a few, mostly non-native species for the students speaking English or Tok Pisin, the national lingua franca. Most (88%) students want to teach indigenous language to their children. While crucial for keeping languages alive, this intention faces powerful external pressures as key factors (education, cash economy, road networks, urbanization) associated with language attrition are valued in contemporary society.Significance StatementAround the world, more than 7,000 languages are spoken, most of them by small populations of speakers in the tropics. Globalization puts small languages at a disadvantage, but our understanding of the drivers and rate of language loss remains incomplete. When we tested key factors causing language attrition among Papua New Guinean students speaking 392 different indigenous languages, we found an unexpectedly rapid decline in their language skills compared to their parents and predicted further acceleration of language loss in the next generation. Language attrition was accompanied by decline in the traditional knowledge of nature among the students, pointing to an uncertain future for languages and biocultural knowledge in the most linguistically diverse place on Earth.

The Central Mountains Archaic began with the arrival of foraging populations in the Intermountain West about 6000 years ago. This migration coincided with the "extremely dramatic" winter-wet event of 4350 cal b.c. and the arrival of piñon pine forests in the central Great Basin. Human foragers likely played a significant role in the rapid spread of piñon across the central and northeastern Great Basin. Logistic hunters exploited local bighorn populations, sometimes serviced by hunting camps (the "man caves" such as Gatecliff Shelter, Triple T Shelter, and several others) and they staged communal pronghorn drives at lower elevations. As climate cooled and became more moist, logistic bighorn hunting gradually shifted downslope, then apparently faded away about 1000 cal b.c. Communal pronghorn driving persisted into the historic era in the central Great Basin. This volume, the first in the Alta Toquima trilogy, describes and analyzes more than 100 alpine hunting features on the Mt. Jefferson tablelands. High-elevation, logistical bighorn hunting virtually disappeared across the central Great Basin with the onset of the Late Holocene Dry Period (about 750-850 cal b.c.), giving way to an alpine residential pattern at Alta Toquima (26NY920) and elsewhere on Mt. Jefferson. Situated at almost exactly 11,000 ft (3352 m) above sea level, Alta Toquima was sited on the south summit of Mt. Jefferson (the third-highest spot in the state of Nevada), where at least 31 residential stone structures were emplaced along this steep, east-facing slope. When first recorded in 1978, Alta Toquima was the highest American Indian village site known in the Northern Hemisphere. This volume discusses the material culture, plant macrofossils, vertebrate fauna, and radiocarbon dating for Alta Toquima. Bayesian analysis of 95 14C dates documents an initial occupation of Alta Toquima at 1370-790 cal b.c., with the sporadic settlements persisting until immediately before European contact. These alpine residences are the most dramatic examples of the intensified provisioning strategies that began in the Central Mountains Archaic about 3000 years ago, broadening the diet breadth to include plant and animal resources previously considered too costly. The oldest summertime residence at Alta Toquima correlates with the onset of Late Holocene Dry Period (LHDP) aridity (~750 cal b.c.), and these houses were episodically occupied only during the driest intervals throughout the next 1500 dramatic years of abrupt climate change. During the intervening wetter stretches, Alta Toquima was abandoned in favor of subalpine basecamps. This sequenced intensification predated the arrival of bow technology in the central Great Basin by more than a millennium. Exactly the opposite sequencing took place a few miles to the north, when Gatecliff Shelter was abandoned during LHDP aridity--precisely when the first summertime settlements appeared at Alta Toquima. This pattern reversed again when lowland habitats became sufficiently well watered to again support summertime patches of seeds and geophytes (~150 cal b.c.-cal a.d. 100). Alta Toquima families responded by abandoning (temporarily) their alpine summertime camps to repurpose former "man caves" like Gatecliff and Triple T shelters into family settlements. The Monitor Valley sequence documents several syncopated lowland-alpine, wet-dry reversals, reflecting an adaptive diversity that spanned more than two millennia. The drought terminating cal a.d. 1150 devastated much of the western Great Basin and American Southwest, but its impact was less severe in central Nevada. Although subalpine sites were again abandoned during the drought buildup that peaked in the mid-12th century, summertime occupation of Alta Toquima became more commonplace, although it increased notably during the ~cal a.d. 1200-1400 aridity and persisted throughout the Little Ice Age.

In their recent critique, Qian et al. (2017) claimed that the results of structural equation modeling analysis (SEM) in Chan et al. (2016) were flawed. Here, we show that the source of the difference in their re-analysis is that Qian et al. did not follow the standard, iterative process of SEM, which allows researchers to evaluate which model offers the best account of the data in both absolute and relative senses. Here, we provide step-by-step instructions to reproduce our published results. All of Qian et al.′s concerns regarding SEM can be put to rest. Moreover, in our original paper we used three distinct statistical methods-hierarchical partitioning, SEM, and stationary bootstrap-to show that different temporal scales of environmental variability can differentially impact the elevational range size (ERS) of species. It is time to move on to probing the pressing issue of how and why climatic variability impacts ERS.

The body size of an animal is probably its most important functional trait. For arthropods, environmental drivers of body size variation are still poorly documented and understood, especially in tr ...

Macroecology has traditionally relied on descriptive characterization of large-scale ecological patterns to offer narrative explanations for the origin and maintenance of those patterns. Only recently have macroecologists begun to employ models termed ‘process-based’ and ‘mechanistic’, in contrast to other areas of ecology, where such models have a longer history. Here, we define and differentiate between process-based and mechanistic features of models, and we identify and discuss important advantages of working with models possessing such features. We describe some of the risks associated with process-based and mechanistic model-centered research programs, and we propose ways to mitigate these risks. Giving process-based and mechanistic models a more central role in research programs can reinvigorate macroecology by strengthening the link between theory and data.

Summary Due to sampling limitations, almost every biodiversity survey misses species that are present, but not detected, so that empirical species counts underestimate species richness. A wide range of species richness estimators has been proposed in the literature to reduce undersampling bias. We focus on nonparametric estimators, which make no assumptions about the mathematical form of the underlying species abundance/incidence distributions. For replicated incidence data, in which only species presence/absence (or detection/non-detection) is recorded in multiple sampling units, most existing nonparametric estimators of the number of undetected species are based on the frequency counts of the uniques (species detected in only one sampling unit) and duplicates (species detected in exactly two sampling units). Some survey methods, however, record only uniques and super-duplicates (species observed in more than one sampling unit). Using the Good–Turing frequency formula, we developed a method to estimate the number of duplicates for such data, allowing estimation of true species richness, including undetected species. We test our estimators on several empirical datasets for which doubletons were recorded and on simulated sampling data, then apply them to an extensive, but previously unusable survey of coral reef fishes, for which only uniques and super-duplicates were recorded. We extend the method to abundance data and discuss other potential applications.

We develop a novel class of measures to quantify sample completeness of a biological survey. The class of measures is parameterized by an order q ≥ 0 to control for sensitivity to species relative abundances. When q = 0, species abundances are disregarded and our measure reduces to the conventional measure of completeness, that is, the ratio of the observed species richness to the true richness (observed plus undetected). When q = 1, our measure reduces to the sample coverage (the proportion of the total number of individuals in the entire assemblage that belongs to detected species), a concept developed by Alan Turing in his cryptographic analysis. The sample completeness of a general order q ≥ 0 extends Turing's sample coverage and quantifies the proportion of the assemblage's individuals belonging to detected species, with each individual being proportionally weighted by the (q − 1)th power of its abundance. We propose the use of a continuous profile depicting our proposed measures with respect to q ≥ 0 to characterize the sample completeness of a survey. An analytic estimator of the diversity profile and its sampling uncertainty based on a bootstrap method are derived and tested by simulations. To compare diversity across multiple assemblages, we propose an integrated approach based on the framework of Hill numbers to assess (a) the sample completeness profile, (b) asymptotic diversity estimates to infer true diversities of entire assemblages, (c) non‐asymptotic standardization via rarefaction and extrapolation, and (d) an evenness profile. Our framework can be extended to incidence data. Empirical data sets from several research fields are used for illustration. Paid Open Access

Mountain regions are unusually biodiverse, with especially rich aggregations of small-30 ranged species that form centers of endemism. Mountains play an array of important roles for Earth's biodiversity, and impact neighboring lowlands through biotic interchange, changes in regional climate, and nutrient run-off. The high biodiversity of certain mountains reflects the interplay of multiple evolutionary mechanisms: enhanced speciation rates with unique opportunities for co-existence and persistence of lineages, shaped by long-term climatic changes 35 interacting with topographically dynamic landscapes. High diversity in most tropical mountains is tightly linked to bedrock geology, notably areas comprising mafic and ultramafic lithologies—rock types rich in magnesium and poor in phosphate that present special requirements for plant physiology. Mountain biodiversity bears the signature of deep-time evolutionary and ecological processes, a history worth preserving in the face of contemporary environmental changes.

Aim To identify hotspots of endemic and non-endemic avian diversity in the mountains of south-west China and delineate biodiversity corridors that connect the faunas of northern and southern Asia. To understand how biodiversity and endemism in this region has been maintained through palaeoclimate change. Location The mountains of south-west China, spanning an elevational gradient > 7000 m. Methods We used the distributional data of 752 breeding birds to investigate current patterns of diversity across elevational and geographical space. We simulated species richness under palaeoclimate models of global temperature change, assessing changes in species richness. Results Contemporary species richness of non-endemic birds peaked at 800–1800 m elevation, while endemic richness peaked at 2000–3000 m. Richness of non-endemic birds was highest in the southern Hengduan Mountains and Yungui Plateau, while endemic richness peaked further north, extending into the mountains along the western edge of the Sichuan Basin. Under global warming models, species richness remained high throughout the Hengduan Mountains region. Under global cooling models, the Sichuan Basin showed increased richness. Conclusions Endemism peaked in the mountains along the western edge of the Sichuan Basin, highlighting the importance of this region in promoting and maintaining diversity. This region has likely functioned as a biodiversity corridor, bridging the Palaearctic and Oriental biotas to the north and south. Climate simulations suggest that the mountains of south-west China can accommodate upslope range shifts in response to warming, but low elevation specialists may have experienced increased extinction probabilities during cold periods in the recent past, which may in part explain the current mid-elevation diversity peak. During glacial periods the Sichuan Basin likely served as a warm refugium for montane birds. Steep environmental heterogeneity has been a key to maintaining high diversity and endemism in the region during palaeoclimate change. These same features will likely shape the effects of future climate change on biodiversity in the region.

Mountains contribute disproportionately to the terrestrial biodiversity of Earth, especially in the tropics, where they host hotspots of extraordinary and puzzling richness. With about 25% of all land area, mountain regions are home to more than 85% of the world’s species of amphibians, birds, and mammals, many entirely restricted to mountains. Biodiversity varies markedly among these regions. Together with the extreme species richness of some tropical mountains, this variation has proven challenging to explain under traditional climatic hypotheses. However, the complex climatic characteristics of rugged mountain regions differ fundamentally from those of lowland regions, likely playing a key role in generating and maintaining diversity. With ongoing global changes in climate and land use, the role of mountains as refugia for biodiversity may well come under threat.

Ronald Mason’s hypothesis from the 1960s that the southeastern United States possesses greater Paleoindian projectile-point diversity than other regions is regularly cited, and often assumed to be true, but in fact has never been quantitatively tested. Even if valid, however, the evolutionary meaning of this diversity is contested. Point diversity is often linked to Clovis “origins,” but point diversity could also arise from group fissioning and drift, admixture, adaptation, or multiple founding events, among other possibilities. Before archaeologists can even begin to discuss these scenarios, it is paramount to ensure that what we think we know is representative of reality. To this end, we tested Mason’s hypothesis for the first time, using a sample of 1,056 Paleoindian points from eastern North America arui employing paradigmatic classification and rigorous statistical tools used in the quantification of ecological biodiversity. Our first set of analyses, which compared the Southeast to the Northeast, showed that the Southeast did indeed possess significantly greater point-class richness. Although this result was consistent with Mason’s hypothesis, our second set of analyses, which compared the Upper Southeast to the Lower Southeast and the Northeast showed that in terms of point-class richness the Upper Southeast > Lower Southeast > Northeast. Given current chronometrie evidence, we suggest that this latter result is consistent with the suggestion that the area of the Ohio, Cumberland, and Tennessee River valleys, as well as the mid-Atlantic coastal plain, were possible initial and secondary “staging areas” for colonizing Paleoindian foragers moving from western to eastern North America.

The statistical framework of rarefaction curves and asymptotic estimators allows for an effective standardization of biodiversity measures. However, most statistical analyses still consist of point comparisons of diversity estimators for a particular sampling level. We introduce new randomization methods that incorporate sampling variability encompassing the entire length of the rarefaction curve and allow for statistical comparison of i ≥2 individual-based, sample-based, or coverage-based rarefaction curves. These methods distinguish between two distinct null hypotheses: the ecological null hypothesis (H0eco) and the biogeographical null hypothesis (H0biog). H0eco states that the i samples were drawn from a single assemblage, and any differences among them in species richness, composition, or relative abundance reflect only sampling effects. H0biog states that the i samples were drawn from assemblages that differ in their species composition but share similar species richness and species abundance distri...

Ecologists and biogeographers usually rely on a single phylogenetic tree to study evolutionary processes that affect macroecological patterns. This approach ignores the fact that each phylogenetic tree is a hypothesis about the evolutionary history of a clade, and cannot be directly observed in nature. Also, trees often leave out many extant species, or include missing species as polytomies because of a lack of information on the relationship among taxa. Still, researchers usually do not quantify the effects of phylogenetic uncertainty in ecological analyses. We propose here a novel analytical strategy to maximize the use of incomplete phylogenetic information, while simultaneously accounting for several sources of phylogenetic uncertainty that may distort statistical inferences about evolutionary processes. We illustrate the approach using a clade-wide analysis of the hummingbirds, evaluating how different sources of uncertainty affect several phylogenetic comparative analyses of trait evolution and biogeographic patterns. Although no statistical approximation can fully substitute for a complete and robust phylogeny, the method we describe and illustrate enables researchers to broaden the number of clades for which studies informed by evolutionary relationships are possible, while allowing the estimation and control of statistical error that arises from phylogenetic uncertainty. Software tools to carry out the necessary computations are offered.

Variability for a day or a seasonSpecies that experience larger seasonal climatic fluctuations are likely to be more physiologically flexible and thus likely to occur across a wider elevational range. Daily changes in temperature are also common but have rarely been considered. Chanet al.used a global data set of vertebrates to look at how these two different sets of variation affect a species' elevational distribution (see the Perspective by Perezet al.). Unexpectedly, larger daily fluctuations were associated with smaller elevational distributions. Thus, specialists are favored where daily fluctuations are dominant, whereas generalists are favored where seasonal fluctuations are the main climate influence.Science, this issue p.1437; see also p.1392

© 2017 Cambridge Philosophical Society Climate change is driving a pervasive global redistribution of the planet's species. Species redistribution poses new questions for the study of ecosystems, conservation science and human societies that require a coordinated and integrated approach. Here we review recent progress, key gaps and strategic directions in this nascent research area, emphasising emerging themes in species redistribution biology, the importance of understanding underlying drivers and the need to anticipate novel outcomes of changes in species ranges. We highlight that species redistribution has manifest implications across multiple temporal and spatial scales and from genes to ecosystems. Understanding range shifts from ecological, physiological, genetic and biogeographical perspectives is essential for informing changing paradigms in conservation science and for designing conservation strategies that incorporate changing population connectivity and advance adaptation to climate change. Species redistributions present challenges for human well-being, environmental management and sustainable development. By synthesising recent approaches, theories and tools, our review establishes an interdisciplinary foundation for the development of future research on species redistribution. Specifically, we demonstrate how ecological, conservation and social research on species redistribution can best be achieved by working across disciplinary boundaries to develop and implement solutions to climate change challenges. Future studies should therefore integrate existing and complementary scientific frameworks while incorporating social science and human-centred approaches. Finally, we emphasise that the best science will not be useful unless more scientists engage with managers, policy makers and the public to develop responsible and socially acceptable options for the global challenges arising from species redistributions.

Simulating South American biodiversityThe emergence, distribution, and extinction of species are driven by interacting factors—spatial, temporal, physical, and biotic. Rangelet al.simulated the past 800,000 years of evolution in South America, incorporating these factors into a spatially explicit dynamic model to explore the geographical generation of diversity. Their simulations, based on a paleoclimate model on a 5° latitude-longitude scale, result in shifting maps of speciation, persistence, and extinction (or cradles, museums, and graves). The simulations culminate in a striking resemblance to contemporary distribution patterns across the continent for birds, mammals, and plants—despite having no target patterns and no empirical data parameterizing them.Science, this issue p.eaar5452

Measures of niche breadth and overlap that depend on the distribution of individual among resource states (ecological categories) should be independent of the relative abundance of the species and of the number of resource states considered. Such measures should also take into account the degree of distinctness of the resource states from the point of view of the organisms concerned. An ecoassay of the distinctness of resource states may well be easier and more meaningful than measurements of physical and chemical factors. We propose that the species composition of communities utilizing different resource states may be used to develop weighting factors with which each state may be weighted in proportion to its degree of distinctness. The weighting factors are used in the development of indices of niche breadth and overlap that correct for variation in the range and distinctness of resource states and that suffer less from human subjectivity than do the measures used to date. The use of such indices and the relationship of niche overlap to competition are discussed.

In their recent critique, Qian et al. (2017) claimed that the results of structural equation modeling analysis (SEM) in Chan et al. (2016) were flawed. Here, we show that the source of the difference in their re-analysis is that Qian et al. did not follow the standard, iterative process of SEM, which allows researchers to evaluate which model offers the best account of the data in both absolute and relative senses. Here, we provide step-by-step instructions to reproduce our published results. All of Qian et al.’s concerns regarding SEM can be put to rest. Moreover, in our original paper we used three distinct statistical methods—hierarchical partitioning, SEM, and stationary bootstrap—to show that different temporal scales of environmental variability can differentially impact the elevational range size (ERS) of species. It is time to move on to probing the pressing issue of how and why climatic variability impacts ERS.

AIMS: We aim to document elevational richness patterns of geometrid moths in a globally replicated, multi-gradient setting, and to test general hypotheses on environmental and spatial effects (i.e. productivity, temperature, precipitation, area, mid-domain effect and human habitat disturbance) on these richness patterns. LOCATION: Twenty-six elevational gradients world-wide (latitudes 28° S to 51° N). METHODS: We compiled field datasets on elevational gradients for geometrid moths, a lepidopteran family, and documented richness patterns across each gradient while accounting for local undersampling of richness. Environmental and spatial predictor variables as well as habitat disturbance were used to test various hypotheses. Our analyses comprised two pathways: univariate correlations within gradients, and multivariate modelling on pooled data after correcting for overall variation in richness among different gradients. RESULTS: The majority of gradients showed midpeak patterns of richness, irrespective of climate and geographical location. The exclusion of human-affected sampling plots did not change these patterns. Support for univariate main drivers of richness was generally low, although there was idiosyncratic support for particular predictors on single gradients. Multivariate models, in agreement with univariate results, provided the strongest support for an effect of area-integrated productivity, or alternatively for an elevational area effect. Temperature and the mid-domain effect received support as weaker, modulating covariates, while precipitation-related variables had no explanatory potential. MAIN CONCLUSIONS: Despite the predicted decreasing diversity–temperature relationship in ectotherms, geometrid moths are similar to ants and salamanders as well as small mammals and ferns in having predominantly their highest diversity at mid-elevations. As in those comparative analyses, single or clear sets of drivers are elusive, but both productivity and area appear to be influential. More comparative elevational studies for various insect taxa are necessary for a more comprehensive understanding of elevational diversity and productivity.

The weaponry technology associated with Clovis and related Early Paleoindians represents the earliest well-defined evidence of humans in Pleistocene North America. We assess the technological diversity of these fluted stone points found at archaeological sites in the western and eastern halves of North America by employing statistical tools used in the quantification of ecological biodiversity. Our results demonstrate that the earliest hunters in the environmentally heterogeneous East used a more diverse set of points than those in the environmentally homogenous West. This and other evidence shows that environmental heterogeneity in the East promoted the relaxation of selective constraints on social learning and increased experimentation with point designs.

Aim To understand the causes of historical and current elevational richness patterns of Leiothrichinae babblers, a diverse and mostly endemic group of birds. Location A 5000-m elevational gradient in the Hengduan Mountains, China. Methods By means of a dated phylogenetic tree and reconstructed ancestral states, we estimated elevation-specific diversification rate, applied a new method to estimate colonization frequency and age and, for the first time, modelled historical species richness patterns that take account of temporal patterns of palaeotemperature. As explanations for current richness patterns, we assessed area, geometric constraints, temperature, precipitation, seasonality and productivity. Results The current elevational pattern of species richness is a hump-shaped curve with a peak at about 1000‐2500 m. The reconstructed palaeopatterns of species richness suggest that babblers, as a clade, first occupied the Hengduan Mountains at low to mid-elevations, although the method of ancestral state reconstruction cannot conclusively reject origins outside the current elevational distribution of the group.Diversification rates varied little along the elevational gradient, and thus cannot explain the richness pattern, but historical colonization frequency and colonization age were highly correlated with present-day species richness. Seasonality and productivity had greater power than area and geometric constraints in explaining the present-day richness pattern of babblers along the elevational gradient. Conclusions Historical and modern factors have both played important roles in shaping species richness patterns. Reconstructed historical richness patterns suggest that babblers first diversified in the Hengduan Mountains at low to mid elevations,but richness patterns almost certainly shifted substantially under changing climates of the past 10 Myr. The current richness patterns of babblers are associated with seasonality and productivity, but they are also a product of historical evolutionary and ecological dynamics. The methods we introduce for assessing historical colonization rates and past patterns of richness offer promise for understanding other elevational richness gradients.

Climate change is transforming the structure of biological communities through the geographic extension and contraction of species’ ranges. Range edges are naturally dynamic, and shifts in the location of range edges occur at different rates and are driven by different mechanisms. This leads to challenges when seeking to generalize responses among taxa and across systems. We focus on warming-related range shifts in marine systems to describe extensions and contractions as stages. Range extensions occur as a sequence of (1) arrival, (2) population increase, and (3) persistence. By contrast, range contractions occur progressively as (1) performance decline, (2) population decrease and (3) local extinction. This stage-based framework can be broadly applied to geographic shifts in any species, life-history stage, or population subset. Ideally the probability of transitioning through progressive range shift stages could be estimated from empirical understanding of the various factors influencing range shift rates. Nevertheless, abundance and occupancy data at the spatial resolution required to quantify range shifts are often unavailable and we suggest the pragmatic solution of considering observations of range shifts within a confidence framework incorporating the type, amount and quality of data. We use case studies to illustrate how diverse evidence sources can be used to stage range extensions and contractions and assign confidence that an observed range shift stage has been reached. We then evaluate the utility of trait-based risk (invasion) and vulnerability (extinction) frameworks for application in a range shift context and find inadequacies, indicating an important area for development. We further consider factors that influence rates of extension and contraction of range edges in marine habitats. Finally, we suggest approaches required to increase our capacity to observe and predict geographic range shifts under climate change.

EstimateS offers statistical tools for analyzing and comparing the diversity and composition of species assemblages, based on sampling data. The latest version computes a wide range of biodiversity statistics for both sample-based and individual-based data, including analytical rarefaction and non-parametric extrapolation, estimators of asymptotic species richness, diversity indices, Hill numbers, and (for sample-based data) measures of compositional similarity among assemblages. In the first 20 yr of its existence, EstimateS has been downloaded more than 70 000 times by users in 140 countries, who have cited it in 5000 publications in studies of taxa from microbes to mammals in every biome.

Physiological thermal-tolerance limits of terrestrial ectotherms often exceed local air temperatures, implying a high degree of thermal safety (an excess of warm or cold thermal tolerance). However, air temperatures can be very different from the equilibrium body temperature of an individual ectotherm. Here, we compile thermal-tolerance limits of ectotherms across a wide range of latitudes and elevations and compare these thermal limits both to air and to operative body temperatures (theoretically equilibrated body temperatures) of small ectothermic animals during the warmest and coldest times of the year. We show that extreme operative body temperatures in exposed habitats match or exceed the physiological thermal limits of most ectotherms. Therefore, contrary to previous findings using air temperatures, most ectotherms do not have a physiological thermal-safety margin. They must therefore rely on behavior to avoid overheating during the warmest times, especially in the lowland tropics. Likewise, species living at temperate latitudes and in alpine habitats must retreat to avoid lethal cold exposure. Behavioral plasticity of habitat use and the energetic consequences of thermal retreats are therefore critical aspects of species’ vulnerability to climate warming and extreme events.

Although many hypotheses have been proposed to explain why humans speak so many languages and why languages are unevenly distributed across the globe, the factors that shape geographical patterns of cultural and linguistic diversity remain poorly understood. Prior research has tended to focus on identifying universal predictors of language diversity, without accounting for how local factors and multiple predictors interact. Here, we use a unique combination of path analysis, mechanistic simulation modelling, and geographically weighted regression to investigate the broadly described, but poorly understood, spatial pattern of language diversity in North America. We show that the ecological drivers of language diversity are not universal or entirely direct. The strongest associations imply a role for previously developed hypothesized drivers such as population density, resource diversity, and carrying capacity with group size limits. The predictive power of this web of factors varies over space from regions where our model predicts approximately 86% of the variation in diversity, to areas where less than 40% is explained.

Quantifying and assessing changes in biological diversity are central aspects of many ecological studies, yet accurate methods of estimating biological diversity from sampling data have been elusive. Hill numbers, or the effective number of species, are increasingly used to characterize the taxonomic, phylogenetic, or functional diversity of an assemblage. However, empirical estimates of Hill numbers, including species richness, tend to be an increasing function of sampling effort and, thus, tend to increase with sample completeness. Integrated curves based on sampling theory that smoothly link rarefaction (interpolation) and prediction (extrapolation) standardize samples on the basis of sample size or sample completeness and facilitate the comparison of biodiversity data. Here we extended previous rarefaction and extrapolation models for species richness (Hill number q D, where q ¼ 0) to measures of taxon diversity incorporating relative abundance (i.e., for any Hill number q D, q . 0) and present a unified approach for both individual-based (abundance) data and sample- based (incidence) data. Using this unified sampling framework, we derive both theoretical formulas and analytic estimators for seamless rarefaction and extrapolation based on Hill numbers. Detailed examples are provided for the first three Hill numbers: q ¼ 0 (species richness), q ¼ 1 (the exponential of Shannon's entropy index), and q ¼ 2 (the inverse of Simpson's concentration index). We developed a bootstrap method for constructing confidence intervals around Hill numbers, facilitating the comparison of multiple assemblages of both rarefied and extrapolated samples. The proposed estimators are accurate for both rarefaction and short-range extrapolation. For long-range extrapolation, the performance of the estimators depends on both the value of q and on the extrapolation range. We tested our methods on simulated data generated from species abundance models and on data from large species inventories. We also illustrate the formulas and estimators using empirical data sets from biodiversity surveys of temperate forest spiders and tropical ants.

Aim To document the species richness pattern of birds in the Hengduan Mountains and to understand its causes. Location Hengduan Mountains, China. Methods Species richness of 738 breeding bird species was calculated for each 100-m elevational band along a gradient from 100 to 6000 m a.s.l. Climate data were compiled based on monthly records from 182 meteorological stations in the Hengduan Mountains from 1959 to 2004. We calculated the planimetric area, predicted richness under geometric constraints, three-year average NDVI (normalized difference vegetation index) and EVI (enhanced vegetation index) in each elevational band. Simple and multiple regression models were used to test the explanatory power of variables associated with different factors proposed to account for elevational species richness gradients. Results The elevational pattern in species richness, for all breeding birds, was hump-shaped, with the peak occurring at 800–1800 m elevation. Endemic and non-endemic species, as well as four elevational range size categories of birds, also showed the general hump-shaped patterns of species richness, but with peaks at different elevations. In most data sets, species richness correlated well with climatic and energy factors along the elevational gradients; seasonality and productivity had a strong statistical relationship with species richness of montane birds in this study, with geometric constraints contributing to richness patterns for larger-ranged species endemic to the gradient. Main conclusions We found that climatic and energy factors correlate well with the richness pattern of birds, and that on the surveyed subtropical mountain, the elevational bands with highest seasonality harbour fewer species than areas with less seasonal variation in temperature. The results, however, vary somewhat among taxonomic groups. The most diverse species groups and species with the broadest ranges have a disproportionate influence on our perception of the overall diversity pattern and its underlying explanatory factors.

Our species displays remarkable linguistic diversity. Although the uneven distribution of this diversity demands explanation, the drivers of these patterns have not been conclusively determined. We address this issue in two steps: First, we review previous empirical studies whose authors have suggested environmental, geographical, and sociocultural drivers of linguistic diversification. However, contradictory results and methodological variation make it difficult to draw general conclusions. Second, we outline a program for future research. We suggest that future analyses should account for interactions among causal factors, the lack of spatial and phylogenetic independence of the data, and transitory patterns. Recent analytical advances in biogeography and evolutionary biology, such as simulation modeling of diversity patterns, hold promise for testing four key mechanisms of language diversification proposed here: neutral change, population movement, contact, and selection. Future modeling approaches should also evaluate how the outcomes of these processes are influenced by demography, environmental heterogeneity, and time.

AimTwo fundamental questions about human language demand answers: why are so many languages spoken today and why is their geographical distribution so uneven? Although hypotheses have been proposed for centuries, the processes that determine patterns of linguistic and cultural diversity remain poorly understood. Previous studies, which relied on correlative, curve-fitting approaches, have produced contradictory results. Here we present the first application of process-based simulation modelling, derived from macroecology, to examine the distribution of human groups and their languages. LocationThe Australian continent is used as a case study to demonstrate the power of simulation modelling for identifying processes shaping the diversity and distribution of human languages. MethodsProcess-based simulation models allow investigators to hold certain factors constant in order to isolate and assess the impact of modelled processes. We tested the extent to which a minimal set of processes determines the number and spatial distribution of languages on the Australian continent. Our model made three basic assumptions based on previously proposed, but untested, hypotheses: groups fill unoccupied spaces, rainfall limits population density and groups divide after reaching a maximum population. ResultsRemarkably, this simple model accurately predicted the total number of languages (average estimate 406, observed 407), and explained 56% of spatial variation in language richness on the Australian continent. Main conclusionsOur results present strong evidence that current climatic conditions and limits to group size are important processes shaping language diversity patterns in Australia. Our study also demonstrates how simulation models from macroecology can be used to understand the processes that have shaped human cultural diversity across the globe.

Distributions of Earth's species are changing at accelerating rates, increasingly driven by humanmediated climate change. Such changes are already altering the composition of ecological communities, but beyond conservation of natural systems, how and why does this matter? We review evidence that climate-driven species redistribution at regional to global scales affects ecosystem functioning, human well-being, and the dynamics of climate change itself. Production of natural resources required for food security, patterns of disease transmission, and processes of carbon sequestration are all altered by changes in species distribution. Consideration of these effects of biodiversity redistribution is critical yet lacking in most mitigation and adaptation strategies, including the United Nation's Sustainable Development Goals.

Estimating the species, phylogenetic, and functional diversity of a community is challenging because rare species are often undetected, even with intensive sampling. The Good-Turing frequency formula, originally developed for cryptography, estimates in an ecological context the true frequencies of rare species in a single assemblage based on an incomplete sample of individuals. Until now, this formula has never been used to estimate undetected species, phylogenetic, and functional diversity. Here, we first generalize the Good-Turing formula to incomplete sampling of two assemblages. The original formula and its two-assemblage generalization provide a novel and unified approach to notation, terminology, and estimation of undetected biological diversity. For species richness, the Good-Turing framework offers an intuitive way to derive the non-parametric estimators of the undetected species richness in a single assemblage, and of the undetected species shared between two assemblages. For phylogenetic diversity, the unified approach leads to an estimator of the undetected Faith's phylogenetic diversity (PD, the total length of undetected branches of a phylogenetic tree connecting all species), as well as a new estimator of undetected PD shared between two phylogenetic trees. For functional diversity based on species traits, the unified approach yields a new estimator of undetected Walker et al.'s functional attribute diversity (FAD, the total species-pairwise functional distance) in a single assemblage, as well as a new estimator of undetected FAD shared between two assemblages. Although some of the resulting estimators have been previously published (but derived with traditional mathematical inequalities), all taxonomic, phylogenetic, and functional diversity estimators are now derived under the same framework. All the derived estimators are theoretically lower bounds of the corresponding undetected diversities; our approach reveals the sufficient conditions under which the estimators are nearly unbiased, thus offering new insights. Simulation results are reported to numerically verify the performance of the derived estimators. We illustrate all estimators and assess their sampling uncertainty with an empirical dataset for Brazilian rain forest trees. These estimators should be widely applicable to many current problems in ecology, such as the effects of climate change on spatial and temporal beta diversity and the contribution of trait diversity to ecosystem multi-functionality. This article is protected by copyright. All rights reserved.

The extinction of a single species is rarely an isolated event. Instead, dependent parasites, commensals, and mutualist partners (affiliates) face the risk of coextinction as their hosts or partners decline and fail. Species interactions in ecological networks can transmit the effects of primary extinctions within and between trophic levels, causing secondary extinctions and extinction cascades. Documenting coextinctions is complicated by ignorance of host specificity, limitations of historical collections, incomplete systematics of affiliate taxa, and lack of experimental studies. Host shifts may reduce the rate of coextinctions, but they are poorly understood. In the absence of better empirical records of coextinctions, statistical models estimate the rates of past and future coextinctions, and based on primary extinctions and interactions among species, network models explore extinction cascades. Models predict and historical evidence reveals that the threat of coextinction is influenced by both host and affiliate traits and is exacerbated by other threats, including habitat loss, climate change, and invasive species.

Aims In ecology and conservation biology, the number of species counted in a biodiversity study is a key metric but is usually a biased underestimate of total species richness because many rare species are not detected. Moreover, comparing species richness among sites or samples is a statistical challenge because the observed number of species is sensitive to the number of individuals counted or the area sampled. For individual-based data, we treat a single, empirical sample of species abundances from an investigator-defined species assemblage or community as a reference point for two estimation objectives under two sampling models: estimating the expected number of species (and its unconditional variance) in a random sample of (i) a smaller number of individuals (multinomial model) or a smaller area sampled (Poisson model) and (ii) a larger number of individuals or a larger area sampled. For sample-based incidence (presence–absence) data, under a Bernoulli product model, we treat a single set of species incidence frequencies as the reference point to estimate richness for smaller and larger numbers of sampling units. Methods The first objective is a problem in interpolation that we address with classical rarefaction (multinomial model) and Coleman rarefaction (Poisson model) for individual-based data and with sample-based rarefaction (Bernoulli product model) for incidence frequencies. The second is a problem in extrapolation that we address with sampling-theoretic predictors for the number of species in a larger sample (multinomial model), a larger area (Poisson model) or a larger number of sampling units (Bernoulli product model), based on an estimate of asymptotic species richness. Although published methods exist for many of these objectives, we bring them together here with some new estimators under a unified statistical and notational framework. This novel integration of mathematically distinct approaches allowed us to link interpolated (rarefaction) curves and extrapolated curves to plot a unified species accumulation curve for empirical examples. We provide new, unconditional variance estimators for classical, individual-based rarefaction and for Coleman rarefaction, long missing from the toolkit of biodiversity measurement. We illustrate these methods with datasets for tropical beetles, tropical trees and tropical ants.

Mountains are centres of global biodiversity, endemism and threatened species. Elevational gradients present opportunities for species currently living near their upper thermal limits to track cooler temperatures upslope in warming climates, but only if changes in precipitation are sufficiently in step with temperature. We model local population extirpation risk for a range of temperature and precipitation scenarios over the next 100 years for 16 848 vertebrate species populations distributed along 156 elevational gradients. Average population extirpation risks due to warming alone were < 5%, but increased 10-fold, on average, when changes in precipitation were also considered. Under the driest scenarios (minimum predicted precipitation), local extirpation risks increased sharply (50‐60%) and were especially worrisome for hydrophilic amphibians and montane Latin America (c. 80%). Realistic assessment of risks urgently requires improved monitoring of precipitation, better regional precipitation models and more research on the effects of changes in precipitation on montane distributions.

Assessing species survival status is an essential component of conservation programs. We devised a new statistical method for estimating the probability of species persistence from the temporal sequence of collection dates of museum specimens. To complement this approach, we developed quantitative stopping rules for terminating the search for missing or allegedly extinct species. These stopping rules are based on survey data for counts of co-occurring species that are encountered in the search for a target species. We illustrate both these methods with a case study of the Ivory-billed Woodpecker (Campephilus principalis), long assumed to have become extinct in the United States in the 1950s, but reportedly rediscovered in 2004. We analyzed the temporal pattern of the collection dates of 239 geo-referenced museum specimens collected throughout the southeastern United States from 1853 to 1932 and estimated the probability of persistence in 2011 as

We provide a cross-taxon and historical analysis of what makes tropical forest species vulnerable to extinction. Several traits have been important for species survival in the recent and distant geological past, including seed dormancy and vegetative growth in plants, small body size in mammals, and vagility in insects. For major past catastrophes, such as the five mass extinction events, large range size and vagility or dispersal were key to species survival. Traits that make some species more vulnerable to extinction are consistent across time scales. Terrestrial organisms, particularly animals, are more extinction prone than marine organisms. Plants that persist through dramatic changes often reproduce vegetatively and possess mechanisms of die back. Synergistic interactions between current anthropogenic threats, such as logging, fire, hunting, pests and diseases, and climate change are frequent. Rising temperatures threaten all organisms, perhaps particularly tropical organisms adapted to small temperature ranges and isolated by distance from suitable future climates. Mutualist species and trophic specialists may also be more threatened because of such range-shift gaps. Phylogenetically specialized groups may be collectively more prone to extinction than generalists. Characterization of tropical forest species' vulnerability to anthropogenic change is constrained by complex interactions among threats and by both taxonomic and ecological impediments, including gross undersampling of biotas and poor understanding of the spatial patterns of taxa at all scales.

Biodiversity sampling is labor intensive, and a substantial fraction of a biota is often represented by species of very low abundance, which typically remain undetected by biodiversity surveys. Statistical methods are widely used to estimate the asymptotic number of species present, including species not yet detected. Additional sampling is required to detect and identify these species, but richness estimators do not indicate how much sampling effort (additional individuals or samples) would be necessary to reach the asymptote of the species accumulation curve. Here we develop the first statistically rigorous nonparametric method for estimating the minimum number of additional individuals, samples, or sampling area required to detect any arbitrary proportion (including 100%) of the estimated asymptotic species richness. The method uses the Chao1 and Chao2 nonparametric estimators of asymptotic richness, which are based on the frequencies of rare species in the original sampling data. To evaluate the performance of the proposed method, we randomly subsampled individuals or quadrats from two large biodiversity inventories (light trap captures of Lepidoptera in Great Britain and censuses of woody plants on Barro Colorado Island [BCI], Panama). The simulation results suggest that the method performs well but is slightly conservative for small sample sizes. Analyses of the BCI results suggest that the method is robust to nonindependence arising from small-scale spatial aggregation of species occurrences. When the method was applied to seven published biodiversity data sets, the additional sampling effort necessary to capture all the estimated species ranged from 1.05 to 10.67 times the original sample (median approximately equal to 2.23). Substantially less effort is needed to detect 90% of the species (0.33-1.10 times the original effort; median approximately equal to 0.80). An Excel spreadsheet tool is provided for calculating necessary sampling effort for either abundance data or replicated incidence data.