Phillipa K. Gillingham

Protected areas facilitate species’ range expansions

The benefits of protected areas (PAs) for biodiversity have been questioned in the context of climate change because PAs are static, whereas the distributions of species are dynamic. Current PAs may, however, continue to be important if they provide suitable locations for species to colonize at their leading-edge range boundaries, thereby enabling spread into new regions. Here, we present an empirical assessment of the role of PAs as targets for colonization during recent range expansions. Records from intensive surveys revealed that seven bird and butterfly species have colonized PAs 4.2 (median) times more frequently than expected from the availability of PAs in the landscapes colonized. Records of an additional 256 invertebrate species with less-intensive surveys supported these findings and showed that 98% of species are disproportionately associated with PAs in newly colonized parts of their ranges. Although colonizing species favor PAs in general, species vary greatly in their reliance on PAs, reflecting differences in the dependence of individual species on particular habitats and other conditions that are available only in PAs. These findings highlight the importance of current PAs for facilitating range expansions and show that a small subset of the landscape receives a high proportion of colonizations by range-expanding species.

Predicting global invasion risks: a management tool to prevent future introductions.

Predicting regions at risk from introductions of non-native species and the subsequent invasions is a fundamental aspect of horizon scanning activities that enable the development of more effective preventative actions and planning of management measures. The Asian cyprinid fish topmouth gudgeon Pseudorasbora parva has proved highly invasive across Europe since its introduction in the 1960s. In addition to direct negative impacts on native fish populations, P. parva has potential for further damage through transmission of an emergent infectious disease, known to cause mortality in other species. To quantify its invasion risk, in regions where it has yet to be introduced, we trained 900 ecological niche models and constructed an Ensemble Model predicting suitability, then integrated a proxy for introduction likelihood. This revealed high potential for P. parva to invade regions well beyond its current invasive range. These included areas in all modelled continents, with several hotspots of climatic suitability and risk of introduction. We believe that these methods are easily adapted for a variety of other invasive species and that such risk maps could be used by policy-makers and managers in hotspots to formulate increased surveillance and early-warning systems that aim to prevent introductions and subsequent invasions.

Comparison of methods for a landscape-scale assessment of the cultural ecosystem services associated with different habitats

ABSTRACT Cultural ecosystem services (CES) are widely acknowledged as important but are often neglected by ecosystem service assessments, leading to a representational bias. This reflects the methodological challenges associated with producing robust and repeatable CES valuations. Here we provide a comparative analysis of three approaches for non-monetary valuation of CES, namely a structured survey, participatory GIS (PGIS) and GPS tracking methods. These were used to assess both recreation and aesthetic value of habitats within the New Forest National Park, UK. The association of CES with habitats enabled results of all three methods to be visualised at the landscape scale using maps, strengthening their value to conservation management. Broadleaved woodland and heathland habitats were consistently valued highly for both CES, whereas agricultural land tended to be associated with low values. Results obtained by the different methods were positively correlated in 6 out of 10 comparisons, indicating a degree of consistency between them. The spatial distribution of CES values at the landscape scale was also generally consistent between the three methods. These results highlight the value of comparative analyses of CES for identifying robust results, providing a way forward for their inclusion in land management decision-making. EDITED BY Matthias Schröter

A reply to ‘A meta-database of Holocene sediment cores for England: missing data’ (Tooley 2015)

We welcome the response of Tooley (2015) to our article describing a new meta-database of Holocene sediment cores for England. In our article we describe the online publication of this meta-database, arising from systematic meta-search. We define its scope and the meta-data it contains, before providing the data themselves (in the Electronic Supplementary Material online). We note that Prof. Tooley describes the idea of such a database as important and valuable, and we welcome the constructive approach he adopts throughout his article. Tooley highlights that the meta-database can be enhanced by the inclusion of a number of studies of the Coastal Lowlands, highlighting gaps in the Lancashire and Hartlepool Bay areas in particular. While it is undoubtedly true that these studies were omitted, they tend to document boreholes which have shown Holocene sediments, rather than boreholes subject to the analysis of least one palaeoecological proxy, as per our inclusion criterion. For example, based on the information M.J. Tooley provides, we estimate that 17 such analyses from Lancashire would have satisfied this criterion. It is certainly clear that these omissions are genuine, and we would agree that they add to the pool of sites already described in the meta-database. Because of the constraints of systematic search however, it could also be the case that omissions exist outside these areas, and in the original text we highlighted that: ‘‘the resulting meta-database is by no means exhaustive and we would expect further additions to be made in due course’’. We therefore welcome this addition and would similarly do so for others highlighted to the author team. We would however contest the suggestion that ‘much’ of the published data have been overlooked from improper searching. Tooley implores a greater level of focus at the county level; we would only encourage consideration of the attendant effects of his proposed strategy on search volume (the modern counties of England would generate an 84 fold increase to our list of

Improving estimates of environmental change using multilevel regression models of Ellenberg indicator values

Abstract Ellenberg indicator values (EIVs) are a widely used metric in plant ecology comprising a semi‐quantitative description of species’ ecological requirements. Typically, point estimates of mean EIV scores are compared over space or time to infer differences in the environmental conditions structuring plant communities—particularly in resurvey studies where no historical environmental data are available. However, the use of point estimates as a basis for inference does not take into account variance among species EIVs within sampled plots and gives equal weighting to means calculated from plots with differing numbers of species. Traditional methods are also vulnerable to inaccurate estimates where only incomplete species lists are available.We present a set of multilevel (hierarchical) models—fitted with and without group‐level predictors (e.g., habitat type)—to improve precision and accuracy of plot mean EIV scores and to provide more reliable inference on changing environmental conditions over spatial and temporal gradients in resurvey studies. We compare multilevel model performance to GLMMs fitted to point estimates of mean EIVs. We also test the reliability of this method to improve inferences with incomplete species lists in some or all sample plots. Hierarchical modeling led to more accurate and precise estimates of plot‐level differences in mean EIV scores between time‐periods, particularly for datasets with incomplete records of species occurrence. Furthermore, hierarchical models revealed directional environmental change within ecological habitat types, which less precise estimates from GLMMs of raw mean EIVs were inadequate to detect. The ability to compute separate residual variance and adjusted R 2 parameters for plot mean EIVs and temporal differences in plot mean EIVs in multilevel models also allowed us to uncover a prominent role of hydrological differences as a driver of community compositional change in our case study, which traditional use of EIVs would fail to reveal. Assessing environmental change underlying ecological communities is a vital issue in the face of accelerating anthropogenic change. We have demonstrated that multilevel modeling of EIVs allows for a nuanced estimation of such from plant assemblage data changes at local scales and beyond, leading to a better understanding of temporal dynamics of ecosystems. Further, the ability of these methods to perform well with missing data should increase the total set of historical data which can be used to this end.