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

Technical note: A bootstrapped LOESS regression approach for comparing soil depth profiles

Understanding the consequences of different land uses for the soil system is important to make better informed decisions based on sustainability. The ability to assess change in soil properties, throughout the soil profile, is a critical step in this process. We present an approach to examine differences in soil depth profiles between land uses using bootstrapped LOESS regressions (BLRs). This non-parametric approach is data-driven, unconstrained by distributional model parameters and provides the ability to determine significant effects of land use at specific locations down a soil profile. We demonstrate an example of the BLR approach using data from a study examining the impacts of bioenergy land use change on soil organic carbon (SOC). While this straightforward non-parametric approach may be most useful in comparing SOC profiles between land uses, it can be applied to any soil property which has been measured at satisfactory resolution down the soil profile. It is hoped that further studies of land use and land management, based on new or existing data, can make use of this approach to examine differences in soil profiles

Feather corticosterone content in predatory birds in relation to body condition and hepatic metal concentration

This study investigated the feasibility of measuring corticosterone in feathers from cryo-archived raptor specimens, in order to provide a retrospective assessment of the activity of the stress axis in relation to contaminant burden. Feather samples were taken from sparrowhawk Accipiter nisus, kestrel Falco tinnunculus, buzzard Buteo buteo, barn owl Tyto alba, and tawny owl Strix aluco and the variation in feather CORT concentrations with respect to species, age, sex, feather position, and body condition was assessed. In sparrowhawks only, variation in feather CORT content was compared with hepatic metal concentrations. For individuals, CORT concentration (pg mm-1) in adjacent primary flight feathers (P5 and P6), and left and right wing primaries (P5), was statistically indistinguishable. The lowest concentrations of CORT were found in sparrowhawk feathers and CORT concentrations did not vary systematically with age or sex for any species. Significant relationships between feather CORT content and condition were observed in only tawny owl and kestrel. In sparrowhawks, feather CORT concentration was found to be positively related to the hepatic concentrations of five metals (Cd, Mn, Co, Cu, Mo) and the metalloid As. There was also a negative relationship between measures of condition and total hepatic metal concentration in males. The results suggest that some factors affecting CORT uptake by feathers remain to be resolved but feather CORT content from archived specimens has the potential to provide a simple effects biomarker for exposure to environmental contaminants

Dependence of ombrotrophic peat nitrogen on phosphorus and climate

Nitrogen (N) is a key, possibly limiting, nutrient in ombrotrophic peat ecosystems, and enrichment by pollutant N in atmospheric deposition (Ndep, g m-2 a-1) is of concern with regard to peatland damage. We collated data on the N content of surface (depth ≤ 25 cm, mean 15 cm) ombrotrophic peat (Nsp) for 215 sites in the UK and 62 other sites around the world, including boreal, temperate and tropical locations (wider global data), and found Nsp to range from 0.5 % to 4%. We examined the dependences of Nsp on surface peat phosphorus (P) content (Psp), mean annual precipitation (MAP), mean annual temperature (MAT) and Ndep. Linear regression on individual independent variables showed highly significant (p < 0.001) correlations of Nsp with Psp (r2 = 0.23) and MAP (r2 = 0.14), and significant (p < 0.01) but weaker correlations with MAT (r2 = 0.03) and Ndep (r2 = 0.03). A multiple regression model using log-transformed values explained 36% of the variance of the UK data, 84% of the variance of the wider global data, and 47% of the variance of the combined data, all with high significance (p < 0.001). In all three cases, most of the variance was explained by Psp and MAP, but in view of a positive correlation between MAP and MAT for many of the sites, a role for MAT in controlling Nsp cannot be ruled out. There is little evidence for an effect of Ndep on Nsp. The results point to a key role of P in N fixation, and thereby C fixation, in ombrotrophic peats

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

Spatial controls on dissolved organic carbon in upland waters inferred from a simple statistical model

Dissolved organic carbon (DOC) concentrations in upland surface waters in many northern hemisphere industrialised regions are at their highest in living memory, provoking debate over their ‘‘naturalness’’. Because of the implications for drinking water treatment and supply there is increasing interest in the potential for mitigation through local land management, and for forecasting the likely impact of environmental change. However, the dominant controls on DOC production remain unresolved, hindering the establishment of appropriate reference levels for specific locations. Here we demonstrate that spatial variation in long-term average DOC levels draining upland UK catchments is highly predictable using a simplemultiple logistic regression model comprising variables representing wetland soil cover, rainfall, altitude, catchment sensitivity to acidification and current acid deposition. A negative relationship was observed between DOC concentration and altitude that, for catchments dominated by organo-mineral soils, is plausibly explained by the combined effects of changing net primary production and temperature-dependent decomposition. However, the magnitude of the altitude effect was considerably greater for catchments with a high proportion ofwetland cover, suggesting that additional controls influence these sites such as impeded respiratory loss of carbon in wet soils and/or an increased susceptibility to water level drawdown at lower altitudes. The model suggests (1) that continuing reductions in sulphur deposition on acid sensitive organo-mineral soils, will drive further significant increases in DOC and, (2) given the differences in the magnitude of the observed altitude-DOC relationships, that DOC production from catchments with peatdominated soilsmay bemore sensitive to climate change than those dominated by mineral soils. However, given that mechanisms remain unclear, the latter warrants further investigation

Pathways to achieving nature-positive and carbon–neutral land use and food systems in Wales

Land use and its management can play a vital role in carbon sequestration, but trade-offs may exist with other objectives including food security and nature recovery. Using an integrated model (the FABLE calculator), four pathways, co-created with colleagues at the Welsh Government, towards achieving climate and biodiversity targets in Wales were explored: status quo, improvements on current trends, land sparing and land sharing. We found that continuing as usual will not be sufficient to meet Wales’s climate and biodiversity targets. In contrast, the land use and agricultural sector became a net carbon sink in both the land sparing and land sharing pathways, through high afforestation targets, peatland restoration, reducing food waste and moving towards a healthier diet. Whilst both pathways released land for biodiversity, the gains were greater in the land sharing pathway, which was also less dependent on optimistic assumptions concerning productivity improvements. The results demonstrate that alternative approaches to achieving nature-positive and carbon–neutral land use and food systems may be possible, but they come with stringent and transformative requirements for policy changes, with an integrated approach necessary to maximise benefits for climate, food and nature

A new approach to characterising and predicting crop rotations using national-scale annual crop maps

Cropping decisions affect the nature, timing and intensity of agricultural management strategies. Specific crop rotations are associated with different environmental impacts, which can be beneficial or detrimental. The ability to map, characterise and accurately predict rotations enables targeting of mitigation measures where most needed and forecasting of potential environmental risks. Using six years of the national UKCEH Land Cover® plus: Crops maps (2015–2020), we extracted crop sequences for every agricultural field parcel in Great Britain (GB). Our aims were to first characterise spatial patterns in rotation properties over a national scale based on their length, type and structural diversity values, second, to test an approach to predicting the next crop in a rotation, using transition probability matrices, and third, to test these predictions at a range of spatial scales. Strict cyclical rotations only occupy 16 % of all agricultural land, whereas long-term grassland and complex-rotational agriculture each occupy over 40 %. Our rotation classifications display a variety of distinctive spatial patterns among rotation lengths, types and diversity values. Rotations are mostly 5 years in length, short mixed crops are the most abundant rotation type, and high structural diversity is concentrated in east Scotland. Predictions were most accurate when using the most local spatial approach (spatial scaling), 5-year rotations, and including long-term grassland. The prediction framework we built demonstrates that our crop predictions have an accuracy of 36–89 %, equivalent to classification accuracy of national crop and land cover mapping using earth observation, and we suggest this could be improved with additional contextual data. Our results emphasise that rotation complexity is multi-faceted, yet it can be mapped in different ways and forms the basis for further exploration in and beyond agronomy, ecology, and other disciplines

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