Integration of ground survey and remote sensing derived data: producing robust indicators of habitat extent and condition

The availability of suitable habitat is a key predictor of the changing status of biodiversity. Quantifying habitat availability over large spatial scales is, however, challenging. Although remote sensing techniques have high spatial coverage, there is uncertainty associated with these estimates due to errors in classification. Alternatively, the extent of habitats can be estimated from ground‐based field survey. Financial and logistical constraints mean that on‐the‐ground surveys have much lower coverage, but they can produce much higher quality estimates of habitat extent in the areas that are surveyed. Here, we demonstrate a new combined model which uses both types of data to produce unified national estimates of the extent of four key habitats across Great Britain based on Countryside Survey and Land Cover Map. This approach considers that the true proportion of habitat per km2 (Zi) is unobserved, but both ground survey and remote sensing can be used to estimate Zi. The model allows the relationship between remote sensing data and Zi to be spatially biased while ground survey is assumed to be unbiased. Taking a statistical model‐based approach to integrating field survey and remote sensing data allows for information on bias and precision to be captured and propagated such that estimates produced and parameters estimated are robust and interpretable. A simulation study shows that the combined model should perform best when error in the ground survey data is low. We use repeat surveys to parameterize the variance of ground survey data and demonstrate that error in this data source is small. The model produced revised national estimates of broadleaved woodland, arable land, bog, and fen, marsh and swamp extent across Britain in 2007

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

Empirical realised niche models for British coastal plant species

Coastal environments host plant taxa adapted to a wide range of salinity conditions. Salinity, along with other abiotic variables, constrains the distribution of coastal plants in predictable ways, with relatively few taxa adapted to the most saline conditions. However, few attempts have been made to quantify these relationships to create niche models for coastal plants. Quantification of the effects of salinity, and other abiotic variables, on coastal plants is essential to predict the responses of coastal ecosystems to external drivers such as sea level rise. We constructed niche models for 132 coastal plant taxa in Great Britain based on eight abiotic variables. Paired measurements of vegetation composition and abiotic variables are rare in coastal habitats so four of the variables were defined using community mean values for Ellenberg indicators, i.e. scores assigned according to the typical alkalinity, fertility, moisture availability and salinity of sites where a species occurs. The remaining variables were the canopy height, annual precipitation, and maximum and minimum temperatures. Salinity and moisture indicator scores were significant terms in over 80 % of models, suggesting the distributions of most coastal species are at least partly determined by these variables. When the models were used to predict species occurrence against an independent dataset 64 % of models gave moderate to good predictions of species occurrence. This indicates that most models had successfully captured the key determinants of the niche. The models could potentially be applied to predict changes to habitats and species-dependent ecosystem services in response to rising sea levels

Drivers of vegetation change in grasslands of the Sheffield region, northern England, between 1965 and 2012/13

Questions: How has vegetation species diversity and species composition changed between 1965 and 2012/13 in acidic and calcareous grasslands? What has driven this change in vegetation? Location: A 2400-km2 area around Sheffield, northern England. Methods: In 1965 a survey was conducted to describe grassland vegetation of the Sheffield region. We repeated this survey in 2012/13, revisiting acidic and calcareous grassland sites (455 quadrats). Climate, N and sulphur deposition, cattle and sheep stocking rates, soil pH, altitude, aspect and slope were considered to be potential drivers of variation in vegetation. We analysed temporal changes in vegetation and examined relationships with spatial and temporal variation in driver variables. Results: Both acidic and calcareous grasslands showed clear changes in species composition between the two time periods. In acidic grasslands there was no significant change in richness but there were declines in diversity. There were significant increases in Ellenberg N. Nitrogen deposition and grazing were identified as potential drivers of spatial and temporal patterns but it was not possible to discriminate the respective impacts of potential drivers. In calcareous grasslands there were declines in species richness, diversity and appropriate diversity indices. Climate and soil pH were identified as potential drivers of spatial and temporal patterns. Conclusions: Despite only small site losses compared to other surveys in the UK, especially within the national park, both calcareous and acidic grasslands showed very clear changes in species composition. In acidic grasslands, high abundance of Pteridium aquilinum was a particular problem and had increased considerably between the two survey periods. Atmospheric N deposition and grazing were identified as drivers of species diversity. A number of calcareous grasslands showed signs of reduced management intensity leading to scrub invasion

Rainfall and temperature effects on fruit body production by stipitate hydnoid fungi in Inverey Wood, Scotland

Stipitate hydnoid fungi are considered to be rare in the United Kingdom based on infrequent and localised observations of fruit bodies. Here, we investigate whether the production of stipitate hydnoid fruit bodies is related to weather conditions using a 14 year standardised survey of 11 species associated with Scots pine. Fruit body production was highly variable over time and asynchronous between species. Relationships with climatic predictors were variable between species, however both overall abundance and species richness of stipitate hydnoid fruit bodies were related to rainfall. These results suggest that climatic conditions in the preceding months can influence the likelihood of observing stipitate hydnoid fruit bodies, but that a large part of variation in fruiting of these taxa remains unexplained

Spatio-temporal data integration for species distribution modelling in R-INLA

1. 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. 2. 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. 3. 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. 4. 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

Integrated species distribution models: a comparison of approaches under different data quality scenarios

Aim: Integrated species distribution modelling has emerged as a useful tool for ecologists to exploit the range of information available on where species occur. In particular, the ability to combine large numbers of ad hoc or presence‐only (PO) records with more structured presence–absence (PA) data can allow ecologists to account for biases in PO data which often confound modelling efforts. A range of modelling techniques have been suggested to implement integrated species distribution models (IDMs) including joint likelihood models, including one dataset as a covariate or informative prior, and fitting a correlation structure between datasets. We aim to investigate the performance of different types of integrated models under realistic ecological data scenarios. Innovation: We use a virtual ecologist approach to investigate which integrated model is most advantageous under varying levels of spatial bias in PO data, sample size of PA data and spatial overlap between datasets. Main conclusions: Joint likelihood models were the best performing models when spatial bias in PO data was low, or could be modelled, but gave poor estimates when there were unknown biases in the data. Correlation models provided good model estimates even when there were unknown biases and when good quality PA data were spatially limited. Including PO data via an informative prior provided little improvement over modelling PA data alone and was inferior to using either the joint likelihood or correlation approach. Our results suggest that correlation models provide a robust alternative to joint likelihood models when covariates related to effort or detection in PO data are not available. Ecologists should be aware of the limitations of each approach and consider how well biases in the data can be modelled when deciding which type of IDM to use

Adaptive sampling in ecology: key challenges and future opportunities

1. 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. 2. 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. 3. 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. 4. 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

Model-based hypervolumes for complex ecological data

Developing a holistic understanding of the ecosystem impacts of global change requires methods that can quantify the interactions among multiple response variables. One approach is to generate high dimensional spaces, or hypervolumes, to answer ecological questions in a multivariate context. A range of statistical methods has been applied to construct hypervolumes but have not yet been applied in the context of ecological datasets with spatial or temporal structure, for example where the data are nested or demonstrate temporal autocorrelation. We outline an approach to account for data structure in quantifying hypervolumes based on the multivariate normal distribution by including random effects. Using simulated data we show that failing to account for structure in data can lead to biased estimates of hypervolume properties in certain contexts. We then illustrate the utility of these ‘model‐based hypervolumes’ in providing new insights into a case study of afforestation effects on ecosystem properties where the data has a nested structure. We demonstrate that the model‐based generalisation allows hypervolumes to be applied to a wide range of ecological datasets and questions

Veterinary drug therapies used for undesirable behaviours in UK dogs under primary veterinary care

Undesirable behaviours (UBs) in dogs are common and important issues with serious potential welfare consequences for both the dogs and their owners. This study aimed to investigate the usage of drug therapy for UBs in dogs and assess demographic risk factors for drug-prescribed UBs within the dog population under primary-care veterinary care in the UK in 2013. Dogs receiving drug therapy for UB were identified through the retrospective analysis of anonymised electronic patient records in VetCompass™. Risk factor analysis used multivariable logistic regression modelling. The study population comprised 103,597 dogs under veterinary care in the UK during 2013. There were 413 drug-prescribed UBs recorded among 404 dogs. The prevalence of dogs with at least one UB event treated with a drug in 2013 was 0.4%. Multivariable modelling identified 3 breeds with increased odds of drug-prescribed UB compared with crossbred dogs: Toy Poodle (OR 2.75), Tibetan Terrier (OR 2.68) and Shih-tzu (OR 1.95). Increasing age was associated with increased odds of drug-prescribed UB, with dogs ≥ 12 years showing 3.1 times the odds compared with dogs < 3 years. Neutered males (OR 1.82) and entire males (OR 1.50) had increased odds compared with entire females. The relatively low prevalence of dogs with at least one UB event that was treated with a drug in 2013 could suggest that opportunities for useful psychopharmaceutical intervention in UBs may be being missed in first opinion veterinary practice. While bodyweight was not a significant factor, the 3 individual breeds at higher odds of an UB treated with a behaviour modifying drug all have a relatively low average bodyweight. The current results also support previous research of a male predisposition to UBs and it is possible that this higher risk resulted in the increased likelihood of being prescribed a behaviour modifying drug, regardless of neuter status