Predicting invasive alien plant distributions: how geographical bias in occurrence records influences model performance

Aim To investigate the impact of geographical bias on the performance of ecological niche models for invasive plant species. Location South Africa and Australia. Methods We selected 10 Australian plants invasive in South Africa and nine South African plants invasive in Australia. Geographical bias was simulated in occurrence records obtained from the native range of a species to represent two scenarios. For the first scenario (A, worst‐case) a proportion of records were excluded from a specific region of a species’ range and for the second scenario (B, less extreme) only some records were excluded from that specific region of the range. Introduced range predictions were produced with the Maxent modelling algorithm where models were calibrated with datasets from these biased occurrence records and 19 bioclimatic variables. Models were evaluated with independent test data obtained from the introduced range of the species. Geographical bias was quantified as the proportional difference between the occurrence records from a control and a biased dataset, and environmental bias was expressed as either the difference in marginality or tolerance between these datasets. Model performance [assessed using the conventional and modified AUC (area under the curve of receiver‐operating characteristic plots) and the maximum true skill statistic] was compared between models calibrated with occurrence records from a biased dataset and a control dataset. Results We found considerable variation in the relationship between geographical and environmental bias. Environmental bias, expressed as the difference in marginality, differed significantly across treatments. Model performance did not differ significantly among treatments. Regions predicted as suitable for most of the species were very similar when compared between a biased and control dataset, with only a few exceptions. Main conclusions The geographical bias simulated in this study was sufficient to result in significant environmental bias across treatments, but despite this we did not find a significant effect on model performance. Differences in the environmental spaces occupied by the species in their native and invaded ranges may explain why we did not find a significant effect on model performance.     

Biased data reduce efficiency and effectiveness of conservation reserve networks

Complementarity-based reserve selection algorithms efficiently prioritize sites for biodiversity conservation, but they are data-intensive and most regions lack accurate distribution maps for the majority of species. We explored implications of basing conservation planning decisions on incomplete and biased data using occurrence records of the plant family Proteaceae in South Africa. Treating this high-quality database as 'complete', we introduced three realistic sampling biases characteristic of biodiversity databases: a detectability sampling bias and two forms of roads sampling bias. We then compared reserve networks constructed using complete, biased, and randomly sampled data. All forms of biased sampling performed worse than both the complete data set and equal-effort random sampling. Biased sampling failed to detect a median of 1-5% of species, and resulted in reserve networks that were 9-17% larger than those designed with complete data. Spatial congruence and the correlation of irreplaceability scores between reserve networks selected with biased and complete data were low. Thus, reserve networks based on biased data require more area to protect fewer species and identify different locations than those selected with randomly sampled or complete data.     

Spatial and taxonomic biases in bat records: Drivers and conservation implications in a megadiverse country

Biases in data availability have serious consequences on scientific inferences that can be derived. The potential consequences of these biases could be more detrimental in the less‐studied megadiverse regions, often characterized by high biodiversity and serious risks of human threats, as conservation and management actions could be misdirected. Here, focusing on 134 bat species in Mexico, we analyze spatial and taxonomic biases and their drivers in occurrence data; and identify priority areas for further data collection which are currently under‐sampled or at future environmental risk. We collated a comprehensive database of 26,192 presence‐only bat records in Mexico to characterize taxonomic and spatial biases and relate them to species' characteristics (range size and foraging behavior). Next, we examined variables related to accessibility, species richness and security to explain the spatial patterns in occurrence records. Finally, we compared the spatial distributions of existing data and future threats to these species to highlight those regions that are likely to experience an increased level of threats but are currently under‐surveyed. We found taxonomic biases, where species with wider geographical ranges and narrow‐space foragers (species easily captured with traditional methods), had more occurrence data. There was a significant oversampling toward tropical regions, and the presence and number of records was positively associated with areas of high topographic heterogeneity, road density, urban, and protected areas, and negatively associated with areas which were predicted to have future increases in temperature and precipitation. Sampling efforts for Mexican bats appear to have focused disproportionately on easily captured species, tropical regions, areas of high species richness and security; leading to under‐sampling in areas of high future threats. These biases could substantially influence the assessment of current status of, and future anthropogenic impacts on, this diverse species group in a tropical megadiverse country.     

Geographical sampling bias and its implications for conservation priorities in Africa

Aim To design and apply statistical tests for measuring sampling bias in the raw data used to the determine priority areas for conservation, and to discuss their impact on conservation analyses for the region. Location Sub‐Saharan Africa. Methods An extensive data set comprising 78,083 vouchered locality records for 1068 passerine birds in sub‐Saharan Africa has been assembled. Using geographical information systems, we designed and applied two tests to determine if sampling of these taxa was biased. First, we detected possible biases because of accessibility by measuring the proximity of each record to cities, rivers and roads. Second, we quantified the intensity of sampling of each species inside and surrounding proposed conservation priority areas and compared it with sampling intensity in non‐priority areas. We applied statistical tests to determine if the distribution of these sampling records deviated significantly from random distributions. Results The analyses show that the location and intensity of collecting have historically been heavily influenced by accessibility. Sampling localities show dense, significant aggregation around city limits, and along rivers and roads. When examining the collecting sites of each individual species, the pattern of sampling has been significantly concentrated within and immediately surrounding areas now designated as conservation priorities. Main conclusions Assessment of patterns of species richness and endemicity at the scale useful for establishing conservation priorities, below the continental level, undoubtedly reflects biases in taxonomic sampling. This is especially problematic for priorities established using the criterion of complementarity because the estimated spatial costs of this approach are highly sensitive to sampling artefacts. Hence such conservation priorities should be interpreted with caution proportional to the bias found. We argue that conservation priority setting analyses require (1) statistical tests to detect these biases, and (2) data treatment to reflect species distribution rather than patterns of collecting effort.     

The second Southern African Bird Atlas Project: Causes and consequences of geographical sampling bias

Using the Southern African Bird Atlas Project (SABAP2) as a case study, we examine the possible determinants of spatial bias in volunteer sampling effort and how well such biased data represent environmental gradients across the area covered by the atlas. For each province in South Africa, we used generalized linear mixed models to determine the combination of variables that explain spatial variation in sampling effort (number of visits per 5′ × 5′ grid cell, or “pentad”). The explanatory variables were distance to major road and exceptional birding locations or “sampling hubs,” percentage cover of protected, urban, and cultivated area, and the climate variables mean annual precipitation, winter temperatures, and summer temperatures. Further, we used the climate variables and plant biomes to define subsets of pentads representing environmental zones across South Africa, Lesotho, and Swaziland. For each environmental zone, we quantified sampling intensity, and we assessed sampling completeness with species accumulation curves fitted to the asymptotic Lomolino model. Sampling effort was highest close to sampling hubs, major roads, urban areas, and protected areas. Cultivated area and the climate variables were less important. Further, environmental zones were not evenly represented by current data and the zones varied in the amount of sampling required representing the species that are present. SABAP2 volunteers' preferences in birding locations cause spatial bias in the dataset that should be taken into account when analyzing these data. Large parts of South Africa remain underrepresented, which may restrict the kind of ecological questions that may be addressed. However, sampling bias may be improved by directing volunteers toward undersampled regions while taking into account volunteer preferences.     

spThin: an R package for spatial thinning of species occurrence records for use in ecological niche models

Spatial thinning of species occurrence records can help address problems associated with spatial sampling biases. Ideally, thinning removes the fewest records necessary to substantially reduce the effects of sampling bias, while simultaneously retaining the greatest amount of useful information. Spatial thinning can be done manually; however, this is prohibitively time consuming for large datasets. Using a randomization approach, the ‘thin’ function in the spThin R package returns a dataset with the maximum number of records for a given thinning distance, when run for sufficient iterations. We here provide a worked example for the Caribbean spiny pocket mouse, where the results obtained match those of manual thinning.     

Spatial Bias in the Marine Fossil Record

Inference of past and present global biodiversity requires enough global data to distinguish biological pattern from sampling artifact. Pertinently, many studies have exposed correlated relationships between richness and sampling in the fossil record, and methods to circumvent these biases have been proposed. Yet, these studies often ignore paleobiogeography, which is undeniably a critical component of ancient global diversity. Alarmingly, our global analysis of 481,613 marine fossils spread throughout the Phanerozoic reveals that where localities are and how intensively they have been sampled almost completely determines empirical spatial patterns of richness, suggesting no separation of biological pattern from sampling pattern. To overcome this, we analyze diversity using occurrence records drawn from two discrete paleolatitudinal bands which cover the bulk of the fossil data. After correcting the data for sampling bias, we find that these two bands have similar patterns of richness despite markedly different spatial coverage. Our findings suggest that i) long-term diversity trends result from large-scale tectonic evolution of the planet, ii) short-term diversity trends are region-specific, and iii) paleodiversity studies must constrain their analyses to well-sampled regions to uncover patterns not driven by sampling.     

Filling in the GAPS: evaluating completeness and coverage of open‐access biodiversity databases in the United States

Primary biodiversity data constitute observations of particular species at given points in time and space. Open‐access electronic databases provide unprecedented access to these data, but their usefulness in characterizing species distributions and patterns in biodiversity depend on how complete species inventories are at a given survey location and how uniformly distributed survey locations are along dimensions of time, space, and environment. Our aim was to compare completeness and coverage among three open‐access databases representing ten taxonomic groups (amphibians, birds, freshwater bivalves, crayfish, freshwater fish, fungi, insects, mammals, plants, and reptiles) in the contiguous United States. We compiled occurrence records from the Global Biodiversity Information Facility (GBIF), the North American Breeding Bird Survey (BBS), and federally administered fish surveys (FFS). We aggregated occurrence records by 0.1° × 0.1° grid cells and computed three completeness metrics to classify each grid cell as well‐surveyed or not. Next, we compared frequency distributions of surveyed grid cells to background environmental conditions in a GIS and performed Kolmogorov–Smirnov tests to quantify coverage through time, along two spatial gradients, and along eight environmental gradients. The three databases contributed >13.6 million reliable occurrence records distributed among >190,000 grid cells. The percent of well‐surveyed grid cells was substantially lower for GBIF (5.2%) than for systematic surveys (BBS and FFS; 82.5%). Still, the large number of GBIF occurrence records produced at least 250 well‐surveyed grid cells for six of nine taxonomic groups. Coverages of systematic surveys were less biased across spatial and environmental dimensions but were more biased in temporal coverage compared to GBIF data. GBIF coverages also varied among taxonomic groups, consistent with commonly recognized geographic, environmental, and institutional sampling biases. This comprehensive assessment of biodiversity data across the contiguous United States provides a prioritization scheme to fill in the gaps by contributing existing occurrence records to the public domain and planning future surveys.     

sampbias,a method for quantifying geographic sampling biases in species distribution data

Geo‐referenced species occurrences from public databases have become essential to biodiversity research and conservation. However, geographical biases are widely recognized as a factor limiting the usefulness of such data for understanding species diversity and distribution. In particular, differences in sampling intensity across a landscape due to differences in human accessibility are ubiquitous but may differ in strength among taxonomic groups and data sets. Although several factors have been described to influence human access (such as presence of roads, rivers, airports and cities), quantifying their specific and combined effects on recorded occurrence data remains challenging. Here we present sampbias, an algorithm and software for quantifying the effect of accessibility biases in species occurrence data sets. sampbias uses a Bayesian approach to estimate how sampling rates vary as a function of proximity to one or multiple bias factors. The results are comparable among bias factors and data sets. We demonstrate the use of sampbias on a data set of mammal occurrences from the island of Borneo, showing a high biasing effect of cities and a moderate effect of roads and airports. sampbias is implemented as a well‐documented, open‐access and user‐friendly R package that we hope will become a standard tool for anyone working with species occurrences in ecology, evolution, conservation and related fields.     

Bias in freshwater biodiversity sampling: the case of Iberian water beetles

Extensive distributional data bases are key tools in ecological research, and good-quality data are required to provide reliable conservation strategies and an understanding of biodiversity patterns and processes. Although the evaluation of data bases requires the incorporation of estimates of sampling effort and bias, no studies have focused on these aspects for freshwater biodiversity data. We used here a comprehensive data base of water beetles from the Iberian Peninsula and the Balearic Islands, and examine whether these data provide an unbiased, reliable picture of their diversity and distribution in the study area. Based on theoretical estimates using the Clench function on the accumulated number of records as a surrogate of sampling effort, about a quarter of the Iberian and Balearic 50 × 50 km Universal Transverse Mercator grid cells can be considered well prospected, with more than 70% of the theoretical species richness actually recorded. These well-surveyed cells are not evenly distributed across biogeographical and physicoclimatic subregions, reflecting some geographical bias in the distribution of sampling effort. Our results suggest that recording was skewed by relatively simple variables affecting collector activity, such as the perceived 'attractiveness' of mountainous landscapes and protected areas with recently described species, and accessibility of sampling sites (distance from main research centres). We emphasize the importance of these evaluation exercises, which are useful to locate areas needed of further sampling as well as to identify potential biases in the distribution of current biodiversity patterns.     

Improved species‐occurrence predictions in data‐poor regions: using large‐scale data and bias correction with down‐weighted Poisson regression and Maxent

Species distribution modelling (SDM) has become an essential method in ecology and conservation. In the absence of survey data, the majority of SDMs are calibrated with opportunistic presence‐only data, incurring substantial sampling bias. We address the challenge of correcting for sampling bias in the data‐sparse situations. We modelled the relative intensity of bat records in their entire range using three modelling algorithms under the point‐process modelling framework (GLMs with subset selection, GLMs fitted with an elastic‐net penalty, and Maxent). To correct for sampling bias, we applied model‐based bias correction by incorporating spatial information on site accessibility or sampling efforts. We evaluated the effect of bias correction on the models’ predictive performance (AUC and TSS), calculated on spatial‐block cross‐validation and a holdout data set. When evaluated with independent, but also sampling‐biased test data, correction for sampling bias led to improved predictions. The predictive performance of the three modelling algorithms was very similar. Elastic‐net models have intermediate performance, with slight advantage for GLMs on cross‐validation and Maxent on hold‐out evaluation. Model‐based bias correction is very useful in data‐sparse situations, where detailed data are not available to apply other bias correction methods. However, bias correction success depends on how well the selected bias variables describe the sources of bias. In this study, accessibility covariates described bias in our data better than the effort covariate, and their use led to larger changes in predictive performance. Objectively evaluating bias correction requires bias‐free presence–absence test data, and without them the real improvement for describing a species’ environmental niche cannot be assessed.     

Reconstructing pre-impact vegetation cover in modified landscapes using environmental modelling, historical surveys and remnant vegetation data: a case study in the Fleurieu Peninsula, South Australia

Aim To investigate the application of environmental modelling to reconstructive mapping of pre‐impact vegetation using historical survey records and remnant vegetation data. Location The higher elevation regions of the Fleurieu Peninsula region in South Australia were selected as a case study. The Fleurieu Peninsula is an area typical of many agricultural regions in temperate Australia that have undergone massive environmental transformation since European settlement. Around 9% of the present land cover is remnant vegetation and historical survey records from the ad 1880s exist. It is a region with strong gradients in climate and topography. Methods Records of pre‐impact vegetation distribution made in surveyors’ field notebooks were transcribed into a geographical information system and the spatial and classificatory accuracy of these records was assessed. Maps of remnant vegetation distribution were obtained. Analysis was undertaken to quantify the environmental domains of historical survey record and remnant vegetation data to selected meso‐scaled climatic parameters and topo‐scaled terrain‐related indices at a 20 m resolution. An exploratory analytical procedure was used to quantify the probability of occurrence of vegetation types in environmental domains. Probability models spatially extended to geographical space produce maps of the probability of occurrence of vegetation types. Individual probability maps were combined to produce a pre‐impact vegetation map of the region. Results Surveyors’ field notebook records provide reliable information that is accurately locatable to levels of resolution such that the vegetation data can be spatially correlated with environmental variables generated on 20 m resolution environmental data sets. Historical survey records of vegetation were weakly correlated with the topo‐scaled environmental variables but were correlated with meso‐scaled climate. Remnant vegetation records similarly not only correlated to climate but also displayed stronger relationships with the topo‐scaled environmental variables, particularly slope. Main conclusions A major conclusion of this study is that multiple sources of evidence are required to reconstruct past vegetation patterns in heavily transformed region. Neither the remnant vegetation data nor historical survey records provided adequate data sets on their own to reconstruct the pre‐impact vegetation of the Fleurieu Peninsula. Multiple sources of evidence provide the only means of assessing the environmental and historical representativeness of data sets. The spatial distribution of historical survey records was more environmentally representative than remnant vegetation data, which reflect biases due to land clearance. Historical survey records were also shown to be classificatory and spatially accurate, thus are suitable for quantitative spatial analyses. Analysis of different spatial vegetation data sets in an environmental modelling framework provided a rigorous means of assessing and comparing respective data sets as well as mapping their predicted distributions based on quantitative correlations. The method could be usefully applied to other regions where predictions of pre‐impact vegetation cover are required.     

Spatial sampling bias and model complexity in stream‐based species distribution models: A case study of Paddlefish (Polyodon spathula) in the Arkansas River basin,USA

Leveraging existing presence records and geospatial datasets, species distribution modeling has been widely applied to informing species conservation and restoration efforts. Maxent is one of the most popular modeling algorithms, yet recent research has demonstrated Maxent models are vulnerable to prediction errors related to spatial sampling bias and model complexity. Despite elevated rates of biodiversity imperilment in stream ecosystems, the application of Maxent models to stream networks has lagged, as has the availability of tools to address potential sources of error and calculate model evaluation metrics when modeling in nonraster environments (such as stream networks). Herein, we use Maxent and customized R code to estimate the potential distribution of paddlefish (Polyodon spathula) at a stream‐segment level within the Arkansas River basin, USA, while accounting for potential spatial sampling bias and model complexity. Filtering the presence data appeared to adequately remove an eastward, large‐river sampling bias that was evident within the unfiltered presence dataset. In particular, our novel riverscape filter provided a repeatable means of obtaining a relatively even coverage of presence data among watersheds and streams of varying sizes. The greatest differences in estimated distributions were observed among models constructed with default versus AIC_C‐selected parameterization. Although all models had similarly high performance and evaluation metrics, the AIC_C‐selected models were more inclusive of westward‐situated and smaller, headwater streams. Overall, our results solidified the importance of accounting for model complexity and spatial sampling bias in SDMs constructed within stream networks and provided a roadmap for future paddlefish restoration efforts in the study area.     

More Than Just Records: Analysing Natural History Collections for Biodiversity Planning

Natural History Collections (NHCs) play a central role as sources of data for biodiversity and conservation. Yet, few NHCs have examined whether the data they contain is adequately representative of local biodiversity. I examined over 15,000 databased records of Hymenoptera from 1435 locations across New Zealand collected over the past 90 years. These records are assessed in terms of their geographical, temporal, and environmental coverage across New Zealand. Results showed that the spatial coverage of records was significantly biased, with the top four areas contributing over 51% of all records. Temporal biases were also evident, with a large proportion (40%) of records collected within a short time period. The lack of repeat visits to specific locations indicated that the current set of NHC records would be of limited use for long-term ecological research. Consequently, analyses and interpretation of historical data, for example, shifts in community composition, would be limited. However, in general, NHC records provided good coverage of the diversity of New Zealand habitats and climatic environments, although fewer NHC records were represented at cooler temperatures (<5°C) and the highest rainfalls (>5000 mm/yr). Analyses of NHCs can be greatly enhanced by using simple techniques that examine collection records in terms of environmental and geographical space. NHCs that initiate a systematic sampling strategy will provide higher quality data for biodiversity research than ad hoc or point samples, as is currently the norm. Although NHCs provide a rich source of information they could be far better utilised in a range of large-scale ecological and conservation studies.     

Maps of relative floristic ignorance and virtual floristic lists: An R package to incorporate uncertainty in mapping and analysing biodiversity data

The vast amount of occurrence records currently available offers increasing opportunities for biodiversity data analyses. This amount of data poses new challenges for the reliability and correct interpretation of the results. Indeed, to safely deal with occurrence records, their uncertainty and associated biases should be taken into account. We developed an R package to explicitly include spatial and temporal uncertainties during the mapping and listing of plant occurrence records for a given study area. Our workflow returns two objects: (a) a ‘Map of Relative Floristic Ignorance’ (MRFI), which represents the spatial distribution of the lack of floristic knowledge; (b) a ‘Virtual Floristic List’ (VFL), i.e. a list of taxa potentially occurring in the area with an associated probability of occurrence. The method implemented in the package can manage a large amount of occurrence data and represents relative floristic ignorance across a study area with a sustainable computational effort. Several parameters can be set by the user, conferring high flexibility to the method. Uncertainty is not avoided, but incorporated into biodiversity analyses through appropriate methodological approaches and innovative spatial representations. Our study introduces a workflow that pushes forward the analytical capacities to deal with uncertainty in biological occurrence records, allowing to produce more accurate outputs.     

The ‘rotiferologist’ effect and other global correlates of species richness in monogonont rotifers

Global biodiversity patterns are often driven by different environmental variables at different scales. However, it is still controversial whether there are general trends, whether similar processes are responsible for similar patterns, and/or whether confounding effects such as sampling bias can produce misleading results. Our aim is twofold: 1) assessing the global correlates of diversity in a group of microscopic animals little analysed so far, and 2) inferring the influence of sampling intensity on biodiversity analyses. As a case study, we choose rotifers, because of their high potential for dispersal across the globe. We assembled and analysed a new worldwide dataset of records of monogonont rotifers, a group of microscopic aquatic animals, from 1960 to 1992. Using spatially explicit models, we assessed whether the diversity patterns conformed to those commonly obtained for larger organisms, and whether they still held true after controlling for sampling intensity, variations in area, and spatial structure in the data. Our results are in part analogous to those commonly obtained for macroorganisms (habitat heterogeneity and precipitation emerge as the main global correlates), but show some divergence (potential absence of a latitudinal gradient and of a large‐scale correlation with human population). Moreover, the effect of sampling effort is remarkable, accounting for >50% of the variability; this strong effect may mask other patterns such as latitudinal gradients. Our study points out that sampling bias should be carefully considered when drawing conclusions from large‐scale analyses, and calls for further faunistic work on microorganisms in all regions of the world to better understand the generality of the processes driving global patterns in biodiversity.     

occAssess: An R package for assessing potential biases in species occurrence data

Species occurrence records from a variety of sources are increasingly aggregated into heterogeneous databases and made available to ecologists for immediate analytical use. However, these data are typically biased, i.e. they are not a probability sample of the target population of interest, meaning that the information they provide may not be an accurate reflection of reality. It is therefore crucial that species occurrence data are properly scrutinised before they are used for research. In this article, we introduce occAssess, an R package that enables straightforward screening of species occurrence data for potential biases. The package contains a number of discrete functions, each of which returns a measure of the potential for bias in one or more of the taxonomic, temporal, spatial, and environmental dimensions. Users can opt to provide a set of time periods into which the data will be split; in this case separate outputs will be provided for each period, making the package particularly useful for assessing the suitability of a dataset for estimating temporal trends in species'' distributions. The outputs are provided visually (as ggplot2 objects) and do not include a formal recommendation as to whether data are of sufficient quality for any given inferential use. Instead, they should be used as ancillary information and viewed in the context of the question that is being asked, and the methods that are being used to answer it. We demonstrate the utility of occAssess by applying it to data on two key pollinator taxa in South America: leaf‐nosed bats (Phyllostomidae) and hoverflies (Syrphidae). In this worked example, we briefly assess the degree to which various aspects of data coverage appear to have changed over time. We then discuss additional applications of the package, highlight its limitations, and point to future development opportunities.     

Comparing the effect of modeled climatic variables on the distribution of African horse sickness in South Africa and Namibia

Africa horse sickness (AHS) is a lethal disease of horses with a seasonal occurrence that is influenced by environmental conditions that favor the development of Culicoides midges (Diptera: Ceratopogonidae). This study compared and evaluated the relationship of various modeled climatic variables with the distribution and abundance of AHS in South Africa and Namibia. A comprehensive literature review of the historical AHS reported data collected from the Windhoek archives as well as annual reports from the Directorate of Veterinary services in Namibia were conducted. South African AHS reported data were collected from the South African Department of Agriculture, Forestry, and Fisheries. Daily climatic data were extracted for the time period 1993–2011 from the ERA‐interim re‐analysis dataset. The principal component analysis of the complete dataset indicated a significant statistical difference between Namibia and South Africa for the various climate variables and the outbreaks of AHS. The most influential parameters in the distribution of AHS included humidity, precipitation, evaporation, and minimum temperature. In South Africa, temperature had the most significant effect on the outbreaks of AHS, whereas in Namibia, humidity and precipitation were the main drivers. The maximum AHS cases in South Africa occurred at temperatures of 20–22° C and relative humidity between 50–70%. Furthermore, anthropogenic effects must be taken into account when trying to understand the distribution of AHS.     

A cloud-based toolbox for the versatile environmental annotation of biodiversity data

A vast range of research applications in biodiversity sciences requires integrating primary species, genetic, or ecosystem data with other environmental data. This integration requires a consideration of the spatial and temporal scale appropriate for the data and processes in question. But a versatile and scale flexible environmental annotation of biodiversity data remains constrained by technical hurdles. Existing tools have streamlined the intersection of occurrence records with gridded environmental data but have remained limited in their ability to address a range of spatial and temporal grains, especially for large datasets. We present the Spatiotemporal Observation Annotation Tool (STOAT), a cloud-based toolbox for flexible biodiversity–environment annotations. STOAT is optimized for large biodiversity datasets and allows user-specified spatial and temporal resolution and buffering in support of environmental characterizations that account for the uncertainty and scale of data and of relevant processes. The tool offers these services for a growing set of near global, remotely sensed, or modeled environmental data, including Landsat, MODIS, EarthEnv, and CHELSA. STOAT includes a user-friendly, web-based dashboard that provides tools for annotation task management and result visualization, linked to Map of Life, and a dedicated R package (rstoat) for programmatic access. We demonstrate STOAT functionality with several examples that illustrate phenological variation and spatial and temporal scale dependence of environmental characteristics of birds at a continental scale. We expect STOAT to facilitate broader exploration and assessment of the scale dependence of observations and processes in ecology.

In ecology and evolution, processes, data collection, and inference or prediction usually occur at different scales in space and time. This study introduces a cloud-based toolbox for the flexible fusion of biodiversity records with remotely sensed and other environmental information that supports an assessment and accounting of such scale dependencies.     

Designing bryophyte surveys for an optimal coverage of diversity gradients

Knowledge on the distribution and abundance of species is plagued by significant taxonomic and geographic biases that influence the analyses on biodiversity patterns. Due to this, standard, easy-to-use methods are needed to design efficient field campaigns that minimize data deficiencies. We evaluate the applicability, usefulness and effectiveness of a survey design protocol based on the Environmental Diversity (ED) criterion under different scenarios, with examples of varying extent of environmental niche, range of spatial distribution and level of previous knowledge. We planned surveys for epiphytic bryophytes growing in three types of forests at NW Iberian Peninsula (dominated by Quercus ilex, Q. faginea and Q. pyrenaica). Knowledge on the distribution and abundance of epiphytic bryophytes in this region presents large gaps and strong geographic biases. Besides, the three forest types differ in their environmental requirements, spatial distribution and level of previous knowledge, providing three working scenarios to test the response of the protocol under different situations. The protocol was implemented as a set of sequential selection rules, starting by an ED-based criterion aiming at maximizing the coverage of climatic and geographic variability; further criteria include an iterative set of qualitative properties: maximizing forest area, conservation status and accessibility. The protocol performed efficiently at different ranges of spatial distribution levels of environmental variability, and degree of previous knowledge and generated an even distribution of sampling points that efficiently covered the diversity of epiphytic bryophytes. The results show that ED protocols are a proficient and time-saving approach to select sampling sites by objective criteria also for groups with high dispersal ability and fragmented landscapes.