Susan G. Jarvis

Spatial patterns and environmental constraints on ecosystem services at a catchment scale.

Improved understanding and prediction of the fundamental environmental controls on ecosystem service supply across the landscape will help to inform decisions made by policy makers and land-water managers. To evaluate this issue for a local catchment case study, we explored metrics and spatial patterns of service supply for water quality regulation, agriculture production, carbon storage, and biodiversity for the Macronutrient Conwy catchment. Methods included using ecosystem models such as LUCI and JULES, integration of national scale field survey datasets, earth observation products and plant trait databases, to produce finely resolved maps of species richness and primary production. Analyses were done with both 1×1km gridded and subcatchment data. A common single gradient characterised catchment scale ecosystem services supply with agricultural production and carbon storage at opposing ends of the gradient as reported for a national-scale assessment. Species diversity was positively related to production due to the below national average productivity levels in the Conwy combined with the unimodal relationship between biodiversity and productivity at the national scale. In contrast to the national scale assessment, a strong reduction in water quality as production increased was observed in these low productive systems. Various soil variables were tested for their predictive power of ecosystem service supply. Soil carbon, nitrogen, their ratio and soil pH all had double the power of rainfall and altitude, each explaining around 45% of variation but soil pH is proposed as a potential metric for ecosystem service supply potential as it is a simple and practical metric which can be carried out in the field with crowd-sourcing technologies now available. The study emphasises the importance of considering multiple ecosystem services together due to the complexity of covariation at local and national scales, and the benefits of exploiting a wide range of metrics for each service to enhance data robustness.

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

Abstract 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.

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

Abstract 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 Irelands 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.