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Extremophiles exist among all three domains of life; however, physiological mechanisms for surviving harsh environmental conditions differ among Bacteria, Archaea and Eukarya. Consequently, we expect that domain-specific variation of diversity and community assembly patterns exist along environmental gradients in extreme environments. We investigated inter-domain community compositional differences along a high-elevation salinity gradient in the McMurdo Dry Valleys, Antarctica. Conductivity for 24 soil samples collected along the gradient ranged widely from 50 to 8355 µS cm -1 . Taxonomic richness varied among domains, with a total of 359 bacterial, 2 archaeal, 56 fungal, and 69 non-fungal eukaryotic operational taxonomic units (OTUs). Richness for bacteria, archaea, fungi, and non-fungal eukaryotes declined with increasing conductivity (all P < 0.05). Principal coordinate ordination analysis (PCoA) revealed significant (ANOSIM R = 0.97) groupings of low/high salinity bacterial OTUs, while OTUs from other domains were not significantly clustered. Bacterial beta diversity was unimodally distributed along the gradient and had a nested structure driven by species losses, whereas in fungi and non-fungal eukaryotes beta diversity declined monotonically without strong evidence of nestedness. Thus, while increased salinity acts as a stressor in all domains, the mechanisms driving community assembly along the gradient differ substantially between the domains., (© 2022. The Author(s), under exclusive licence to Springer Japan KK, part of Springer Nature.)

Aim: To evaluate the role of seasonal and non‐seasonal productivity fluctuations in global patterns of species richness. Location: Worldwide. Time period: 2000–2017. Major taxa studied: Amphibians, birds, mammals. Methods: We analysed time series of monthly variation of the Normalized Difference Vegetation Index (NDVI), a surrogate of primary productivity, within c. 100 km × 100 km cells across all continents, estimating the mean, periodic seasonal variation and aperiodic unpredictable fluctuations of the NDVI in these cells. We then explored the relationships between mean NDVI and the components of its temporal variation and evaluated the independent effects of the above‐mentioned variables on species richness in the three vertebrate groups by means of variation partitioning. Results: There is a hump‐shaped relationship between mean productivity and variation in productivity, so that temporal variation in productivity is lowest in regions with minimum and maximum values of mean productivity. Although mean productivity is a strong determinant of species richness, both seasonal and non‐seasonal productivity variation significantly affect the species richness of all studied taxa when accounting for mean productivity. However, the direction of these effects differs between regions differing in the mean productivity level. High variation in productivity has a negative effect on species richness in regions with moderate to high productivity levels, whereas species richness is higher in arid regions with high variation in productivity. Main conclusions: Species richness is affected by temporal variation in productivity, but these effects differ regionally. In productive areas, high environmental stochasticity may increase population extinction rates, whereas arid regions probably benefit from resource fluctuations that promote species coexistence. Our results indicate that contemporary changes in patterns of temporal resource fluctuations may affect future global patterns of biological diversity on Earth. [ABSTRACT FROM AUTHOR]

The idea that the number of species within an area is limited by a specific capacity of that area to host species is old yet controversial. Here, we show that the concept of carrying capacity for species richness can be as useful as the analogous concept in population biology. Many lines of empirical evidence indicate the existence of limits of species richness, at least at large spatial and phylogenetic scales. However, available evidence does not support the idea of diversity limits based on limited niche space; instead, carrying capacity should be understood as a stable equilibrium of biodiversity dynamics driven by diversity‐dependent processes of extinction, speciation and/or colonization. We argue that such stable equilibria exist even if not all resources are used and if increasing species richness increases the ability of a community to use resources. Evaluating the various theoretical approaches to modelling diversity dynamics, we conclude that a fruitful approach for macroecology and biodiversity science is to develop theory that assumes that the key mechanism leading to stable diversity equilibria is the negative diversity dependence of per‐species extinction rates, driven by the fact that population sizes of species must decrease with an increasing number of species owing to limited energy availability. The recently proposed equilibrium theory of biodiversity dynamics is an example of such a theory, which predicts that equilibrium species richness (i.e., carrying capacity) is determined by the interplay of the total amount of available resources, the ability of communities to use those resources, environmental stability that affects extinction rates, and the factors that affect speciation and colonization rates. We argue that the diversity equilibria resulting from these biodiversity dynamics are first‐order drivers of large‐scale biodiversity patterns, such as the latitudinal diversity gradient. [ABSTRACT FROM AUTHOR]

Understanding the pattern of species distribution and the underlying mechanism is essential for conservation planning. Several climatic variables determine the species diversity, and the dependency of species on climate motivates ecologists and bio-geographers to explain the richness patterns along with elevation and environmental correlates. We used interpolated elevational distribution data to examine the relative importance of climatic variables in determining the species richness pattern of 26 species of gymnosperms in the longest elevation gradients in the world. Thirteen environmental variables were divided into three predictors set representing each hypothesis model (energy-water, physical-tolerance, and climatic-seasonality); to explain the species richness pattern of gymnosperms along the elevational gradient. We performed generalized linear models and variation partitioning to evaluate the relevant role of environmental variables on species richness patterns. Our findings showed that the gymnosperms’ richness formed a hump-shaped distribution pattern. The individual effect of energy-water predictor set was identified as the primary determinant of species richness. While, the joint effects of energy-water and physical-tolerance predictors have explained highest variations in gymnosperm distribution. The multiple environmental indicators are essential drivers of species distribution and have direct implications in understanding the effect of climate change on the species richness pattern.

Ecology and biodiversity research are underpinned by species richness patterns and their environmental drivers. However, a key topic in this discussion is the accuracy of these patterns which are greatly dependent on species detection probabilities. Due to variations in detection of species, true ecological patterns may be distorted. This is particularly true for subtidal macro‐infaunal communities. We tested three hypothesized relationships between marine benthic macrofaunal diversity and depth using species richness per site estimated with a capture–recapture heterogeneity model that accounts for variable detection probabilities. These metrics were based on data from 42 replicated sites across the continental shelf of the Southern Benguela. Average detection probability decreased with greater depth but species richness increased along the same depth gradient. The conflation of these trends in observed diversity data resulted in a positively near‐linear depth–diversity relationship, while accounting for variable species detection revealed a much stronger relationship. Ignoring species detection in ecosystems with imperfect detection could therefore distort species richness patterns, which has implications for ecological theory, management and conservation. [ABSTRACT FROM AUTHOR]

Aim It has been found that species richness can be a peaked function of natural area, meaning that conversion of natural cover can increase species numbers. Here, we test whether regional richness-environment relationships can predict local change in species richness. Location The greater park ecosystem of Thousand Islands National Park ( TINP ecosystem), Ontario, Canada. Methods We evaluated change in bird richness in 85 100-km2 sites, censused from 1981 to 1985 and from 2001 to 2005. We related change in richness over 20 years to change in natural land cover for sites having initially less or greater than half natural area to determine whether richness changes in the direction predicted. We also assessed the extent to which local effects (site-level extinction and colonization based on species-specific habitat amount and neighbourhood occupancy by conspecifics) affected the predictability of species richness response to environmental change. Results Local effects predicted bird richness changes through time far better than natural area change (adjusted r2 = 0.56 vs. adjusted r2 = 0.13). Species richness did not consistently respond to change in natural area in the directions predicted by the broader spatial richness-environment relationship, tending to increase with decrease in natural area regardless of the initial amount of natural area. The observed natural area changes (+0.7 km2 to −4.4 km2) were small relative to the changes in richness (+60 species to −37 species), likely impacting our ability to detect a response. Main conclusions The importance of local effects observed here in determining site-level species presence and consequently species richness has implications for species monitoring and the use of species richness as a measure of the avian response to land cover modification in Ontario. Detecting richness responses to environmental changes may often be challenged by low 'signal-to-noise' ratios and highlights the benefits of long-term species monitoring. [ABSTRACT FROM AUTHOR]

Aim The general dynamic model ( GDM) of oceanic island biogeography integrates rates of immigration, speciation and extinction in relation to a humped trajectory of island area, species carrying capacity and topographic complexity through time, based on a simplified island ontogeny. In practice, many islands have more complex ontogenies, featuring surfaces of varying age. Here, we extend the GDM to apply at a local scale within islands, and test the predictions analytically within individual islands. Location El Hierro, La Palma and Tenerife (Canary Islands). Methods Following the GDM logic, we derive predictions for the distributions and richness of single island endemics ( SIEs) across island landscapes of different age. We test these predictions by means of generalized linear models and binominal tests using gridded species occurrence data for vascular plant SIE species and a set of climatic, topographic and terrain age variables. We also examined phylogenetic divergence times for a subset of endemic lineages. Results Geological age, in interaction with slope, and topographic variables, best explained SIE richness at the landscape scale. About 70% of SIEs had ranges strongly biased to, or largely restricted to old terrain. Available phylogenetic divergence times of SIEs of radiated plant lineages suggested an origin on the older parts of the islands. Metrics of anthropogenic disturbance and habitat availability were unrelated to the observed SIE pattern. Main conclusions Our findings support the hypothesis that SIEs have evolved and accumulated on older and topographically complex terrain, while colonization processes predominate on the youngest parts. These results imply that evolutionary processes shape species distributions at the landscape scale within islands. This opens the perspective of extending the GDM framework to understand processes at a local scale within individual islands. [ABSTRACT FROM AUTHOR]

Abstract: Knowledge about spatial biodiversity patterns is a basic criterion for reserve network design. Although herbarium collections hold large quantities of information, the data are often scattered and cannot supply complete spatial coverage. Alternatively, herbarium data can be used to fit species distribution models and their predictions can be used to provide complete spatial coverage and derive species richness maps. Here, we build on previous effort to propose an improved compositionalist framework for using species distribution models to better inform conservation management. We illustrate the approach with models fitted with six different methods and combined using an ensemble approach for 408 plant species in a tropical and megadiverse country (Ecuador). As a complementary view to the traditional richness hotspots methodology, consisting of a simple stacking of species distribution maps, the compositionalist modelling approach used here combines separate predictions for different pools of species to identify areas of alternative suitability for conservation. Our results show that the compositionalist approach better captures the established protected areas than the traditional richness hotspots strategies and allows the identification of areas in Ecuador that would optimally complement the current protection network. Further studies should aim at refining the approach with more groups and additional species information. [Copyright &y& Elsevier]

This article aims to test for and explore spatial nonstationarity in the relationship between avian species richness and a set of explanatory variables to further the understanding of species diversity variation. Sub-Saharan Africa. Geographically weighted regression was used to study the relationship between species richness of the endemic avifauna of sub-Saharan Africa and a set of perceived environmental determinants, comprising the variables of temperature, precipitation and normalized difference vegetation index. The relationships between species richness and the explanatory variables were found to be significantly spatially variable and scale-dependent. At local scales > 90% of the variation was explained, but this declined at coarser scales, with the greatest sensitivity to scale variation evident for narrow ranging species. The complex spatial pattern in regression model parameter estimates also gave rise to a spatial variation in scale effects. Relationships between environmental variables are generally assumed to be spatially stationary and conventional, global, regression techniques are therefore used in their modelling. This assumption was not satisfied in this study, with the relationships varying significantly in space. In such circumstances the average impression provided by a global model may not accurately represent conditions locally. Spatial nonstationarity in the relationship has important implications, especially for studies of species diversity patterns and their scaling. [ABSTRACT FROM AUTHOR]

Abstract The suggestion that spatial gradients in species richness are influenced by geometric constraints resulting in the mid-domain effect has been investigated by null models. The technical aspects of making such null models are well explored, but the implicit ecological assumptions behind these models are less explored. Four ecological models that all assume that species ranges are constrained by hard boundaries are made: evolutionary model, source-sink model, dynamic-environment model, and range-size model. These models give different predictions that make it possible to separate the models from each other, and from a model that assumes that hard boundaries are not important. [ABSTRACT FROM AUTHOR]

The biodiversity data typically available for fitting distributional models in the tropics come from museum and scientific collections which are often incomplete and prone to sampling and environmental biases. Nevertheless, most studies undertaken in tropical regions assume that collection data offers a satisfactory environmental coverage without any quantitative assessment. In this study, we investigate the effects of differences in environmental bias and coverage provided by distributional data when aggregated into different grid cell sizes, on the performance of species richness-environment models and predictions. We use an extensive data compilation, including national and regional collections, on the distribution of amphibians, reptiles and fishes in the hydrologic region of the Usumacinta River as a case study. General additive models and environmental variables are used to construct predictive models at 40, 20, 10 and 5 km grid resolutions, based on well-sampled cells. The best multivariate models included nonparametric interaction terms for the effects of precipitation and temperature and suggested an altitudinal shift in the relative importance of energy and water in determining the distribution of species richness. For fishes, geomorphology accounted for fine scale variation in species richness along the hydrologic network, indicated by peaks in species diversity at the junction of the major rivers where major accumulation of water and sediments occurs. For all taxonomic groups, we found that sampling biases deviated most from the mean bias at the extremes of gradients accounting for important environmental factors. The pattern of environmental bias changed with grid size, with the form and amount of change being case-specific. Biases affected distribution predictions when compared with unbiased datasets. Moreover, not all models resulted best at coarser resolution as it is commonly assumed. Our results demonstrate that bias in the available data must be evaluated before mapping biodiversity distributions, irrespective of the choice of scale. • Environmental bias is related to major discrepancies found in predictive models. • Sampling bias is most heavily concentrated at the extremes of environmental gradients. • The pattern of environmental bias change with grid size with change being case-specific. • Predictive models not always result best at coarser resolution as it is commonly assumed. • Bias must be evaluated before mapping biodiversity irrespective of the choice of scale. [ABSTRACT FROM AUTHOR]

Understanding the pattern of species distribution and the underlying mechanism is essential for conservation planning. Several climatic variables determine the species diversity, and the dependency of species on climate motivates ecologists and bio-geographers to explain the richness patterns along with elevation and environmental correlates. We used interpolated elevational distribution data to examine the relative importance of climatic variables in determining the species richness pattern of 26 species of gymnosperms in the longest elevation gradients in the world. Thirteen environmental variables were divided into three predictors set representing each hypothesis model (energy-water, physical-tolerance, and climatic-seasonality); to explain the species richness pattern of gymnosperms along the elevational gradient. We performed generalized linear models and variation partitioning to evaluate the relevant role of environmental variables on species richness patterns. Our findings showed that the gymnosperms' richness formed a hump-shaped distribution pattern. The individual effect of energy-water predictor set was identified as the primary determinant of species richness. While, the joint effects of energy-water and physical-tolerance predictors have explained highest variations in gymnosperm distribution. The multiple environmental indicators are essential drivers of species distribution and have direct implications in understanding the effect of climate change on the species richness pattern. [ABSTRACT FROM AUTHOR]