Guillaume Latombe

Multi‐site generalised dissimilarity modelling: using zeta diversity to differentiate drivers of turnover in rare and widespread species

Summary Generalised dissimilarity modelling (GDM) applies pairwise beta diversity as a measure of species turnover with the purpose of explaining changes in species composition under changing environments or along environmental gradients. Beta diversity only captures turnover across pairs of sites and, therefore, disproportionately represents turnover in rare species across communities. By contrast, zeta diversity, the average number of shared species across multiple sites, captures the full spectrum of rare, intermediate and widespread species as they contribute differently to compositional turnover. We show how integrating zeta diversity into GDMs (which we term multi-site generalised dissimilarity modelling, MS-GDM), provides a more information rich approach to modelling how communities respond to environmental variation and change. We demonstrate the value of including zeta diversity in biodiversity assessment and modelling using BirdLife Australia Atlas data. Zeta diversity values for different numbers of sites (the order of zeta) are regressed against environmental differences and distance using two kinds of regressions: shape constrained additive models and a combination of I-splines and generalised linear models. Applying MS-GDM to different orders of zeta revealed shifts in the importance of environmental variables in explaining species turnover, varying with the order of zeta and thus with the level of co-occurrence of the species and, by extension, their commonness and rarity. In particular, precipitation gradients emerged as drivers in the turnover of rare species, whereas temperature gradients were more important drivers of turnover in widespread species. Appreciation of the factors that drive compositional turnover across multiple sites is necessary for accommodating the full spectrum of compositional turnover across rare to common species. This extends beyond understanding drivers for pairwise beta diversity only. MS-GDM provides a valuable addition to the toolkit of GDM, with further potential for survey gap analysis and prediction of species composition in unsampled sites.

Beyond the continuum: a multi-dimensional phase space for neutral–niche community assembly

Neutral and niche processes are generally considered to interact in natural communities along a continuum, exhibiting community patterns bounded by pure neutral and pure niche processes. The continuum concept uses niche separation, an attribute of the community, to test the hypothesis that communities are bounded by pure niche or pure neutral conditions. It does not accommodate interactions via feedback between processes and the environment. By contrast, we introduce the Community Assembly Phase Space (CAPS), a multi-dimensional space that uses community processes (such as dispersal and niche selection) to define the limiting neutral and niche conditions and to test the continuum hypothesis. We compare the outputs of modelled communities in a heterogeneous landscape, assembled by pure neutral, pure niche and composite processes. Differences in patterns under different combinations of processes in CAPS reveal hidden complexity in neutral–niche community dynamics. The neutral–niche continuum only holds for strong dispersal limitation and niche separation. For weaker dispersal limitation and niche separation, neutral and niche processes amplify each other via feedback with the environment. This generates patterns that lie well beyond those predicted by a continuum. Inferences drawn from patterns about community assembly processes can therefore be misguided when based on the continuum perspective. CAPS also demonstrates the complementary information value of different patterns for inferring community processes and captures the complexity of community assembly. It provides a general tool for studying the processes structuring communities and can be applied to address a range of questions in community and metacommunity ecology.

The application of zeta diversity as a continuous measure of compositional change in ecology

Zeta diversity provides the average number of shared species across n sites (or shared operational taxonomic units (OTUs) across n cases). It quantifies the variation in species composition of multiple assemblages in space and time to capture the contribution of the full suite of narrow, intermediate and wide-ranging species to biotic heterogeneity. Zeta diversity was proposed for measuring compositional turnover in plant and animal assemblages, but is equally relevant for application to any biological system that can be characterised by a row by column incidence matrix. Here we illustrate the application of zeta diversity to explore compositional change in empirical data, and how observed patterns may be interpreted. We use 10 datasets from a broad range of scales and levels of biological organisation – from DNA molecules to microbes, plants and birds – including one of the original data sets used by R.H. Whittaker in the 1960’s to express compositional change and distance decay using beta diversity. The applications show (i) how different sampling schemes used during the calculation of zeta diversity may be appropriate for different data types and ecological questions, (ii) how higher orders of zeta may in some cases better detect shifts, transitions or periodicity, and importantly (iii) the relative roles of rare versus common species in driving patterns of compositional change. By exploring the application of zeta diversity across this broad range of contexts, our goal is to demonstrate its value as a tool for understanding continuous biodiversity turnover and as a metric for filling the empirical gap that exists on spatial or temporal change in compositional diversity.

zetadiv: an R package for computing compositional change across multiple sites, assemblages or cases

Spatial variation in compositional diversity, or species turnover, is necessary for capturing the components of heterogeneity that constitute biodiversity. However, no incidence-based metric of pairwise species turnover can calculate all components of diversity partitioning. Zeta (ζ) diversity, the mean number of species shared by any given number of sites or assemblages, captures all diversity components produced by assemblage partitioning. zetadiv is an R package for analysing and measuring compositional change for occurrence data using zeta diversity. Four types of analyses are performed on bird composition data in Australia: (i) decline in zeta diversity; (ii) distance decay; (iii) multi-site generalised dissimilarity modelling; and (iv) hierarchical scaling. Some analyses, such as the zeta decline, are specific to zeta diversity, whereas others, such as distance decay, are commonly applied to beta diversity, and have been adapted using zeta diversity to differentiate the contribution of common and rare species to compositional change. Highlights An R package to analyse compositional change using zeta diversity is presented. Zeta diversity is the mean number of species shared by any number of assemblages Zeta diversity captures all diversity components produced by assemblage partitioning Analyses relate zeta diversity to space, environment and spatial scale Analyses differentiate the contribution of rare and common species to biodiversity

Drivers of species turnover vary with species commonness for native and alien plants with different residence times.

Communities comprising alien species with different residence times are natural experiments allowing the assessment of drivers of community assembly over time. Stochastic processes (such as dispersal and fluctuating environments) should be the dominant factors structuring communities of exotic species with short residence times. In contrast, communities should become more similar, or systematically diverge, if they contain exotics with increasing resident times, due to the increasing importance of deterministic processes (such as environmental filtering). We use zeta diversity (the number of species shared by multiple assemblages) to explore the relationship between the turnover of native species and two categories of alien species with different residence times (archaeophytes [introduced between 4000 BC and 1500 AD] and neophytes [introduced after 1500 AD]) in a network of nature reserves in central Europe. By considering multiple assemblages simultaneously, zeta diversity allows us to determine the contribution of rare and widespread species to turnover. Specifically, we explore the relative effects of assembly processes representing isolation by distance, environmental filtering, and environmental stochasticity (fluctuating environments) on zeta diversity using Multi-Site Generalized Dissimilarity Modelling (MS-GDM). Four clusters of results emerged. First, stochastic processes for structuring plant assemblages decreased in importance with increasing residence time. Environmental stochasticity only affected species composition for neophytes, offering possibilities to predict the spread debt of recent invasions. Second, native species turnover was well explained by environmental filtering and isolation by distance, although these factors did not explain the turnover of archaeophytes and neophytes. Third, native and alien species compositions were only correlated for rare species, whereas turnover in widespread alien species was surprisingly unrelated to the composition of widespread native species. Site-specific approaches would therefore be more appropriate for the monitoring and management of rare alien species, whereas species-specific approaches would suit widespread species. Finally, the size difference of nature reserves influences not only native species richness, but also their richness-independent turnover. A network of reserves must therefore be designed and managed using a variety of approaches to enhance native diversity, while controlling alien species with different residence times and degrees of commonness.

Comparison of spatial downscaling methods of general circulation models to study climate variability during the Last Glacial Maximum

The extent to which climate conditions influenced the spatial distribution of hominin populations in the past is highly debated. General Circulation Models (GCMs) and archaeological data have been used to address this issue. Most GCMs are 15 not currently capable of simulating past surface climate conditions with sufficiently detailed spatial resolution to distinguish areas of potential hominin habitat, however. In this paper we propose a Statistical Downscaling Method (SDM) for increasing the resolution of climate model outputs in a computationally efficient way. Our method uses a generalized additive model (GAM), calibrated over present-day climatology data, to statistically downscale temperature and precipitation time series from the outputs of a GCM simulating the climate of the Last Glacial Maximum (19-23,000 BP) over Western Europe. Once the 20 SDM is calibrated, we first interpolate the coarse-scale GCM outputs to the final resolution and then use the GAM to compute surface air temperature and precipitation levels using these interpolated GCM outputs and fine resolution geographical variables such as topography and distance from an ocean. The GAM acts as a transfer function, capturing non-linear relationships between variables at different spatial scales and correcting for the GCM biases. We tested three different techniques for the first interpolation of GCM output: bilinear, bicubic, and kriging. The resulting SDMs were evaluated by 25 comparing downscaled temperature and precipitation at local sites with paleoclimate reconstructions based on paleoclimate archives (archaeozoological and palynological data) and the impact of the interpolation technique on patterns of variability was explored. The SDM based on kriging interpolation, providing the best accuracy, was then validated on present-day data outside of the calibration period. Our results show that the downscaled temperature and precipitation values are in good agreement with paleoclimate reconstructions at local sites, and that our method for producing fine-grained paleoclimate 30 simulations is therefore suitable for conducting paleo-anthropological research. It is nonetheless important to calibrate the GAM on a range of data encompassing the data to be downscaled. Otherwise, the SDM is likely to over-correct the coarsegrain data. In addition, the bilinear and bicubic interpolation techniques were shown to distort either the temporal variability