Bias and information in biological records

Biological recording is in essence a very simple concept in which a record is the report of a species at a physical location at a certain time. The collation of these records into a dataset is a powerful approach to addressing large-scale questions about biodiversity change. Records are collected by volunteers at times and places that suit them, leading to a variety of biases: uneven sampling over space and time, uneven sampling effort per visit and uneven detectability. These need to be controlled for in statistical analyses that use biological records. In particular, the data are ‘presence-only’, and lack information on the sampling protocol or intensity. Submitting ‘complete lists’ of all the species seen is one potential solution because the data can be treated as ‘presence–absence’ and detectability of each species can be statistically modelled. The corollary of bias is that records vary in their ‘information content’. The information content is a measure of how much an individual record, or collection of records, contributes to reducing uncertainty in a parameter of interest. The information content of biological records varies, depending on the question to which the data are being applied. We consider a set of hypothetical ‘syndromes’ of recording behaviour, each of which is characterized by different information content. We demonstrate how these concepts can be used to support the growth of a particular type of recording behaviour. Approaches to recording are rapidly changing, especially with the growth of mass participation citizen science. We discuss how these developments present a range of challenges and opportunities for biological recording in the future

Butterfly abundance in a warming climate: patterns in space and time are not congruent

We present a model of butterfly abundance on transects in England. The model indicates a significant role for climate, but the direction of association is counter to expectation: butterfly population density is higher on sites with a cooler climate. However, the effect is highly heterogeneous, with one in five species displaying a net positive association. We use this model to project the population-level effects of climate warming for the year 2080, using a medium emissions scenario. The results suggest that most populations and species will decline markedly, but that the total number of butterflies will increase as communities become dominated by a few common species. In particular, Maniola jurtina is predicted to make up nearly half of all butterflies on UK Butterfly Monitoring Scheme (UKBMS) transects by 2080. These results contradict the accepted wisdom that most insect populations will grow as the climate becomes warmer. Indeed, our predictions contrast strongly with those derived from inter-annual variation in abundance, emphasizing that we lack a mechanistic understanding about the factors driving butterfly population dynamics over large spatial and temporal scales. Our study underscores the difficulty of predicting future population trends and reveals the naivety of simple space-for-time substitutions, which our projections share with species distribution modelling

Rapid Anthropocene realignment of allometric scaling rules

The negative relationship between body size and population density in mammals is often interpreted as resulting from energetic constraints. In a global change scenario, however, this relationship might be expected to change, given the size-dependent nature of anthropogenic pressures and vulnerability to extinction. Here we test whether the size-density relationship (SDR) in mammals has changed over the last 50 years. We show that the relationship has shifted down and became shallower, corresponding to a decline in population density of 31–73%, for the largest and smallest mammals, respectively. However, the SDRs became steeper in some groups (e.g. carnivores) and shallower in others (e.g. herbivores). The Anthropocene reorganisation of biotic systems is apparent in macroecological relationships, reinforcing the notion that biodiversity pattens are contingent upon conditions at the time of investigation. We call for an increased attention to the role of global change on macroecological inferences

Trait correlates of distribution trends in the Odonata of Britain and Ireland

A major challenge in ecology is understanding why certain species persist, while others decline, in response to environmental change. Trait-based comparative analyses are useful in this regard as they can help identify the key drivers of decline, and highlight traits that promote resistance to change. Despite their popularity trait-based comparative analyses tend to focus on explaining variation in range shift and extinction risk, seldom being applied to actual measures of species decline. Furthermore they have tended to be taxonomically restricted to birds, mammals, plants and butterflies. Here we utilise a novel approach to estimate occurrence trends for the Odonata in Britain and Ireland, and examine trait correlates of these trends using a recently available trait dataset. We found the dragonfly fauna in Britain and Ireland has undergone considerable change between 1980 and 2012, with 22 and 53% of species declining and increasing, respectively. Distribution region, habitat specialism and range size were the key traits associated with these trends, where habitat generalists that occupy southern Britain tend to have increased in comparison to the declining narrow-ranged specialist species. In combination with previous evidence, we conclude that the lower trend estimates for the narrow-ranged specialists could be a sign of biotic homogenization with ecological specialists being replaced by warm-adapted generalists

Assessing the usefulness of citizen science data for habitat suitability modelling: opportunistic reporting versus sampling based on a systematic protocol

Aim: To evaluate the potential of models based on opportunistic reporting (OR) compared to models based on data from a systematic protocol (SP) for modelling species distributions. We compared model performance for eight forest bird species with contrasting spatial distributions, habitat requirements and rarity. Differences in the reporting of species were also assessed. Finally, we tested potential improvement of models when inferring high‐quality absences from OR based on questionnaires sent to observers. Location: Both datasets cover the same large area (Sweden) and time period (2000–2013). Methods: Species distributions were modelled using logistic regression. Predictive performance of OR models to predict SP data was assessed based on AUC. We quantified the congruence in spatial predictions using Spearman's rank correlation coefficient. We related these results to species characteristics and reporting behaviour of observers. We also assessed the gain in predictive performance of OR models by adding inferred absences. Finally, we investigated the potential impact of sampling bias in OR. Results: For all species, and despite the sampling biases, results from OR overall agreed well with those of SP, for the nationwide spatial congruence of habitat suitability maps and the selection and directions of species–environment relationships. The OR models also performed well in predicting the SP data. The predictive performance of the OR models increased with species rarity and even outperformed the SP model for the rarest species. No significant impact of observer behaviour was found. Main conclusions: Relatively simple analyses with inferred absences could produce reliable spatial predictions of habitat suitability. This was especially true for rare species. OR data should be seen as a complement to SP, as the weakness of one is the strength of the other, and OR may be especially useful at large spatial scales or where no systematic data collection protocols exist

Agrochemicals in the wild: identifying links between pesticide use and declines of nontarget organisms

Agricultural pesticides are a key component of the toolbox of most agricultural systems and are likely to continue to play a role in meeting the challenge of feeding a growing global population. However, pesticide use has well documented and often significant consequences for populations of native wildlife. Although rigorous, regulatory processes for the approval of new chemicals for agronomic use do have limitations which may fail to identify real world negative effects of products. Here, we describe a possible approach to complement the existing regulatory process, which is to combine long-term and national-scale data sets on native wildlife with pesticide use data to understand long-term and large-scale impacts of agrochemicals on wildlife populations

TetraDENSITY: a database of population density estimates in terrestrial vertebrates

Motivation: Population density is a key demographic parameter influencing many ecological processes, and macroecology has described both intra‐ and interspecific patterns of variation. Population density data are expensive to collect and contain many forms of noise and potential bias; these factors have impeded investigation of macroecological patterns, and many hypotheses remain largely unexplored. Population density also represents fundamental information for conservation, because it underlies population dynamics and, ultimately, extinction risk. Here we present TetraDENSITY, an extensive dataset with >18,000 records of density estimates for terrestrial vertebrates, in order to facilitate new research on this topic. Main types of variable contained: The dataset includes taxonomic information on species, population density estimate, year of data collection, season, coordinates of the locality, locality name, habitat, sampling method and sampling area. Spatial location and grain: Global. Spatial accuracy varies across studies; conservatively, it can be considered at 1°, but for many data it is much finer. Time period and grain: From 1926 to 2017. Temporal accuracy is yearly in most cases, but studies with higher temporal resolution (season, month) are also present. Major taxa and level of measurement: Amphibians in terrestrial phase, reptiles, birds and mammals. Estimates derive from multiple methods, reflecting the study taxon, location and techniques available at the time of density estimation

Butterfly abundance is determined by food availability and is mediated by species traits

1. Understanding the drivers of population abundance across species and sites is crucial for effective conservation management. At present, we lack a framework for predicting which sites are likely to support abundant butterfly communities. 2. We address this problem by exploring the determinants of abundance among 1111 populations of butterflies in the UK, spanning 27 species on 54 sites. Our general hypothesis is that the availability of food resources is a strong predictor of population abundance both within and between species, but that the relationship varies systematically with species’ traits. 3. We found strong positive correlations between butterfly abundance and the availability of food resources. Our indices of host plant and nectar are both significant predictors of butterfly population density, but the relationship is strongest for host plants, which explain up to 36% of the inter-site variance in abundance for some species. 4. Among species, the host plant–abundance relationship is mediated by butterfly species traits. It is strongest among those species with narrow diet breadths, low mobility and habitat specialists. Abundance for species with generalist diet and habitat associations is uncorrelated with our host plant index. 5. The host plant–abundance relationship is more pronounced on sites with predominantly north-facing slopes, suggesting a role for microclimate in mediating resource availability. 6. Synthesis and applications. We have shown that simple measures can be used to help understand patterns in abundance at large spatial scales. For some butterfly species, population carrying capacity on occupied sites is predictable from information about the vegetation composition. These results suggest that targeted management to increase host plant availability will translate into higher carrying capacity. Among UK butterflies, the species that would benefit most from such intervention have recently experienced steep declines in both abundance and distribution. The host plant–abundance relationship we have identified is likely to be transferrable to other systems characterized by strong interspecific interactions across trophic levels. This raises the possibility that the quality of habitat patches for specialist species is estimable from rapid assessment of the host plant resource

Is more data always better? A simulation study of benefits and limitations of integrated distribution models

Species distribution models are popular and widely applied ecological tools. Recent increases in data availability have led to opportunities and challenges for species distribution modelling. Each data source has different qualities, determined by how it was collected. As several data sources can inform on a single species, ecologists have often analysed just one of the data sources, but this loses information, as some data sources are discarded. Integrated distribution models (IDMs) were developed to enable inclusion of multiple datasets in a single model, whilst accounting for different data collection protocols. This is advantageous because it allows efficient use of all data available, can improve estimation and account for biases in data collection. What is not yet known is when integrating different data sources does not bring advantages. Here, for the first time, we explore the potential limits of IDMs using a simulation study integrating a spatially biased, opportunistic, presence‐only dataset with a structured, presence–absence dataset. We explore four scenarios based on real ecological problems; small sample sizes, low levels of detection probability, correlations between covariates and a lack of knowledge of the drivers of bias in data collection. For each scenario we ask; do we see improvements in parameter estimation or the accuracy of spatial pattern prediction in the IDM versus modelling either data source alone? We found integration alone was unable to correct for spatial bias in presence‐only data. Including a covariate to explain bias or adding a flexible spatial term improved IDM performance beyond single dataset models, with the models including a flexible spatial term producing the most accurate and robust estimates. Increasing the sample size of presence–absence data and having no correlated covariates also improved estimation. These results demonstrate under which conditions integrated models provide benefits over modelling single data sources

The use of opportunistic data for IUCN Red List assessments

IUCN Red Lists are recognized worldwide as powerful instruments for the conservation of species. Quantitative criteria to standardize approaches for estimating population trends, geographic ranges and population sizes have been developed at global and sub-global levels. Little attention has been given to the data needed to estimate species trends and range sizes for IUCN Red List assessments. Few regions collect monitoring data in a structured way and usually only for a limited number of taxa. Therefore, opportunistic data are increasingly used for estimating trends and geographic range sizes. Trend calculations use a range of proxies: (i) monitoring sentinel populations, (ii) estimating changes in available habitat, or (iii) statistical models of change based on opportunistic records. Geographic ranges have been determined using: (i) marginal occurrences, (ii) habitat distributions, (iii) range-wide occurrences, (iv) species distribution modelling (including site-occupancy models), and (v) process-based modelling. Red List assessments differ strongly among regions (Europe, Britain and Flanders, north Belgium). Across different taxonomic groups, in European Red Lists IUCN criteria B and D resulted in the highest level of threat. In Britain, this was the case for criterion D and criterion A, while in Flanders criterion B and criterion A resulted in the highest threat level. Among taxonomic groups, however, large differences in the use of IUCN criteria were revealed. We give examples from Europe, Britain and Flemish Red List assessments using opportunistic data and give recommendations for a more uniform use of IUCN criteria among regions and among taxonomic groups