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

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. 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

Dear Dr Perring… correspondence in the Atlas of the British Flora archive

An overview of original correspondence (held at CEH Wallingford) relating to the 1962 Atlas of the British Flora (Perring & Walters 1962) data collection process

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 accou URL link.nt for spatial autocorrelation, and a clearer focus on the aims of a study, whether explanatory or predictive, are all required

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

\ua9 2023 The Authors. Ecology published by Wiley Periodicals LLC on behalf of The Ecological Society of America.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

Grasslands of the Arieş Valley and the Comana Natural Park, Romania: a Stapledon Travelling Fellowship report

Broadly, the aims of this Stapledon Memorial Trust Travelling Fellowship were to learn about and investigate the grasslands of two areas of Romania, with a particular focus on the impacts of non-native species where these were present. The two areas visited during this trip were the Arieş and Ampoi river systems in north-west Romania, and the Comana Natural Park, a large area of wetlands and associated habitats south of Bucharest. The report takes the form of a photo diary, with a particular focus on plants, habitats, and incidents that represent the highlights of both my learning and the trip

COST Action short term scientific mission – Alien plant species in Overseas Territories in Cyprus, 5th-18th March 2017

Two weeks were spent in March 2017 surveying alien species in Cyprus, building on work conducted during an earlier mission. The surveys were directed within four key areas: the Akrotiri Forest; phrygana within both the Akrotiri and Dhekelia Sovereign Base Areas (SBAs); and the Fassouri marsh

Improving species distribution models for invasive non‐native species with biologically informed pseudo‐absence selection

Aim: We present a novel strategy for species distribution models (SDMs) aimed at predicting the potential distributions of range‐expanding invasive non‐native species (INNS). The strategy combines two established perspectives on defining the background region for sampling “pseudo‐absences” that have hitherto only been applied separately. These are the accessible area, which accounts for dispersal constraints, and the area outside the environmental range of the species and therefore assumed to be unsuitable for the species. We tested an approach to combine these by fitting SDMs using background samples (pseudo‐absences) from both types of background. Location: Global. Taxon: Invasive non‐native plants: Humulus scandens, Lygodium japonicum, Lespedeza cuneata, Triadica sebifera, Cinnamomum camphora. Methods: Presence‐background (or presence‐only) SDMs were developed for the potential global distributions of five plant species native to Asia, invasive elsewhere and prioritised for risk assessment as emerging INNS in Europe. We compared models where the pseudo‐absences were selected from the accessible background, the unsuitable background (defined using biological knowledge of the species’ key limiting factors) or from both types of background. Results: Combining the unsuitable and accessible backgrounds expanded the range of environments available for model fitting and caused biological knowledge about ecological unsuitability to influence the fitted species‐environment relationships. This improved the realism and accuracy of distribution projections globally and, generally, within the species’ ranges. Main conclusions: Correlative SDMs remain valuable for INNS risk mapping and management, but are often criticised for a lack of biological underpinning. Our approach partly addresses this concern by using prior knowledge of species’ requirements or tolerances to define the unsuitable background for modelling, while also accommodating dispersal constraints through considerations of accessibility. It can be implemented with current SDM software and results in more accurate and realistic distribution projections. As such, wider adoption has potential to improve SDMs that support INNS risk assessment