The relationship between remotely-sensed spectral heterogeneity and bird diversity is modulated by landscape type.

To identify areas of high biodiversity and prioritize conservation efforts, it is crucial to understand the drivers of species richness patterns and their scale dependence. While classified land cover products are commonly used to explain bird species richness, recent studies suggest that unclassified remote-sensed images can provide equally good or better results. In our study, we aimed to investigate whether unclassified multispectral data from Landsat 8 can replace image classification for bird diversity modeling. Moreover, we also tested the Spectral Variability Hypothesis. Using the Atlas of Breeding Birds in the Czech Republic 2014-2017, we modeled species richness at two spatial resolutions of approx. 131 km2 (large squares) and 8 km2 (small squares). As predictors of the richness, we assessed 1) classified land cover data (Corine Land Cover 2018 database), 2) spectral heterogeneity (computed in three ways) and landscape composition derived from unclassified remote-sensed reflectance and vegetation indices. Furthermore, we integrated information about the landscape types (expressed by the most prevalent land cover class) into models based on unclassified remote-sensed data to investigate whether the landscape type plays a role in explaining bird species richness. We found that unclassified remote-sensed data, particularly spectral heterogeneity metrics, were better predictors of bird species richness than classified land cover data. The best results were achieved by models that included interactions between the unclassified data and landscape types, indicating that relationships between bird diversity and spectral heterogeneity vary across landscape types. Our findings demonstrate that spectral heterogeneity derived from unclassified multispectral data is effective for assessing bird diversity across the Czech Republic. When explaining bird species richness, it is important to account for the type of landscape and carefully consider the significance of the chosen spatial scale.

SPARSE 1.0: a template for databases of species inventories, with an open example of Czech birds.

Here, we introduce SPARSE (acronym for "SPecies AcRoss ScalEs"), a simple and portable template for databases that can store data on species composition derived from ecological inventories, surveys and checklists, with emphasis on metadata describing sampling effort and methods. SPARSE can accommodate resurveys and time series and data from different spatial scales, as well as complex sampling designs. SPARSE focuses on inventories that report multiple species for a given site, together with sampling methods and effort, which can be used in statistical models of true probability of occurrence of species. SPARSE is spatially explicit and can accommodate nested spatial structures from multiple spatial scales, including sampling designs where multiple sites within a larger area have been surveyed and the larger area can again be nested in an even larger region. Each site in SPARSE is represented either by a point, line (for transects) or polygon, stored in an ESRI shapefile. SPARSE implements a new combination of our own field definitions with Darwin Core biodiversity data standard and its Humboldt core extension. The use of Humboldt core also makes SPARSE suitable for biodiversity data with temporal replication. We provide an example use of the SPARSE framework by digitising data on birds from the Czech Republic, from 348 sites and 524 sampling events, with 15,969 unique species-per-event observations of presence, abundance or population density. To facilitate use without the need for a high-level database expertise, the Czech bird example is implemented as MS Access .accdb file, but can be ported to other database engines. The example of Czech birds complements other bird datasets from the Czech Republic, specifically the four gridded national atlases and the breeding bird survey which cover a similar temporal extent, but different locations and spatial scales.

All together now: Limitations and recommendations for the simultaneous analysis of all eukaryotic soil sequences.

The soil environment contains a large, but historically underexplored, reservoir of biodiversity. Sequencing prokaryotic marker genes has become commonplace for the discovery and characterization of soil bacteria and archaea. Increasingly, this approach is also applied to eukaryotic marker genes to characterize the diversity and distribution of soil eukaryotes. However, understanding the properties and limitations of eukaryotic marker sequences is essential for correctly analysing, interpreting, and synthesizing the resulting data. Here, we illustrate several biases from sequencing data that affect measurements of biodiversity that arise from variation in morphology, taxonomy and phylogeny between organisms, as well as from sampling designs. We recommend analytical approaches to overcome these limitations, and outline how the benchmarking and standardization of sequencing protocols may improve the comparability of the data.

Data Integration for Large-Scale Models of Species Distributions.

With the expansion in the quantity and types of biodiversity data being collected, there is a need to find ways to combine these different sources to provide cohesive summaries of species' potential and realized distributions in space and time. Recently, model-based data integration has emerged as a means to achieve this by combining datasets in ways that retain the strengths of each. We describe a flexible approach to data integration using point process models, which provide a convenient way to translate across ecological currencies. We highlight recent examples of large-scale ecological models based on data integration and outline the conceptual and technical challenges and opportunities that arise.

Essential biodiversity variables for mapping and monitoring species populations.

Species distributions and abundances are undergoing rapid changes worldwide. This highlights the significance of reliable, integrated information for guiding and assessing actions and policies aimed at managing and sustaining the many functions and benefits of species. Here we synthesize the types of data and approaches that are required to achieve such an integration and conceptualize 'essential biodiversity variables' (EBVs) for a unified global capture of species populations in space and time. The inherent heterogeneity and sparseness of raw biodiversity data are overcome by the use of models and remotely sensed covariates to inform predictions that are contiguous in space and time and global in extent. We define the species population EBVs as a space-time-species-gram (cube) that simultaneously addresses the distribution or abundance of multiple species, with its resolution adjusted to represent available evidence and acceptable levels of uncertainty. This essential information enables the monitoring of single or aggregate spatial or taxonomic units at scales relevant to research and decision-making. When combined with ancillary environmental or species data, this fundamental species population information directly underpins a range of biodiversity and ecosystem function indicators. The unified concept we present links disparate data to downstream uses and informs a vision for species population monitoring in which data collection is closely integrated with models and infrastructure to support effective biodiversity assessment.

Global patterns and drivers of tree diversity integrated across a continuum of spatial grains.

Controversy remains over what drives patterns in the variation of biodiversity across the planet. The resolution is obscured by lack of data and mismatches in their spatial grain (scale), and by grain-dependent effects of the drivers. Here we introduce cross-scale models integrating global data on tree-species richness from 1,336 local forest surveys and 282 regional checklists, enabling the estimation of drivers and patterns of biodiversity across spatial grains. We uncover grain-dependent effects of both environment and biogeographic regions on species richness, with a striking positive effect of Southeast Asia at coarse grain that disappears at fine grains. We show that, globally, biodiversity cannot be attributed purely to environmental or regional drivers, as the regions are environmentally distinct even within a single latitudinal band. Finally, we predict global maps of biodiversity at local (plot-based) and regional grains, identifying areas of exceptional beta-diversity in China, East Africa and North America. By allowing the importance of drivers of diversity to vary with grain in a single model, our approach unifies disparate results from previous studies regarding environmental versus biogeographic predictors of biodiversity, and enables efficient integration of heterogeneous data.

Macroecology to Unite All Life, Large and Small.

Macroecology is the study of the mechanisms underlying general patterns of ecology across scales. Research in microbial ecology and macroecology have long been detached. Here, we argue that it is time to bridge the gap, as they share a common currency of species and individuals, and a common goal of understanding the causes and consequences of changes in biodiversity. Microbial ecology and macroecology will mutually benefit from a unified research agenda and shared datasets that span the entirety of the biodiversity of life and the geographic expanse of the Earth.

Which Latitudinal Gradients for Genetic Diversity?

A recent global analysis of GenBank DNA sequences from amphibians and mammals indicated consistent poleward decrease of intraspecific genetic diversity in both classes. We highlight that this result was biased by not accounting for distance decay of similarity and reanalyse the datasets, revealing distinct latitudinal gradients in mammals and amphibians.

On the decline of biodiversity due to area loss.

Predictions of how different facets of biodiversity decline with habitat loss are broadly needed, yet challenging. Here we provide theory and a global empirical evaluation to address this challenge. We show that extinction estimates based on endemics-area and backward species-area relationships are complementary, and the crucial difference comprises the geometry of area loss. Across three taxa on four continents, the relative loss of species, and of phylogenetic and functional diversity, is highest when habitable area disappears inward from the edge of a region, lower when it disappears from the centre outwards, and lowest when area is lost at random. In inward destruction, species loss is almost proportional to area loss, although the decline in phylogenetic and functional diversity is less severe. These trends are explained by the geometry of species ranges and the shape of phylogenetic and functional trees, which may allow baseline predictions of biodiversity decline for underexplored taxa.

Historical Biogeography Using Species Geographical Ranges.

Spatial variation in biodiversity is the result of complex interactions between evolutionary history and ecological factors. Methods in historical biogeography combine phylogenetic information with current species locations to infer the evolutionary history of a clade through space and time. A major limitation of most methods for historical biogeographic inference is the requirement of single locations for terminal lineages, reducing contemporary species geographical ranges to a point in two-dimensional space. In reality, geographic ranges usually show complex geographic patterns, irregular shapes, or discontinuities. In this article, we describe a method for phylogeographic analysis using polygonal species geographic ranges of arbitrary complexity. By integrating the geographic diversification process across species ranges, we provide a method to infer the geographic location of ancestors in a Bayesian framework. By modeling migration conditioned on a phylogenetic tree, this approach permits reconstructing the geographic location of ancestors through time. We apply this new method to the diversification of two neotropical bird genera, Trumpeters (Psophia) and Cinclodes ovenbirds. We demonstrate the usefulness of our method (called rase) in phylogeographic reconstruction of species ancestral locations and contrast our results with previous methods that compel researchers to reduce the distribution of species to one point in space. We discuss model extensions to enable a more general, spatially explicit framework for historical biogeographic analysis.

Fine-scale malaria risk mapping from routine aggregated case data.

BACKGROUND: Mapping malaria risk is an integral component of efficient resource allocation. Routine health facility data are convenient to collect, but without information on the locations at which transmission occurred, their utility for predicting variation in risk at a sub-catchment level is presently unclear. METHODS: Using routinely collected health facility level case data in Swaziland between 2011-2013, and fine scale environmental and ecological variables, this study explores the use of a hierarchical Bayesian modelling framework for downscaling risk maps from health facility catchment level to a fine scale (1 km x 1 km). Fine scale predictions were validated using known household locations of cases and a random sample of points to act as pseudo-controls. RESULTS: Results show that fine-scale predictions were able to discriminate between cases and pseudo-controls with an AUC value of 0.84. When scaled up to catchment level, predicted numbers of cases per health facility showed broad correspondence with observed numbers of cases with little bias, with 84 of the 101 health facilities with zero cases correctly predicted as having zero cases. CONCLUSIONS: This method holds promise for helping countries in pre-elimination and elimination stages use health facility level data to produce accurate risk maps at finer scales. Further validation in other transmission settings and an evaluation of the operational value of the approach is necessary.

Environmental effects on the shape variation of male ultraviolet patterns in the Brimstone butterfly (Gonepteryx rhamni, Pieridae, Lepidoptera).

The males of the Brimstone butterfly (Gonepteryx rhamni) have ultraviolet pattern on the dorsal surfaces of their wings. Using geometric morphometrics, we have analysed correlations between environmental variables (climate, productivity) and shape variability of the ultraviolet pattern and the forewing in 110 male specimens of G. rhamni collected in the Palaearctic zone. To start with, we subjected the environmental variables to principal component analysis (PCA). The first PCA axis (precipitation, temperature, latitude) significantly correlated with shape variation of the ultraviolet patterns across the Palaearctic. Additionally, we have performed two-block partial least squares (PLS) analysis to assess co-variation between intraspecific shape variation and the variation of 11 environmental variables. The first PLS axis explained 93% of variability and represented the effect of precipitation, temperature and latitude. Along this axis, we observed a systematic increase in the relative area of ultraviolet colouration with increasing temperature and precipitation and decreasing latitude. We conclude that the shape variation of ultraviolet patterns on the forewings of male Brimstones is correlated with large-scale environmental factors.

Downscaling the environmental associations and spatial patterns of species richness.

We introduce a method that enables the estimation of species richness environment association and prediction of geographic patterns of species richness at grains finer than the original grain of observation. The method is based on a hierarchical model that uses coarse-grain values of species richness and fine-grain environmental data as input. In the model, the (unobserved) fine-grain species richness is linked to the observed fine-grain environment and upscaled using a simple species-area relationship (SAR). The upscaled values are then stochastically linked to the observed coarse-grain species richness. We tested the method on Southern African Bird Atlas data by downscaling richness from 2 degrees to 0.25 degrees (-250 km to -30 km) resolution. When prior knowledge of the SAR slope (average species turnover within coarse-grain cells) was available, the method predicted the fine-grain relationship between richness and environment and provided fine-grain predictions of richness that closely resembled results from native fine-grain models. Without the SAR knowledge the method still accurately quantified the richness-environment relationship, but accurately predicted only relative (rank) values of richness. The approach can be easily extended and it is a powerful path for cross-scale statistical modeling of richness-environment relationships, and for the provision of high-resolution maps for basic science and conservation.

Comment on "High-resolution global maps of 21st-century forest cover change".

Hansen et al. (Reports, 15 November 2013, p. 850) published a high-resolution global forest map with detailed information on local forest loss and gain. We show that their product does not distinguish tropical forests from plantations and even herbaceous crops, which leads to a substantial underestimate of forest loss and compromises its value for local policy decisions.

Species richness declines and biotic homogenisation have slowed down for NW-European pollinators and plants.

Concern about biodiversity loss has led to increased public investment in conservation. Whereas there is a widespread perception that such initiatives have been unsuccessful, there are few quantitative tests of this perception. Here, we evaluate whether rates of biodiversity change have altered in recent decades in three European countries (Great Britain, Netherlands and Belgium) for plants and flower visiting insects. We compared four 20-year periods, comparing periods of rapid land-use intensification and natural habitat loss (1930-1990) with a period of increased conservation investment (post-1990). We found that extensive species richness loss and biotic homogenisation occurred before 1990, whereas these negative trends became substantially less accentuated during recent decades, being partially reversed for certain taxa (e.g. bees in Great Britain and Netherlands). These results highlight the potential to maintain or even restore current species assemblages (which despite past extinctions are still of great conservation value), at least in regions where large-scale land-use intensification and natural habitat loss has ceased.

Universal species-area and endemics-area relationships at continental scales.

Despite the broad conceptual and applied relevance of how the number of species or endemics changes with area (the species-area and endemics-area relationships (SAR and EAR)), our understanding of universality and pervasiveness of these patterns across taxa and regions has remained limited. The SAR has traditionally been approximated by a power law, but recent theories predict a triphasic SAR in logarithmic space, characterized by steeper increases in species richness at both small and large spatial scales. Here we uncover such universally upward accelerating SARs for amphibians, birds and mammals across the world's major landmasses. Although apparently taxon-specific and continent-specific, all curves collapse into one universal function after the area is rescaled by using the mean range sizes of taxa within continents. In addition, all EARs approximately follow a power law with a slope close to 1, indicating that for most spatial scales there is roughly proportional species extinction with area loss. These patterns can be predicted by a simulation model based on the random placement of contiguous ranges within a domain. The universality of SARs and EARs after rescaling implies that both total and endemic species richness within an area, and also their rate of change with area, can be estimated by using only the knowledge of mean geographic range size in the region and mean species richness at one spatial scale.

Predictions of Taylor's power law, density dependence and pink noise from a neutrally modeled time series.

There has recently been increasing interest in neutral models of biodiversity and their ability to reproduce the patterns observed in nature, such as species abundance distributions. Here we investigate the ability of a neutral model to predict phenomena observed in single-population time series, a study complementary to most existing work that concentrates on snapshots in time of the whole community. We consider tests for density dependence, the dominant frequencies of population fluctuation (spectral density) and a relationship between the mean and variance of a fluctuating population (Taylor's power law). We simulated an archipelago model of a set of interconnected local communities with variable mortality rate, migration rate, speciation rate, size of local community and number of local communities. Our spectral analysis showed 'pink noise': a departure from a standard random walk dynamics in favor of the higher frequency fluctuations which is partly consistent with empirical data. We detected density dependence in local community time series but not in metacommunity time series. The slope of the Taylor's power law in the model was similar to the slopes observed in natural populations, but the fit to the power law was worse. Our observations of pink noise and density dependence can be attributed to the presence of an upper limit to community sizes and to the effect of migration which distorts temporal autocorrelation in local time series. We conclude that some of the phenomena observed in natural time series can emerge from neutral processes, as a result of random zero-sum birth, death and migration. This suggests the neutral model would be a parsimonious null model for future studies of time series data.

Rapoport's rule, species tolerances, and the latitudinal diversity gradient: geometric considerations.

The most pervasive species-richness pattern, the latitudinal gradient of diversity, has been related to Rapoport's rule, i.e., decreasing latitudinal extent of species' ranges toward the equator. According to this theory, species can have narrower tolerances in more stable climates, leading to smaller ranges and allowing coexistence of more species. We show, using a simple geometric model, that the postulated decrease of species' potential range sizes toward the tropics would itself lead to a latitudinal gradient opposite to that observed. In contrast, an increase in extent of potential ranges toward the tropics would lead to the observed diversity gradient. Moreover, in the presence of geographic barriers constraining actual species' ranges, Rapoport's rule emerges if the latitudinal trend in extents of potential ranges (as defined by climatic tolerance) is opposite to that postulated or if variability in potential range extents decreases toward the poles. A strong implicit latitudinal diversity gradient (i.e., higher concentration of midpoints of species' potential ranges in the tropics), however, produces both observed macroecological patterns without the contribution of any latitudinal trends in species climatic tolerances or in potential range sizes. Our model underscores the necessity of discriminating theoretical processes and principles from the patterns we observe, and it is well supported by data on global distribution of species' range sizes.

Arrested development of sheep strongyles: onset and resumption under field conditions of Central Europe.

Two tracer tests were conducted between August 2004 and March 2007 at an ecological farm in western Bohemia. The first tracer test was performed for the summer-autumn grazing period (onset of arrested development), the second for spring (resumption of arrested development). In the first tracer test, the percentage of nematodes arresting development over the winter months reached 87.7% for Teladorsagia circumcincta, 66.7% for Haemonchus contortus, 89.9% for Nematodirus filicollis, 21.6% for Trichostrongylus axei, and 23.9% for both Trichostrongylus vitrinus and Trichostrongylus colubriformis. None of the arrested larvae were observed with species Cooperia curticei, Nematodirus battus, and Oesophagostomum venulosum. In the second tracer test, a significant increase of adult worms was discovered in March of species T. circumcincta and N. filicollis and Trichostrongylus spp. in February. Redundancy analysis and generalized linear models analyses have confirmed that environmental conditions play a crucial role in hypobiosis of sheep strongyles in the Czech Republic. The analysis of influences of various environmental factors revealed that the number of arrested larvae was negatively influenced by light-day length, sunshine, or daylight decrease (p < 0.01).