A visualization platform to analyze contextual links between natural capital and ecosystem services

To prevent further loss of our vital ecosystem services we must understand the linkages to their supporting natural capital attributes. Systematic literature reviews synthesise evidence of natural capital attribute to ecosystem service (NC-ES) linkages. However, such reviews rarely account for the context dependency of evidence that is derived from individual studies undertaken for a particular purpose, at a specific spatial scale or geographic location. To address this deficiency, we developed the LiNCAGES (Linking Natural Capital Attribute Groups to Ecosystem Services) platform for investigating the context dependency of literature-based evidence for NC-ES linkages. We demonstrate the application of the LiNCAGES platform using the OpenNESS systematic literature review of NC-ES linkages. A hypothetical use case scenario of a small-scale European forest manager is described. We find evidence for many NC-ES linkages, and trade-offs and synergies between services, is severely diminished or non-existent under certain contexts, such as larger spatial scales and European study location. The LiNCAGES platform provides a flexible tool that researchers can use to support collation, exploration and synthesis of literature-based evidence on NC-ES linkages. This is vital for providing credible and salient evidence to stakeholders on important NC-ES linkages that occur under their context, to guide effective management strategies

Common plants as indicators of habitat suitability for rare plants; quantifying the strength of the association between threatened plants and their neighbours

Rare plants are vulnerable to environmental change but easy to over-look during survey. Methods are therefore needed that can provide early warnings of population change and identify potentially suitable vegetation that could support new or previously overlooked populations. We developed an indicator species approach based on quantifying the association between rare plants across their British ecological range and their suite of more common neighbours. We combined quadrat data, targeted on six example species selected from the Botanical Society of Britain and Ireland's Threatened Plant Project (TPP), with representative survey data from across Britain. Bayes Theorem was then used to calculate the probability that the rare species would occur given the presence of an associated species that occurred at least once with the rare species in the TPP quadrats. These values can be interpreted as indicators of habitat suitability rather than expectations of species presence. Probability values for each neighbour species are calculated separately and are therefore unaffected by biased recording of other species. The method can still be applied if only a subset of species is recorded, for example, where weaker botanists record a pre-selected subset of more easily identifiable neighbour species. Disadvantages are that the method is constrained by the availability of quadrats currently targeted on rare species and results are influenced by any recording biases associated with existing quadrat data

Changes in the frequency of common plant species across linear features in Wales from 1990 to 2016: implications for potential delivery of ecosystem services

In 2016, 21 1km squares recorded in Wales as part of the Countryside Survey of Great Britain were revisited. One hundred and thirty seven quadrats alongside linear features that had all been recorded in the same place in 1990, 1998 and 2007 were re-found and the plant species compositions recorded. Changes in individual species frequency were analysed and the results summarised by a number of ecosystem services and one disservice whose delivery are linked to functionally important species being present. Results indicated a continuation of a trend toward increased shading and woody cover seen between the first Countryside Survey in 1978 and the last in 2007. Most species showed no significant change in frequency suggesting that the significant directional trend seems only to have impacted a subset of the species present. A greater sample size would be required to capture impacts on a larger number of species including a wider range of Common Standards Monitoring (CSM) positive indicator species that may find refuge on the linear network in lowland Wales. Having grouped species by the ecosystem services they help deliver, we found that injurious weeds (an ecosystem disservice to food production) either declined or remained stable, a greater number of butterfly larval food plants decreased than increased and there was a net decline in potential nectar yield. Consistent with the successional trend, increasing species in these service-providing groups tended to be tall or shade-tolerant herbs and tree species. Decreasing species tended to be short, shade-intolerant forbs

Environment and Rural Affairs Monitoring & Modelling Programme - ERAMMP Report-30: Analysis of National Monitoring Data in Wales for the State of Natural Resources Report 2020

The Glastir Monitoring and Evaluation Programme (GMEP, https://gmep.wales/) was at the forefront of the ecosystem approach to monitoring the impact of Pillar II schemes across the European Union - as recognised by the European Commission’s Monitoring and Evaluation Help Desk. GMEP also recruited a large sample of counterfactual “wider Wales” sites, thus enabling additional all Wales reporting. GMEP and other assimilated data represents a significant source of robust, timely and spatially relevant evidence which can contribute to SoNaRR. To facilitate use of GMEP data in SoNaRR, we present new analyses of national monitoring data which has been co-developed with SoNaRR technical leads at Natural Resources Wales (NRW)

Niche models for British plants and lichens obtained using an ensemble approach

Site-occupancy models that predict habitat suitability for plant species in relation to measurable environmental factors can be useful for conservation planning. Such models can be derived from large-scale presence–absence datasets on the basis of environmental observations or, where only floristic data are available, using plant trait values averaged across a plot. However, the estimated modelled relationship between species presence and environmental variables depends on the type of statistical model adopted and hence can introduce additional uncertainty. Authors used an ensemble-modelling approach to constrain and quantify the uncertainty because of the choice of statistical model, applying generalised linear models (GLM), generalised additive models (GAM), and multivariate adaptive regression splines (MARS). Niche models were derived for over 1000 species of vascular plants, bryophytes and lichens, representing a large proportion of the British flora and many species occurring in continental Europe. Each model predicts habitat suitability for a species in response to climate variables and trait-based scores (evaluated excluding the species being modelled) for soil pH, fertility, wetness and canopy height. An R package containing the fitted models for each species is presented which allows the user to predict the habitat suitability of a given set of conditions for a particular species. Further functions within the package are included so that these habitat suitability scores can be plotted in relation to individual explanatory variables. A simple case study shows how the R package (MultiMOVE) can be used quickly and efficiently to answer questions of scientific interest, specifically whether climate change will counteract any benefits of sheep-grazing for a particular plant community. The package itself is freely available via http://doi.org/10.5285/94ae1a5a-2a28-4315-8d4b-35ae964fc3b9

Identifying effective approaches for monitoring national natural capital for policy use

In order to effectively manage natural resources at national scales national decision makers require data on the natural capital which supports the delivery of ecosystem services (ES). Key data sources used for the provision of national natural capital metrics include Satellite Remote Sensing (SRS), which provides information on land cover at an increasing range of resolutions, and field survey, which can provide very high resolution data on ecosystem components, but is constrained in its potential coverage by resource requirements. Here we combine spatially representative field data from a historic national survey of Great Britain (Countryside Survey (CS)) with concurrent low resolution SRS data land cover map within modelling frameworks to produce national natural capital metrics. We present three examples of natural capital metrics; top soil carbon, headwater stream quality and nectar species plant richness which show how highly resolved, but spatially representative field data can be used to significantly enhance the potential of low resolution SRS land cover data for providing national spatial data on natural capital metrics which have been linked to ecosystem services (ES). We discuss the role of such metrics in evaluations of ecosystem service provision and areas of further development to improve their utility for stakeholders

BICCO-Net II. Final report to the Biological Impacts of Climate Change Observation Network (BICCO-Net) Steering Group

• BICCO-Net Phase II presents the most comprehensive single assessment of climate change impacts on UK biodiversity to date. • The results provide a valuable resource for the CCRA 2018, future LWEC report cards, the National Adaptation Programme and other policy-relevant initiatives linked to climate change impacts on biodiversity

A new approach to modelling the relationship between annual population abundance indices and weather data

Weather has often been associated with fluctuations in population sizes of species; however, it can be difficult to estimate the effects satisfactorily because population size is naturally measured by annual abundance indices whilst weather varies on much shorter timescales. We describe a novel method for estimating the effects of a temporal sequence of a weather variable (such as mean temperatures from successive months) on annual species abundance indices. The model we use has a separate regression coefficient for each covariate in the temporal sequence, and over-fitting is avoided by constraining the regression coefficients to lie on a curve defined by a small number of parameters. The constrained curve is the product of a periodic function, reflecting assumptions that associations with weather will vary smoothly throughout the year and tend to be repetitive across years, and an exponentially decaying term, reflecting an assumption that the weather from the most recent year will tend to have the greatest effect on the current population and that the effect of weather in previous years tends to diminish as the time lag increases. We have used this approach to model 501 species abundance indices from Great Britain and present detailed results for two contrasting species alongside an overall impression of the results across all species. We believe this approach provides an important advance to the challenge of robustly modelling relationships between weather and species population size