Descriptive inference using large, unrepresentative nonprobability samples: An introduction for ecologists.

Biodiversity monitoring usually involves drawing inferences about some variable of interest across a defined landscape from observations made at a sample of locations within that landscape. If the variable of interest differs between sampled and nonsampled locations, and no mitigating action is taken, then the sample is unrepresentative and inferences drawn from it will be biased. It is possible to adjust unrepresentative samples so that they more closely resemble the wider landscape in terms of "auxiliary variables." A good auxiliary variable is a common cause of sample inclusion and the variable of interest, and if it explains an appreciable portion of the variance in both, then inferences drawn from the adjusted sample will be closer to the truth. We applied six types of survey sample adjustment-subsampling, quasirandomization, poststratification, superpopulation modeling, a "doubly robust" procedure, and multilevel regression and poststratification-to a simple two-part biodiversity monitoring problem. The first part was to estimate the mean occupancy of the plant Calluna vulgaris in Great Britain in two time periods (1987-1999 and 2010-2019); the second was to estimate the difference between the two (i.e., the trend). We estimated the means and trend using large, but (originally) unrepresentative, samples from a citizen science dataset. Compared with the unadjusted estimates, the means and trends estimated using most adjustment methods were more accurate, although standard uncertainty intervals generally did not cover the true values. Completely unbiased inference is not possible from an unrepresentative sample without knowing and having data on all relevant auxiliary variables. Adjustments can reduce the bias if auxiliary variables are available and selected carefully, but the potential for residual bias should be acknowledged and reported.

We need to talk about nonprobability samples.

In most circumstances, probability sampling is the only way to ensure unbiased inference about population quantities where a complete census is not possible. As we enter the era of 'big data', however, nonprobability samples, whose sampling mechanisms are unknown, are undergoing a renaissance. We explain why the use of nonprobability samples can lead to spurious conclusions, and why seemingly large nonprobability samples can be (effectively) very small. We also review some recent controversies surrounding the use of nonprobability samples in biodiversity monitoring. These points notwithstanding, we argue that nonprobability samples can be useful, provided that their limitations are assessed, mitigated where possible and clearly communicated. Ecologists can learn much from other disciplines on each of these fronts.

ROBITT: A tool for assessing the risk-of-bias in studies of temporal trends in ecology.

Aggregated species occurrence and abundance data from disparate sources are increasingly accessible to ecologists for the analysis of temporal trends in biodiversity. However, sampling biases relevant to any given research question are often poorly explored and infrequently reported; this can undermine statistical inference. In other disciplines, it is common for researchers to complete 'risk-of-bias' assessments to expose and document the potential for biases to undermine conclusions. The huge growth in available data, and recent controversies surrounding their use to infer temporal trends, indicate that similar assessments are urgently needed in ecology.We introduce ROBITT, a structured tool for assessing the 'Risk-Of-Bias In studies of Temporal Trends in ecology'. ROBITT has a similar format to its counterparts in other disciplines: it comprises signalling questions designed to elicit information on the potential for bias in key study domains. In answering these, users will define study inferential goal(s) and relevant statistical target populations. This information is used to assess potential sampling biases across domains relevant to the research question (e.g. geography, taxonomy, environment), and how these vary through time. If assessments indicate biases, then users must clearly describe them and/or explain what mitigating action will be taken.Everything that users need to complete a ROBITT assessment is provided: the tool, a guidance document and a worked example. Following other disciplines, the tool and guidance document were developed through a consensus-forming process across experts working in relevant areas of ecology and evidence synthesis.We propose that researchers should be strongly encouraged to include a ROBITT assessment when publishing studies of biodiversity trends, especially when using aggregated data. This will help researchers to structure their thinking, clearly acknowledge potential sampling issues, highlight where expert consultation is required and provide an opportunity to describe data checks that might go unreported. ROBITT will also enable reviewers, editors and readers to establish how well research conclusions are supported given a dataset combined with some analytical approach. In turn, it should strengthen evidence-based policy and practice, reduce differing interpretations of data and provide a clearer picture of the uncertainties associated with our understanding of reality.

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.

AI Naturalists Might Hold the Key to Unlocking Biodiversity Data in Social Media Imagery.

The increasing availability of digital images, coupled with sophisticated artificial intelligence (AI) techniques for image classification, presents an exciting opportunity for biodiversity researchers to create new datasets of species observations. We investigated whether an AI plant species classifier could extract previously unexploited biodiversity data from social media photos (Flickr). We found over 60,000 geolocated images tagged with the keyword "flower" across an urban and rural location in the UK and classified these using AI, reviewing these identifications and assessing the representativeness of images. Images were predominantly biodiversity focused, showing single species. Non-native garden plants dominated, particularly in the urban setting. The AI classifier performed best when photos were focused on single native species in wild situations but also performed well at higher taxonomic levels (genus and family), even when images substantially deviated from this. We present a checklist of questions that should be considered when undertaking a similar analysis.

Reassessing the observational evidence for nitrogen deposition impacts in acid grassland: spatial Bayesian linear models indicate small and ambiguous effects on species richness.

Nitrogen deposition (Ndep) is considered a significant threat to plant diversity in grassland ecosystems around the world. The evidence supporting this conclusion comes from both observational and experimental research, with "space-for-time" substitution surveys of pollutant gradients a significant portion of the former. However, estimates of regression coefficients for Ndep impacts on species richness, derived with a focus on causal inference, are hard to locate in the observational literature. Some influential observational studies have presented estimates from univariate models, overlooking the effects of omitted variable bias, and/or have used P-value-based stepwise variable selection (PSVS) to infer impacts, a strategy known to be poorly suited to the accurate estimation of regression coefficients. Broad-scale spatial autocorrelation has also generally been unaccounted for. We re-examine two UK observational datasets that have previously been used to investigate the relationship between Ndep and plant species richness in acid grasslands, a much-researched habitat in this context. One of these studies (Stevens et al., 2004, Science, 303: 1876-1879) estimated a large negative impact of Ndep on richness through the use of PSVS; the other reported smaller impacts (Maskell et al., 2010, Global Change Biology, 16: 671-679), but did not explicitly report regression coefficients or partial effects, making the actual size of the estimated Ndep impact difficult to assess. We reanalyse both datasets using a spatial Bayesian linear model estimated using integrated nested Laplace approximation (INLA). Contrary to previous results, we found similar-sized estimates of the Ndep impact on plant richness between studies, both with and without bryophytes, albeit with some disagreement over the most likely direction of this effect. Our analyses suggest that some previous estimates of Ndep impacts on richness from space-for-time substitution studies are likely to have been over-estimated, and that the evidence from observational studies could be fragile when confronted with alternative model specifications, although further work is required to investigate potentially nonlinear responses. Given the growing literature on the use of observational data to estimate the impacts of pollutants on biodiversity, we suggest that a greater focus on clearly reporting important outcomes with associated uncertainty, the use of techniques to account for spatial autocorrelation, and a clearer focus on the aims of a study, whether explanatory or predictive, are all required.

Invasive non-native species likely to threaten biodiversity and ecosystems in the Antarctic Peninsula region.

The Antarctic is considered to be a pristine environment relative to other regions of the Earth, but it is increasingly vulnerable to invasions by marine, freshwater and terrestrial non-native species. The Antarctic Peninsula region (APR), which encompasses the Antarctic Peninsula, South Shetland Islands and South Orkney Islands, is by far the most invaded part of the Antarctica continent. The risk of introduction of invasive non-native species to the APR is likely to increase with predicted increases in the intensity, diversity and distribution of human activities. Parties that are signatories to the Antarctic Treaty have called for regional assessments of non-native species risk. In response, taxonomic and Antarctic experts undertook a horizon scanning exercise using expert opinion and consensus approaches to identify the species that are likely to present the highest risk to biodiversity and ecosystems within the APR over the next 10 years. One hundred and three species, currently absent in the APR, were identified as relevant for review, with 13 species identified as presenting a high risk of invading the APR. Marine invertebrates dominated the list of highest risk species, with flowering plants and terrestrial invertebrates also represented; however, vertebrate species were thought unlikely to establish in the APR within the 10 year timeframe. We recommend (a) the further development and application of biosecurity measures by all stakeholders active in the APR, including surveillance for species such as those identified during this horizon scanning exercise, and (b) use of this methodology across the other regions of Antarctica. Without the application of appropriate biosecurity measures, rates of introductions and invasions within the APR are likely to increase, resulting in negative consequences for the biodiversity of the whole continent, as introduced species establish and spread further due to climate change and increasing human activity.

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.

Annual estimates of occupancy for bryophytes, lichens and invertebrates in the UK, 1970-2015.

Here, we determine annual estimates of occupancy and species trends for 5,293 UK bryophytes, lichens, and invertebrates, providing national scale information on UK biodiversity change for 31 taxonomic groups for the time period 1970 to 2015. The dataset was produced through the application of a Bayesian occupancy modelling framework to species occurrence records supplied by 29 national recording schemes or societies (n = 24,118,549 records). In the UK, annual measures of species status from fine scale data (e.g. 1 × 1 km) had previously been limited to a few taxa for which structured monitoring data are available, mainly birds, butterflies, bats and a subset of moth species. By using an occupancy modelling framework designed for use with relatively low recording intensity data, we have been able to estimate species trends and generate annual estimates of occupancy for taxa where annual trend estimates and status were previously limited or unknown at this scale. These data broaden our knowledge of UK biodiversity and can be used to investigate variation in and drivers of biodiversity change.

Urban meadows as an alternative to short mown grassland: effects of composition and height on biodiversity.

There are increasing calls to provide greenspace in urban areas, yet the ecological quality, as well as quantity, of greenspace is important. Short mown grassland designed for recreational use is the dominant form of urban greenspace in temperate regions but requires considerable maintenance and typically provides limited habitat value for most taxa. Alternatives are increasingly proposed, but the biodiversity potential of these is not well understood. In a replicated experiment across six public urban greenspaces, we used nine different perennial meadow plantings to quantify the relative roles of floristic diversity and height of sown meadows on the richness and composition of three taxonomic groups: plants, invertebrates, and soil microbes. We found that all meadow treatments were colonized by plant species not sown in the plots, suggesting that establishing sown meadows does not preclude further locally determined grassland development if management is appropriate. Colonizing species were rarer in taller and more diverse plots, indicating competition may limit invasion rates. Urban meadow treatments contained invertebrate and microbial communities that differed from mown grassland. Invertebrate taxa responded to changes in both height and richness of meadow vegetation, but most orders were more abundant where vegetation height was longer than mown grassland. Order richness also increased in longer vegetation and Coleoptera family richness increased with plant diversity in summer. Microbial community composition seems sensitive to plant species composition at the soil surface (0-10 cm), but in deeper soils (11-20 cm) community variation was most responsive to plant height, with bacteria and fungi responding differently. In addition to improving local residents' site satisfaction, native perennial meadow plantings can produce biologically diverse grasslands that support richer and more abundant invertebrate communities, and restructured plant, invertebrate, and soil microbial communities compared with short mown grassland. Our results suggest that diversification of urban greenspace by planting urban meadows in place of some mown amenity grassland is likely to generate substantial biodiversity benefits, with a mosaic of meadow types likely to maximize such benefits.

The design, launch and assessment of a new volunteer-based plant monitoring scheme for the United Kingdom.

Volunteer-based plant monitoring in the UK has focused mainly on distribution mapping; there has been less emphasis on the collection of data on plant communities and habitats. Abundance data provide different insights into ecological pattern and allow for more powerful inference when considering environmental change. Abundance monitoring for other groups of organisms is well-established in the UK, e.g. for birds and butterflies, and conservation agencies have long desired comparable schemes for plants. We describe a new citizen science scheme for the UK (the 'National Plant Monitoring Scheme'; NPMS), with the primary aim of monitoring the abundance of plants at small scales. Scheme development emphasised volunteer flexibility through scheme co-creation and feedback, whilst retaining a rigorous approach to design. Sampling frameworks, target habitats and species, field methods and power are all described. We also evaluate several outcomes of the scheme design process, including: (i) landscape-context bias in the first two years of the scheme; (ii) the ability of different sets of indicator species to capture the main ecological gradients of UK vegetation; and, (iii) species richness bias in returns relative to a professional survey. Survey rates have been promising (over 60% of squares released have been surveyed), although upland squares are under-represented. Ecological gradients present in an ordination of an independent, unbiased, national survey were well-represented by NPMS indicator species, although further filtering to an entry-level set of easily identifiable species degraded signal in an ordination axis representing succession and disturbance. Comparison with another professional survey indicated that different biases might be present at different levels of participation within the scheme. Understanding the strengths and limitations of the NPMS will guide development, increase trust in outputs, and direct efforts for maintaining volunteer interest, as well as providing a set of ideas for other countries to experiment with.

Developing a list of invasive alien species likely to threaten biodiversity and ecosystems in the European Union.

The European Union (EU) has recently published its first list of invasive alien species (IAS) of EU concern to which current legislation must apply. The list comprises species known to pose great threats to biodiversity and needs to be maintained and updated. Horizon scanning is seen as critical to identify the most threatening potential IAS that do not yet occur in Europe to be subsequently risk assessed for future listing. Accordingly, we present a systematic consensus horizon scanning procedure to derive a ranked list of potential IAS likely to arrive, establish, spread and have an impact on biodiversity in the region over the next decade. The approach is unique in the continental scale examined, the breadth of taxonomic groups and environments considered, and the methods and data sources used. International experts were brought together to address five broad thematic groups of potential IAS. For each thematic group the experts first independently assembled lists of potential IAS not yet established in the EU but potentially threatening biodiversity if introduced. Experts were asked to score the species within their thematic group for their separate likelihoods of i) arrival, ii) establishment, iii) spread, and iv) magnitude of the potential negative impact on biodiversity within the EU. Experts then convened for a 2-day workshop applying consensus methods to compile a ranked list of potential IAS. From an initial working list of 329 species, a list of 66 species not yet established in the EU that were considered to be very high (8 species), high (40 species) or medium (18 species) risk species was derived. Here, we present these species highlighting the potential negative impacts and the most likely biogeographic regions to be affected by these potential IAS.

A systematic florula of a disturbed urban habitat: Pavements of Sheffield, England.

BACKGROUND: Human settlements are of increasing interest to ecologists, a fact demonstrated by the recent cluster of book-length treatments of the topic (Forman 2008, McDonnell et al. 2009, Gaston 2010, Niemelä et al. 2011, Wilson 2011, Forman 2014). The natural world as a fascinating feature of towns and cities has a much longer history (e.g. Fitter 1945), and has also played a strong part in local biological conservation in some countries over the late 20th Century (Goode 2014​). Despite much existing information on urban plant and animal communities resulting from these trends, very little, easily accessible, systematic data on urban biodiversity is currently available. NEW INFORMATION: Few systematic, randomised surveys at fine spatial grain exist for urban habitats, and even fewer of these surveys are in the public domain. This study was designed as a systematic florula (i.e. a small flora) of a relatively discrete urban habitat in order to provide a baseline that would enable robust insights into future environmental change. In addition, the dataset is likely to be useful for comparative studies of plant traits, particularly those of highly disturbed habitats (Williams et al. 2009​). The survey is an occupancy study of the vascular plants of pavements (i.e. sidewalks) within 16 500 x 500 m (0.25 km2) urban grid cells, stratified by quadrant at the scale of the focal city (Sheffield, England) in order to provide more even coverage. The final dataset comprises 862 records of 183 taxa.

Assessing the impact of human trampling on vegetation: a systematic review and meta-analysis of experimental evidence.

Vegetation trampling resulting from recreation can adversely impact natural habitats, leading to the loss of vegetation and the degradation of plant communities. A considerable primary literature exists on this topic, therefore it is important to assess whether this accumulated evidence can be used to reach general conclusions concerning vegetation vulnerability to inform conservation management decisions. Experimental trampling studies on a global scale were retrieved using a systematic review methodology and synthesised using random effects meta-analysis. The relationships between vegetation recovery and each of initial vegetation resistance, trampling intensity, time for recovery, Raunkiaer life-form (perennating bud position), and habitat were tested using random effects multiple meta-regressions and subgroup analyses. The systematic search yielded 304 studies; of these, nine reported relevant randomized controlled experiments, providing 188 vegetation recovery effect sizes for analysis. The synthesis indicated there was significant heterogeneity in the impact of trampling on vegetation recovery. This was related to resistance and recovery time, and the interactions of these variables with Raunkiaer life-form, but was not strongly dependent on the intensity of the trampling experienced. The available evidence suggests that vegetation dominated by hemicryptophytes and geophytes recovers from trampling to a greater extent than vegetation dominated by other life-forms. Variation in effect within the chamaephyte, hemicryptophyte and geophyte life-form sub-groups was also explained by the initial resistance of vegetation to trampling, but not by trampling intensity. Intrinsic properties of plant communities appear to be the most important factors determining the response of vegetation to trampling disturbance. Specifically, the dominant Raunkiaer life-form of a plant community accounts for more variation in the resilience of communities to trampling than the intensity of the trampling experienced, suggesting that simple assessments based on this trait could guide decisions concerning sustainable access to natural areas. Methodological and reporting limitations must be overcome before more disparate types of evidence can be synthesised; this would enable more reliable extrapolation to non-study situations, and a more comprehensive understanding of how assessments of intrinsic plant traits can be used to underpin conservation management decisions concerning access.