Susan G. Jarvis

Agri‐environment schemes (AES) provide an important mechanism for environmental improvement with the potential to benefit many taxa, yet evidence of national scale benefits is mixed. Multiple sources of evidence are available to assess AES effects, with different strengths and weaknesses, but most existing studies use a single dataset to evaluate AES impacts. We developed an approach analysing multiple datasets to assess relationships between AES and species abundance, richness and diversity, using the example of butterflies in England. We analysed data from a study specifically designed to assess AES effects (the LandSpAES study) alongside two different citizen science UK Butterfly Monitoring Scheme (UKBMS) surveys. UKBMS surveys were not designed to evaluate AES effects, but they provide better spatial coverage across the agricultural landscapes of England. We compared AES relationships between the three datasets, using a generalised AES gradient method to allow integration of different AES options, including the creation of habitat features such as wildflower strips and the restoration of semi‐natural habitats. We assessed AES effects at both local (1 km) and landscape (3 km) scales. We found that AES in the surrounding landscape was positively associated with butterfly community responses in all three datasets and some evidence that local‐scale AES was positively associated with butterfly richness. The smaller size of the LandSpAES study led to wider confidence bounds around effect sizes, but the careful design provided assurance that potentially confounding effects were accounted for. The wider spatial coverage of the citizen science datasets increased confidence that results can be extrapolated to the national scale. Synthesis and applications. Our results provide support for positive effects of AES on butterflies in England from multiple sources of evidence, providing confidence that these schemes are providing tangible benefits for butterflies. Our recommendations for managers and policy makers are (1) multiple data sources should be considered for AES monitoring and evaluation, exploiting the strengths of different data types; (2) AES intervention over larger spatial areas than individual fields and farms should be considered when aiming to provide benefits for butterflies.

Traditional ecological monitoring employs fixed designs, which do not vary over the survey duration. Adaptive sampling, whereby the data already collected informs a sampling design which changes over the course of the study, can provide a more optimal and flexible survey design but is little used in ecology. We aim to provide an introduction to adaptive sampling for ecologists. We review previous literature and highlight examples of both empirical adaptive approaches, such as adaptive cluster sampling, and more novel model‐based adaptive methods. To conceptualise the process of adaptive sampling we identify four key stages: choice of data, definition of a criterion, selection of new sampling occasions and sampling activity. We discuss each stage in turn and focus on the decisions ecologists need to consider in order to successfully implement an adaptive sampling strategy. We include a full walkthrough of an adaptive sampling example with code provided to demonstrate each step. Adaptive sampling has potential advantages to ecologists but so far has had limited uptake. We review key challenges and barriers to uptake and suggest potential ways forward. We hope our paper will both increase awareness of adaptive sampling methods and provide a useful resource for ecologists considering an adaptive survey design.

Volunteer recorders generate large amounts of biodiversity data through citizen science which is used in conservation planning and policy decision‐making. Unstructured sampling, where the volunteer can record what they want, where they want, leads to spatial unevenness in these data. While there are many statistical techniques to account for the resulting biases, it may be possible to improve datasets by directing a subset of recorders to sample in the most informative locations, known as adaptive sampling. We investigated the potential for adaptive sampling to improve the performance of species distribution models built on citizen science data using simulated ecological communities. We simulated ecological assemblages across Great Britain based on current butterfly data and modelled the distributions of each species. We then simulated the sampling of new data based on five adaptive sampling methods (one empirical method based simply on gap‐filling, and four model‐based methods using various measures from the model outputs) and one non‐adaptive method (a method in which recording continued in the current pattern), and re‐ran the species distribution models. In these, we also varied the rate of recording effort that was distributed according to adaptive sampling. The model predictions using the original and adaptively sampled data were compared to true species distributions to evaluate the performance of each method. We found that all adaptive sampling approaches improved model performance, with greatest improvement for model‐based approaches compared to the empirical sampling method (i.e. simple gap‐filling). All four model‐based adaptive sampling approaches provided similar benefits for model outputs. Improvements in model performance were greatest when the amount of adaptive sampling changed from no uptake to 1% uptake, indicating that only a small amount of change in recorder behaviour is needed to improve model performance. Directing volunteer recorders to places where records are most needed, based on information from model outputs, can improve species distribution models built on citizen science data, even with minimal uptake of suggested locations. Our results therefore suggest that adaptive sampling by recorders could be beneficial for real‐world citizen science datasets.

Species distribution modelling is a highly used tool for understanding and predicting biodiversity change, and recent work has emphasised the importance of understanding how species distributions change over both time and space. Spatio‐temporal models require large amounts of data spread over time and space, and as such are clear candidates to benefit from model‐based integration of different data sources. However, spatio‐temporal models are highly computationally intensive and integrating different data sources can make this approach even more unfeasible to ecologists. Here we demonstrate how the R‐INLA methodology can be used for model‐based data integration for spatio‐temporally explicit modelling of species distribution change. We demonstrate that this method can be applied to both point and areal data with two contrasting case studies, one using the SPDE approach for modelling spatio‐temporal change in the Gatekeeper butterfly (Pyronia tithonus) across Great Britain and the second using a spatio‐temporal areal model to describe change in caddisfly (Trichoptera) populations across the River Thames catchment. We show that in the caddisfly case study integrating together different data sources led to greater understanding of the change in abundance across the River Thames both seasonally and over 5 years of data. However, in the butterfly case study moving to a spatio‐temporal context exacerbated differences between the data sources and resulted in no greater ecological insight into change in the Gatekeeper population. Our work provides a computationally feasible framework for spatio‐temporally explicit integration of data within SDMs and demonstrates both the potential benefits and the challenges in applying this methodology to real ecological data.

In order to better quantify spatial and temporal patterns in freshwater biodiversity, and potential underlying drivers of change, we must utilise the increasingly broad range of data available on freshwater ecosystems. Statistical advances in the field of integrated modelling provide new opportunities to further our understanding through the combined and simultaneous analysis of these diverse datasets. We briefly introduce integrated modelling in the context of freshwater biodiversity and outline the key steps involved in its implementation, from data collection to analysis. We highlight both opportunities and challenges for the application of integrated approaches. To illustrate the potential for integrated models to improve our understanding of freshwater biodiversity compared to standard approaches, we combine two datasets collected using different methods to model the distribution of Agabus water beetles in England. The integrated model had greater power to detect covariate effects on Agabus distribution, and reduced parameter uncertainty compared with analysis using only a single dataset. We show that integrated methods have the potential to increase our understanding of freshwater systems and enable us to make full use of the diversity of freshwater data available.

Traditional strict separation of fungi into ecological niches as mutualist, parasite or saprotroph is increasingly called into question. Sequences of assumed saprotrophs have been amplified from plant root interiors, and several saprotrophic genera can invade and interact with host plants in laboratory growth experiments. However, it is uncertain if root invasion by saprotrophic fungi is a widespread phenomenon and if laboratory interactions mirror field conditions. Here, we focused on the widespread and speciose saprotrophic genus Mycena and performed (1) a systematic survey of their occurrences (in ITS1/ITS2 datasets) in mycorrhizal roots of 10 plant species, and (2) an analysis of natural abundances of ¹³ C/ ¹⁵ N stable isotope signatures of Mycena basidiocarps from five field locations to examine their trophic status. We found that Mycena was the only saprotrophic genus consistently found in 9 out of 10 plant host roots, with no indication that the host roots were senescent or otherwise vulnerable. Furthermore, Mycena basidiocarps displayed isotopic signatures consistent with published ¹³ C/ ¹⁵ N profiles of both saprotrophic and mutualistic lifestyles, supporting earlier laboratory‐based studies. We argue that Mycena are widespread latent invaders of healthy plant roots and that Mycena species may form a spectrum of interactions besides saprotrophy also in the field.

Relationships between area, heterogeneity and species richness are fundamental concepts in ecology yet questions remain about how area and heterogeneity tradeoff (AHTO) to constrain biodiversity. Although there is growing evidence for unimodal heterogeneity diversity relationships (HDR's) and an AHTO, tests of the concept and consequences for species richness across a landscape‐scale gradient of human‐modified ecosystems are rare. Using data from a national (Wales) field survey we analysed relationships between environmental heterogeneity and plant species richness (α and γ). We used ordination to produce a composite metric of heterogeneity and compared this to commonly used metrics. We used niche hypervolumes to categorise the breadth of plant species' ecological preferences and analysed relationships between species richness, niche width and heterogeneity. The HDR was unimodal with α diversity at the smallest scale and positive with α and γ diversity (non‐linear) at the 1 km scale although in low intensity landscapes the HDR with γ diversity was unimodal. There was a unimodal relationship between habitat diversity and γ diversity. Land use intensity was unimodally related to diversity. There were significant interactions between niche width and heterogeneity. Richness of broad niche species increased with heterogeneity with flattening of the curve at higher levels. Narrow niche species were rare and mostly unresponsive. The expected decline in narrow niche species with increasing heterogeneity was not found although they did decline with land‐use intensity. Using a unique dataset, an analysis of a large‐scale mosaic of ecosystems found that the shape of the HDR varies with land use intensity, the heterogeneity metric, spatial scale, diversity type and niche width. Although heterogeneity can increase species richness, there may be tradeoffs at higher heterogeneity. A fundamental constraint on realising the benefit of heterogeneity is the low availability of narrower niche species in local species pools in modified landscapes.

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.