Search

International audience; Ambrosia trifida L. (giant ragweed, Asteraceae) is native to the North American continent and was introduced into Europe and Asia at the end of the 19th century. In its native range, this tall annual species is common in riparian and ruderal habitats and is also a major weed in annual cropping systems. For nearly a century, A. trifida has also been of great concern in the U.S. for its highly allergenic pollen, necessitating targeted control measures to reduce its impact on human populations. Based on the distribution of A. trifida in North America and in its introduced range, riparian systems in the rest of the world may be particularly at risk to invasion, with potential negative consequences for their biodiversity. Currently, A. trifida has invaded Asia more widely than Europe, likely due to the more favourable local conditions in Asia. Throughout its introduced range, A. trifida is host to a limited number of invertebrates and pathogens and only a few biological agents are available for its control. The main impacts of A. trifida at a global level are on crop yield and human health, resulting in significant socio-economic impacts. The success of A. trifida invasion in areas in which it has been introduced is still unclear, but climate change may increase climate suitability, increasing the potential for A. trifida to spread. While effective management in cultivated fields seems potentially possible, the development and control of A. trifida in natural riparian habitats is of great concern due to the difficulty of management in these areas.

1. Plant pathogens are introduced to new geographical regions ever more frequently as global connectivity increases. Predicting the threat they pose to plant health can be difficult without in‐depth knowledge of behaviour, distribution and spread. Here, we evaluate the potential for using biological traits and phylogeny to predict global threats from emerging pathogens. 2. We use a species‐level trait database and phylogeny for 179 Phytophthora species: oomycete pathogens impacting natural, agricultural, horticultural and forestry settings. We compile host and distribution reports for Phytophthora species across 178 countries and evaluate the power of traits, phylogeny and time since description (reflecting species‐level knowledge) to explain and predict their international transport, maximum latitude and host breadth using Bayesian phylogenetic generalised linear mixed models. 3. In the best‐performing models, traits, phylogeny and time since description together explained up to 90%, 97% and 87% of variance in number of countries reached, latitudinal limits and host range, respectively. Traits and phylogeny together explained up to 26%, 41% and 34% of variance in the number of countries reached, maximum latitude and host plant families affected, respectively, but time since description had the strongest effect. 4. Root‐attacking species were reported in more countries, and on more host plant families than foliar‐attacking species. Host generalist pathogens had thicker‐walled resting structures (stress‐tolerant oospores) and faster growth rates at their optima. Cold‐tolerant species are reported in more countries and at higher latitudes, though more accurate interspecific empirical data are needed to confirm this finding. 5. Policy implications. We evaluate the potential of an evolutionary trait‐based framework to support horizon‐scanning approaches for identifying pathogens with greater potential for global‐scale impacts. Potential future threats from Phytophthora include Phytophthora x heterohybrida, P. lactucae, P. glovera, P. x incrassata, P. amnicola and P. aquimorbida, which are recently described, possibly under‐reported species, with similar traits and/or phylogenetic proximity to other high‐impact species. Priority traits to measure for emerging species may be thermal minima, oospore wall index and growth rate at optimum temperature. Trait‐based horizon‐scanning approaches would benefit from the development of international and cross‐sectoral collaborations to deliver centralised databases incorporating pathogen distributions, traits and phylogeny.

1. Human‐modified tropical landscapes are often invaded by exotic plant species, but relatively few species are able to colonise remnant areas of rainforest embedded within such landscapes. The functional traits of successful invaders of natural versus anthropogenic habitats are poorly known, especially in tropical regions, and identifying such traits provides insight into the mechanisms that drive invasion. Here, we examine the invasion of tropical rainforest remnants along a disturbance gradient, within a human‐modified agricultural landscape, and determine whether exotic species that invade these forests are selected according to particular traits. 2. We surveyed the occurrence of 18 exotic species along 100‐m transects in four habitats—oil palm road, forest‐oil palm edges and disturbed and intact forest within rainforest remnants—at 21 sites across Sabah, Malaysian Borneo. We collated data on four functional traits relevant to the barriers plants encounter when colonising new environments (e.g. dispersal and persistence) and tested whether trait filtering occurs during invasion of rainforest remnants. 3. Exotic species richness declined significantly from oil palm (mean 9.2 species per transect) to forest edge (7.8 species) to inside rainforest remnants (3.1 species in disturbed forest), and only one species, Clidemia hirta, invaded intact forest. Exotic communities within rainforest remnants had long‐distance (vertebrate) dispersal, were woodier and had taller maximum heights, compared to those found in oil palm. For each trait, the community‐weighted mean for the forest edge community was intermediate between oil palm and disturbed forest, suggesting trait filtering during the invasion of rainforest remnants. 4. Our study provides strong evidence that trait filtering occurs during invasion from human‐modified agricultural habitats into previously disturbed forests via the forest edge. Successful invasion of rainforest remnants requires relatively long‐distance dispersal, in particular by vertebrates, as well as traits that are more similar to those of native forest species (i.e. tall and woody), making these exotic species more able to compete and persist in that environment. Our results show that disturbed tropical rainforests with open canopies are susceptible to invasion and highlight the traits of exotic species which can invade rainforest habitats, and which may pose a threat to regenerating tropical rainforests.

Context Intact tropical rainforests are considered robust to plant invasions. However, land-use change alters the structure and species composition of native forest, opening up tropical landscapes to invasion. Yet, the relative roles of key drivers on tropical forest invasions remain little investigated. Objectives We examine factors affecting plant invasion of rainforest remnants in oil-palm dominated landscapes in Sabah, Malaysian Borneo. We hypothesized that invasion is greater in highly fragmented landscapes, and in disturbed forests with lower native plant diversity (cf. old-growth rainforests). Methods Native and exotic plants were surveyed in 47 plots at 17 forest sites, spanning gradients in landscape-scale fragmentation and local forest disturbance. Using partial least squares path-modelling, we examined correlations between invasion, fragmentation, forest disturbance, propagule pressure, soil characteristics and native plant community. Results We recorded 6999 individuals from 329 genera in total, including eight exotic species (0–51% of individuals/plot, median = 1.4%) representing shrubs, forbs, graminoids and climbers. The best model (R2 = 0.343) revealed that invasion was correlated with disturbance and propagule pressure (high prevalence of exotic species in plantation matrix), the latter being driven by greater fragmentation of the landscape. Our models revealed a significant negative correlation between invasion and native tree seedlings and sapling community diversity. Conclusions Increasing landscape fragmentation promotes exotic plant invasion in remnant tropical forests, especially if local disturbance is high. The association between exotic species invasion and young native tree community may have impacts for regeneration given that fragmentation is predicted to increase and so plant invasion may become more prevalent.

Interactions between stressors in freshwater ecosystems, including those associated with climate change and nutrient enrichment, are currently difficult to detect and manage. Our understanding of the forms and frequency of occurrence of such interactions is limited; assessments using field data have been constrained as a result of varying data forms and quality. To address this issue, we demonstrate a statistical approach capable of assessing multiple stressor interactions using contrasting data forms in 3 European catchments (Loch Leven Catchment, UK: assessment of phytoplankton response in a single lake with time series data; Pinios Catchment, Greece: macroinvertebrate response across multiple rivers using spatial data; and Lepsämänjoki Catchment, Finland: phytoplankton response across multiple rivers using spatiotemporal data). Statistical models were developed to predict the relative and interactive effects of climate change and nutrient enrichment sensitive indicators (stressors) on indicators of ecological quality (ecological responses) within the framework of linear mixed effects models. In all catchments, indicators of nutrient enrichment were identified as the primary stressor, with climate change-sensitive indicators causing secondary effects (Loch Leven: additive, total phosphorus [TP] × precipitation; Pinios: additive, nitrate × dissolved oxygen; Lepsämänjoki: synergistic, TP × summer water temperature), the intensity of which varied between catchments and along the nutrient stressor gradient. Simple stressor change scenarios were constructed for each catchment and used in combination with mechanistic models to explore potential management responses. This approach can be used to explore the need for multiple stressor management in freshwaters, helping practitioners navigate a complex world of environmental change.

Humans fundamentally affect dispersal, directly by transporting individuals and indirectly by altering landscapes and natural vectors. This human-mediated dispersal (HMD) modifies long-distance dispersal, changes dispersal paths, and overall benefits certain species or genotypes while disadvantaging others. HMD is leading to radical changes in the structure and functioning of spatial networks, which are likely to intensify as human activities increase in scope and extent. Here, we provide an overview to guide research into HMD and the resulting rewiring of spatial networks, making predictions about the ecological and evolutionary consequences and how these vary according to spatial scale and the traits of species. Future research should consider HMD holistically, assessing the range of direct and indirect processes to understand the complex impacts on eco-evolutionary dynamics.

Selective logging has affected large areas of tropical forests and there is increasing interest in how to manage selectively logged forests to enhance recovery. However, the impacts of logging and active restoration, by liberation cutting and enrichment planting, on tree community composition are poorly understood compared to trajectories of biomass recovery. Here, we assess the long-term impacts of selective logging and active restoration for biomass recovery on tree species diversity, community composition, and forest structure. We censused all stems ≥2 cm diameter at breast height (DBH) on 46 permanent plots in unlogged, primary forest in the Danum Valley Conservation Area (DVCA; 12 plots, totalling 0.6 ha) and in sites logged 23–35 years prior to the census in the Ulu Segama Forest Reserve adjacent to DVCA (34 plots, totalling 1.7 ha) in Sabah, Malaysian Borneo. Active restoration treatments, including enrichment planting and climber cutting, were implemented on 17 of the logged forest plots 12–24 years prior to the census. Total plot-level basal area and pole (5–10 cm DBH) stem density were lower in logged than unlogged forests, however no difference was found in stem density amongst saplings (2–5 cm DBH) or established trees (≥10 cm DBH). Neither basal area, nor plot-level stem density varied with time since logging at any size class, although sapling and pole stem densities were lower in actively restored than naturally regenerating logged forest. Sapling species diversity was lower in logged than unlogged forest, however there were no other significant effects of logging on tree species richness or diversity indices. Tree species composition, however, differed between logged and unlogged forests across all stem size classes (PERMANOVA), reflected by 23 significant indicator species that were only present in unlogged forest. PERMANOVA tests revealed no evidence that overall species composition changed with time since logging or with active restoration treatments at any size class. However, when naturally regenerating and actively restored communities were compared, two indicator species were identified in naturally regenerating forest and three in actively restored forests. Together our results suggest that selective logging has a lasting effect on tree community composition regardless of active restoration treatments and, even when species richness and diversity are stable, species composition remains distinct from unlogged forest for more than two decades post-harvest. Active restoration efforts should be targeted, monitored, and refined to try to ensure positive outcomes for multiple metrics of forest recovery. © 2021 Elsevier, Forest Ecology and Management, 488, ISSN:0378-1127, ISSN:1872-7042

International audience; Ecosystems face multiple, potentially interacting, anthropogenic pressures that can modify biodiversity and ecosystem functioning. Using a bryophyte–microarthropod microecosystem we tested the combined effects of habitat loss, episodic heat-shocks and an introduced non-native apex predator on ecosystem function (chlorophyll fluorescence as an indicatorof photosystem II function) and microarthropod communities (abundance and body size). The photosynthetic function was degraded by the sequence of heat-shock episodes, but unaffected by microecosystem patch size or top-down pressure from the introduced predator. In small microecosystem patches without the non-native predator, Acari abundance decreased with heat-shock frequency, while Collembola abundance increased. These trends disappeared in larger microecosystem patches or when predators were introduced, although Acari abundance was lower in large patchesthat underwent heat-shocks and were exposed to the predator. Mean assemblage body length (Collembola) was reduced independently in small microecosystem patches and with greater heat-shock frequency. Our experimental simulation of episodic heatwaves, habitat loss and nonnative predation pressure in microecosystems produced evidence of individual and potentially synergistic and antagonistic effects on ecosystem function and microarthropod communities. Such complex outcomes of interactions between multiple stressors need to be considered when assessing anthropogenic risks for biota and ecosystem functioning.

Embargo until February 26, 2023 Despite advances in conceptual understanding, single-stressor abatement approaches remain common in the management of fresh waters, even though they can produce unexpected ecological responses when multiple stressors interact. Here we identify limitations restricting the development of multiple-stressor management strategies and address these, bridging theory and practice, within a novel empirical framework. Those critical limitations include that (i) monitoring schemes fall short of accounting for theory on relationships between multiple-stressor interactions and ecological responses, (ii) current empirical modelling approaches neglect the prevalence and intensity of multiple-stressor interactions, and (iii) mechanisms of stressor interactions are often poorly understood. We offer practical recommendations for the use of empirical models and experiments to predict the effects of freshwater degradation in response to changes in multiple stressors, demonstrating this approach in a case study. Drawing on our framework, we offer practical recommendations to support the development of effective management strategies in three general multiple-stressor scenarios.

In order to prevent and control the emergence of biosecurity threats such as vector-borne diseases of plants, it is vital to understand drivers of entry, establishment, and spatiotemporal spread, as well as the form, timing, and effectiveness of disease management strategies. An inherent challenge for policy in combatting emerging disease is the uncertainty associated with intervention planning in areas not yet affected, based on models and data from current outbreaks. Following the recent high-profile emergence of the bacterium Xylella fastidiosa in a number of European countries, we review the most pertinent epidemiological uncertainties concerning the dynamics of this bacterium in novel environments. To reduce the considerable ecological and socio-economic impacts of these outbreaks, eco-epidemiological research in a broader range of environmental conditions needs to be conducted and used to inform policy to enhance disease risk assessment, and support successful policy-making decisions. By characterizing infection pathways, we can highlight the uncertainties that surround our knowledge of this disease, drawing attention to how these are amplified when trying to predict and manage outbreaks in currently unaffected locations. To help guide future research and decision-making processes, we invited experts in different fields of plant pathology to identify data to prioritize when developing pest risk assessments. Our analysis revealed that epidemiological uncertainty is mainly driven by the large variety of hosts, vectors, and bacterial strains, leading to a range of different epidemiological characteristics further magnified by novel environmental conditions. These results offer new insights on how eco-epidemiological analyses can enhance understanding of plant disease spread and support management recommendations. [Formula: see text] Copyright © 2020 The Author(s). This is an open access article distributed under the CC BY 4.0 International license .

In order to prevent and control the emergence of biosecurity threats such as vector-borne diseases of plants, it is vital to understand drivers of entry, establishment, and spatiotemporal spread, as well as the form, timing, and effectiveness of disease management strategies. An inherent challenge for policy in combatting emerging disease is the uncertainty associated with intervention planning in areas not yet affected, based on models and data from current outbreaks. Following the recent high-profile emergence of the bacterium

Maintaining forest conservation set-asides is a key criterion of sustainability certification of many crops that drive tropical deforestation, but their value for carbon storage and associated biodiversity is unclear. We conducted vegetation measurements to examine the benefits of set-asides for aboveground carbon stocks (AGC) in certified oil palm plantations on Borneo, and whether their AGC is positively associated with plant diversity. The mean estimated AGC of live trees and palms ≥10 cm diameter in set-asides in certified oil palm plantations (52.8 Mg ha−1) was >1.5-times that of oil palm (30.3 Mg ha−1), with some plots supporting similar AGC to primary forest. For lowland Borneo, we estimate that the average AGC of oil palm plantations with 10% coverage of set-asides is up to 20% greater than the average AGC of oil palm plantations without set-asides, newly demonstrating carbon storage as a benefit of conservation set-asides. We found positive relationships between AGC and plant diversity, highlighting the carbon–biodiversity co-benefits of set-asides. However, set-asides had a lower density of tree seedlings than continuous primary forest, indicating potential suppression of future tree regeneration and AGC. Our findings support the application of zero-deforestation during agricultural development, to conserve areas of remaining forest with high AGC and high biodiversity. We recommend management practices that boost regeneration in existing set-asides (e.g. enrichment planting), which would be most effective in larger set-asides, and could substantially increase the AGC of agricultural landscapes without removing land from production, and help conserve forest-dependent biodiversity.

Aim: Hotspots of human activity are focal points for ecosystem disturbance and non‐native introduction, from which invading populations disperse and spread. As such, connectivity to locations used by humans may influence the likelihood of invasion. Moreover, connectivity in freshwater ecosystems may follow the hydrological network. Here we tested whether multiple forms of connectivity to human recreational activities promotes biological invasion of freshwater ecosystems. Location: England, UK. Time period: 1990–2018. Major taxa studied: One hundred and twenty‐six non‐native freshwater birds, crustaceans, fish, molluscs and plants. Methods: Machine learning was used to predict spatial gradients in human recreation and two high risk activities for invasion (fishing and water sports). Connectivity indices were developed for each activity, in which human influence decayed from activity hotspots according to Euclidean distance (spatial connectivity) or hydrological network distance (downstream, upstream and along‐channel connectivity). Generalized linear mixed models identified the connectivity type most associated to invasive species richness of each group, while controlling for other anthropogenic and environmental drivers. Results: Connectivity to humans generally had stronger positive effects on invasion than all other drivers except recording effort. Recreation had stronger influence than urban land cover, and for most groups high risk activities had stronger effects than general recreation. Downstream human connectivity was most important for invasion by most of the groups, potentially reflecting predominantly hydrological dispersal. An exception was birds, for which spatial connectivity was most important, possibly because of overland dispersal capacity. Main conclusions: These findings support the hypothesis that freshwater invasion is partly determined by an interaction between human activity and species dispersal in the hydrological network. By comparing alternative connectivity types for different human activities, our approach could enable robust inference of specific pathways and spread mechanisms associated with particular taxa. This would provide evidence to support better prioritization of surveillance and management for invasive non‐native species.

The present study aimed to investigate the role of propagule pressure (P), abiotic (A), and biotic (B) factors (collectively indicated as PAB) on the suitability of the Mediterranean island of Sardinia (Italy) to be invaded by the tree Acacia saligna, recently included in the list of invasive alien species of European Union concern. To this aim, a binomial Generalized Linear Model was applied for disentangling the relationship between 432 A. saligna occurrence records and 10 thematic layers, at high-resolution (10 x10 m), used as proxies for the 3 categories of PAB variables. The 432 occurrence records of A. saligna were periodically monitored (period 2000–2018) to check the persistence of the populations and their invasive status. The predictive power of the model was evaluated by computing the mean of the AUC scores, through cross-fold validation. The model adequately described how the PAB factors influence the presence of A. saligna which is mainly shaped by abiotic factors such as topography, and biotic factors such as the presence of woody dune vegetation, and to a lesser extent by other predictors. The projection of the model to the whole island clearly shows that suitability varies at the landscape level due to the variation of the PAB across the territory. The probability of A. saligna occurrence near the coast is higher in sand dunes. In the internal areas of the island it occurs close to the roads and urban areas. This study and the tested methodology could represent a suitable tool to prioritize areas for the monitoring of A. saligna to meet the requirements of the Regulation (EU) No. 1143/2014 on Invasive Alien Species (the IAS Regulation).

Aim: Although global trade is implicated in biological invasions, the assumption that trade networks explain the large-scale distributions of non-native species remains largely untested. We addressed this by analysing relationships between global trade networks and plant pest invasion. Location: Forty-eight countries in Europe and the Mediterranean. Time period: Current. Major taxa studied: Four hundred and twenty-two non-native plant pests (173 invertebrates, 166 pathogens, 83 plants). Methods: Ten types of connectivity index were developed, representing potential roles of trade networks, air transport links, geographical proximity, climatic similarity and source country wealth in facilitating invasion. Generalized linear mixed models (GLMMs) identified the connectivity index that best explained both historical and recent invasion. Then, more complex GLMMs were developed including connectivity through trade networks for multiple commodities relevant for pests (live plants, forest products, fruit and vegetables and seeds) and species’ transport associations with those commodities. Results: Total import volumes, species’ global prevalence and connectivity measures based on air transport, geographical distance or climate did not explain invasion as well as connectivity through global trade networks. Invasion was strongly promoted by agricultural imports from countries in which the focal species was present and that were climatically similar to the importing country. However, live plant imports from nearby countries provided a better explanation of the most recent invasions. Connectivity through multiple trade networks predicted invasion better than total agricultural trade, and there was support for our hypothesis that species known to be transported with a particular network had greater sensitivity to its connectivity. Main conclusions: Our findings show that patterns of invasion are governed to a large extent by global trade networks connecting source areas for non-native species and the dispersal of those species through multiple trade networks. This enhances potential for developing a predictive framework to improve risk assessment, biosecurity and surveillance for invasions.

Xylella fastidiosa is an important plant pathogen that attacks several plants of economic importance. Once restricted to the Americas, the bacterium, which causes olive quick decline syndrome, was discovered near Lecce, Italy in 2013. Since the initial outbreak, it has invaded 23,000 ha of olives in the Apulian Region, southern Italy, and is of great concern throughout Mediterranean basin. Therefore, predicting its spread and estimating the efficacy of control are of utmost importance. As data on this invasive infectious disease are poor, we have developed a spatially-explicit simulation model for X. fastidiosa to provide guidance for predicting spread in the early stages of invasion and inform management strategies. The model qualitatively and quantitatively predicts the patterns of spread. We model control zones currently employed in Apulia, showing that increasing buffer widths decrease infection risk beyond the control zone, but this may not halt the spread completely due to stochastic long-distance jumps caused by vector dispersal. Therefore, management practices should aim to reduce vector long-distance dispersal. We find optimal control scenarios that minimise control effort while reducing X. fastidiosa spread maximally—suggesting that increasing buffer zone widths should be favoured over surveillance efforts as control budgets increase. Our model highlights the importance of non-olive hosts which increase the spread rate of the disease and may lead to an order of magnitude increase in risk. Many aspects of X. fastidiosa disease invasion remain uncertain and hinder forecasting; we recommend future studies investigating quantification of the infection growth rate, and short and long distance dispersal. Electronic supplementary material The online version of this article (doi:10.1007/s10530-017-1393-5) contains supplementary material, which is available to authorized users.

Climate and land-use change drive a suite of stressors that shape ecosystems and interact to yield complex ecological responses (that is, additive, antagonistic and synergistic effects). We know little about the spatial scales relevant for the outcomes of such interactions and little about effect sizes. These knowledge gaps need to be filled to underpin future land management decisions or climate mitigation interventions for protecting and restoring freshwater ecosystems. This study combines data across scales from 33 mesocosm experiments with those from 14 river basins and 22 cross-basin studies in Europe, producing 174 combinations of paired-stressor effects on a biological response variable. Generalized linear models showed that only one of the two stressors had a significant effect in 39% of the analysed cases, 28% of the paired-stressor combinations resulted in additive effects and 33% resulted in interactive (antagonistic, synergistic, opposing or reversal) effects. For lakes, the frequencies of additive and interactive effects were similar for all spatial scales addressed, while for rivers these frequencies increased with scale. Nutrient enrichment was the overriding stressor for lakes, with effects generally exceeding those of secondary stressors. For rivers, the effects of nutrient enrichment were dependent on the specific stressor combination and biological response vari- able. These results vindicate the traditional focus of lake restoration and management on nutrient stress, while highlighting that river management requires more bespoke management solutions. This work was supported by the MARS project (Managing Aquatic Ecosystems and Water Resources under Multiple Stress) funded under the 7th EU Framework Programme, Theme 6 (Environment including Climate Change), contract no. 603378 (http://www.mars-project.eu). Further support was received through the ILES (SAW- 2015-IGB-1) and BIBS (BMBF 01LC1501G) projects. Partner organizations provided 25% cofunding through their institutional budgets. We thank J. Strackbein, J. Lorenz and L. Mack for their support. This work was supported by the MARS project (Managing Aquatic Ecosystems and Water Resources under Multiple Stress) funded under the 7th EU Framework Programme, Theme 6 (Environment including Climate Change), contract no. 603378 (http://www.mars-project.eu). Further support was received through the ILES (SAW- 2015-IGB-1) and BIBS (BMBF 01LC1501G) projects. Partner organizations provided 25% cofunding through their institutional budgets. We thank J. Strackbein, J. Lorenz and L. Mack for their support.

Aim: We present a novel strategy for species distribution models (SDMs) aimed at predicting the potential distributions of range‐expanding invasive non‐native species (INNS). The strategy combines two established perspectives on defining the background region for sampling “pseudo‐absences” that have hitherto only been applied separately. These are the accessible area, which accounts for dispersal constraints, and the area outside the environmental range of the species and therefore assumed to be unsuitable for the species. We tested an approach to combine these by fitting SDMs using background samples (pseudo‐absences) from both types of background. Location: Global. Taxon: Invasive non‐native plants: Humulus scandens, Lygodium japonicum, Lespedeza cuneata, Triadica sebifera, Cinnamomum camphora. Methods: Presence‐background (or presence‐only) SDMs were developed for the potential global distributions of five plant species native to Asia, invasive elsewhere and prioritised for risk assessment as emerging INNS in Europe. We compared models where the pseudo‐absences were selected from the accessible background, the unsuitable background (defined using biological knowledge of the species’ key limiting factors) or from both types of background. Results: Combining the unsuitable and accessible backgrounds expanded the range of environments available for model fitting and caused biological knowledge about ecological unsuitability to influence the fitted species‐environment relationships. This improved the realism and accuracy of distribution projections globally and, generally, within the species’ ranges. Main conclusions: Correlative SDMs remain valuable for INNS risk mapping and management, but are often criticised for a lack of biological underpinning. Our approach partly addresses this concern by using prior knowledge of species’ requirements or tolerances to define the unsuitable background for modelling, while also accommodating dispersal constraints through considerations of accessibility. It can be implemented with current SDM software and results in more accurate and realistic distribution projections. As such, wider adoption has potential to improve SDMs that support INNS risk assessment.

It is well-recognised that to achieve long-term sustainable and resilient land management we need to understand the coupled dynamics of social and ecological systems. Land use change scenarios will often aim to understand (i) the behaviours of land management, influenced by direct and indirect drivers, (ii) the resulting changes in land use and (iii) the environmental implications of these changes. While the literature in this field is extensive, approaches to parameterise coupled systems through integration of empirical social science based models and ecology based models still need further development. We propose an approach to land use dynamics modelling based on the integration of behavioural models derived from choice experiments and spatially explicit systems dynamics modelling. This involves the specification of a choice model to parameterise land use behaviour and the integration with a spatial habitat succession model. We test this approach in an upland socio-ecological system in the United Kingdom. We conduct a choice experiment with land managers in the Peak District National Park. The elicited preferences form the basis for a behavioural model, which is integrated with a habitat succession model to predict the landscape level vegetation impacts. The integrated model allows us to create projections of how land use may change in the future under different environmental and policy scenarios, and the impact this may have on landscape vegetation patterns. We illustrate this by showing future projection of landscape changes related to hypothetical changes to EU level agricultural management incentives. The advantages of this approach are (i) the approach takes into account potential environmental and management feedbacks, an aspect often ignored in choice modelling, (ii) the behavioural rules are revealed from actual and hypothetical choice data, which allow the research to test the empirical evidence for various determinants of choice, (iii) the behavioural choice models generate probabilities of alternative behaviours which make them ideally suited for integration with simulation models. The paper concludes that the modelling approach offers a promising route for linking socio-economic and ecological features of socio-ecological systems. Furthermore, our proposed approach allows testing of the underlying socio-economic and environmental drivers and their interaction in real environmental systems.

When faced with a large species pool of invasive or potentially invasive alien plants, prioritization is an essential prerequisite for focusing limited resources on species which inflict high impacts, have a high rate of spread and can be cost-effectively managed. The prioritization process as detailed within this paper is the first tool to assess species for priority for risk assessment (RA) in the European Union (EU) specifically designed to incorporate the requirements of EU Regulation no. 1143/2014. The prioritization process can be used for any plant species alien to the EU, whether currently present within the territory or absent. The purpose of the prioritization is to act as a preliminarily evaluation to determine which species have the highest priority for RA at the EU level and may eventually be proposed for inclusion in the list of invasive alien species of EU concern. The preliminary risk assessment stage (Stage 1), prioritizes species into one of four lists (EU List of Invasive Alien Plants, EU Observation List of Invasive Alien Plants, EU List of Minor Concern and the Residual List) based on their potential for spread coupled with impacts. The impacts on native species and ecosystem functions and related ecosystem services are emphasized in line with Article 4.3(c) of the Regulation. Only those species included in the EU List of Invasive Alien Plants proceed to Stage 2 where potential for further spread and establishment coupled with evaluating preventative and management actions is evaluated. The output of Stage 2 is to prioritize those species which have the highest priority for a RA at the EU level or should be considered under national measures which may involve a trade ban, cessation of cultivation, monitoring, control, containment or eradication. When considering alien plant species for the whole of the EPPO region, or for species under the Plant Health Regulation, the original EPPO prioritization process for invasive alien plants remains the optimum tool.

Indicators of large-scale ecological change are typically derived from long-term monitoring networks. As such, it is important to assess how well monitoring networks provide evidence for ecological trends in the regions they are monitoring. In part, this depends on the network’s representativeness of the full range of environmental conditions occurring in the monitored region. In addition, the statistical power to detect trends and ecological changes using the network depends on its structure, size and the intensity and accuracy of monitoring. This paper addresses the optimisation of representativeness and statistical power when re-designing existing large-scale ecological monitoring networks, for example due to financial constraints on monitoring programmes. It uses a real world example of a well-established river monitoring network of 254 sites distributed across Scotland. We first present a novel approach for assessing a monitoring network’s representativeness of national habitat and pressure gradients using the multivariate two-sample Cramer’s T statistic. This compares multivariate gradient distributions among sites inside and outside of the network. Using this test, the existing network was found to over-represent larger and more heavily polluted sites, reflecting earlier research priorities when it was originally designed. Network re-design was addressed through stepwise selection of individual sites to remove from or add to the network to maximise multivariate representativeness. This showed that combinations of selective site retention and addition can be used to modify existing monitoring networks, changing the number of sites and improving representativeness. We then investigated the effect of network re-design on the statistical power to detect long-term trends across the whole network. The power analysis was based on linear mixed effects models for long-term trends in three ecological indicators (ecological quality ratios for diatoms, invertebrates and macrophytes) over a ten-year period. This revealed a clear loss of power in smaller networks with less accurate sampling, but sampling schedule had a smaller effect on power. Interestingly, more representative networks had slightly lower trend detection power than the current unrepresentative network, though they should give a less biased estimate of national trends. Our analyses of representativeness and statistical power provide a general framework for designing and adapting large-scale ecological monitoring networks. Wider use of such methods would improve the quality of indicators derived from them and improve the evidence base for detecting and managing ecological change.

Phenology is a key indicator and mediator of the ecological impacts of climate change. However, studies monitoring the phenology of individual species are moderate in number, taxonomically and geographically restricted, and mainly focused on spring events. As such, attention is being given to non-standard sources of phenology data, such as the dates of species’ biological records. Here, we present a conceptual framework for deriving phenological metrics from biological recording data, while accounting for seasonal variation in the level of activity by recorders. We develop a new Bayesian statistical model to infer the seasonal pattern of plant ‘recordability’. The modelled dates of maximum recordability are strongly indicative of the flowering peaks of 29 insect-pollinated species monitored in two botanic gardens in Great Britain. Conversely, not accounting for the seasonality in recording activity results in biased estimates of the observed flowering peaks. However, observed first and last flowering dates were less reliably explained by the model, which probably reflects greater interspecific variation in levels of recording before and after flowering. We conclude that our method provides new potential for gaining useful insights into large-scale variation in peak phenology across a much broader range of plant species than have previously been studied.

The Global Biodiversity Information Facility (GBIF) is at the moment one of the largest and most widely used biodiversity databases. Nevertheless, there are still some limitations, e.g. in terms of plant species status (native vs. non-native) and geographic resolution of records. At the same time, it is well known that alien plant invasions in inland freshwaters can alter community structure, ecosystem functions and services with significant negative impacts on biodiversity and human activities. We assessed if the GBIF database has a geospatial homogeneous information for native and non-native aquatic plant species for South America and whether or not literature resources not yet digitalized (floras, checklists and other papers) could provide additional information. We selected a set of 40 native and 40 non-native aquatic species. These 80 species included a sub-set of 40 alien species previously evaluated with the USAqWRA scheme (US Aquatic Weed Risk Assessment). Species with non-reliable identification, duplicates of the same collection, records poorly georeferenced were removed from the dataset. New records were manually compiled through classical literature research. All the georeferenced records (GBIF + literature) were used for the mapping and the comparative analysis. As a result, we can conclude that the two datasets provide quite significantly different information and the combination of the two offers new information that would not exist in a single data source. Nevertheless, a careful quality evaluation of the primary information, both in the case of literature and GBIF should be conducted, before the data is used for further analyses.

1.Riparian invertebrate communities occupy a dynamic ecotone where hydrogeomorphological (e.g. river flows) and ecological (e.g. succession) processes may govern assemblage structure by filtering species according to their traits (e.g. dispersal capacity, niche).2.We surveyed terrestrial invertebrate assemblages (millipedes, carabid beetles, spiders) in 28 river islands across four river catchments over 2 years. We predicted that distinct ecological niches would produce taxon-specific responses of abundance and species richness to: (i) disturbance from episodic floods, (ii) island area, (iii) island vegetation structure, and (iv) landscape structure. We also predicted that responses would differ according to species’ dispersal ability (aerial vs. terrestrial only), indicating migration was sustaining community structure.3.Invertebrate abundance and richness was affected by different combinations of vegetation structure, island area, and flood disturbance according to species’ dispersal capacity. Carabid abundance related negatively to episodic floods, particularly for flightless species, but the other taxa were insensitive to this disturbance. Larger islands supported greater abundance of carabids and all invertebrates able to disperse aerially. Vegetation structure, particularly tree canopy density and plant richness, related positively to invertebrate abundance across all taxa and aerial dispersers, whereas terrestrial disperser richness related positively to tree cover. Landscape structure did not influence richness or abundance.4.Multiple ecological processes govern riparian invertebrate assemblages. Overall insensitivity to flood disturbance and responses contingent on dispersal mode imply that spatial dynamics subsidise the communities through immigration. Particular habitat features (e.g. trees, speciose vegetation) may provide refuges from disturbance and concentration of niches and food resources.

Thirty-seven alien plant species, pre-identified by horizon scanning exercises were prioritised for pest risk analysis (PRA) using a modified version of the EPPO Prioritisation Process designed to be compliant with the EU Regulation 1143/2014. In Stage 1, species were categorised into one of four lists – a Residual List, EU List of Minor Concern, EU Observation List and the EU List of Invasive Alien Plants. Only those species included in the latter proceeded to the risk management stage where their priority for PRA was assessed. Due to medium or high spread potential coupled with high impacts twenty-two species were included in the EU List of Invasive Alien Plants and proceeded to Stage 2. Four species (Ambrosia trifida, Egeria densa, Fallopia baldschuanica and Oxalis pes-caprae) were assigned to the EU Observation List due to moderate or low impacts. Albizia lebbeck, Clematis terniflora, Euonymus japonicus, Lonicera morrowii, Prunus campanulata and Rubus rosifolius were assigned to the residual list due to a current lack of information on impacts. Similarly, Cornus sericea and Hydrilla verticillata were assigned to the Residual List due to unclear taxonomy and uncertainty in native status, respectively. Chromolaena odorata, Cryptostegia grandiflora and Sphagneticola trilobata were assigned to the Residual List as it is unlikely they will establish in the Union under current climatic conditions. In the risk management stage, Euonymus fortunei, Ligustrum sinense and Lonicera maackii were considered a low priority for PRA as they do not exhibit invasive tendencies despite being widely cultivated in the EU over several decades. Nineteen species were identified as having a high priority for a PRA (Acacia dealbata, Ambrosia confertiflora, Andropogon virginicus, Cardiospermum grandiflorum, Celastrus orbiculatus, Cinnamomum camphora, Cortaderia jubata, Ehrharta calycina, Gymnocoronis spilanthoides, Hakea sericea, Humulus scandens, Hygrophila polysperma, Lespedeza cuneata, Lygodium japonicum, Pennisetum setaceum, Prosopis juliflora, Sapium sebiferum, Pistia stratiotes and Salvinia molesta).

1. Pollinator network structure arising from the extent and strength of interspecific mutualistic interactions can promote species persistence and community robustness. However, environmental change may re-organise network structure limiting capacity to absorb or resist shocks and increasing species extinctions. 2. We investigated if habitat disturbance and the level of mutualism dependence between species affected the robustness of insect–flower visitation networks Following a recently developed Stochastic Co-extinction Model (SCM), we ran simulations to produce the number of extinction episodes (cascade degree), which we correlated with network structure in undisturbed and disturbed habitat. We also explicitly modelled whether a species’ intrinsic dependence on mutualism affected the propensity for extinction cascades in the network. 3. Habitat disturbance generated a gradient in network structure with those from disturbed sites being less connected, but more speciose and so larger. Controlling for network size (z-score standardisation against the null model) revealed that disturbed networks had disproportionately low linkage density, high specialisation, fewer insect visitors per plant species (vulnerability) and lower nestedness (NODF). 4. This network structure gradient driven by disturbance increased and decreased different aspects of robustness to simulated plant extinction. Disturbance decreased the risk that an initial insect extinction would follow a plant species loss. Although, this effect disappeared when network size and connectance were standardised, suggesting the lower connectance of disturbed networks increased robustness to an initial secondary extinction. 5. However, if a secondary extinction occurred then networks from disturbed habitat were more prone to large co-extinction cascades, likely resulting from a greater chance of extinction in these larger, speciose networks. Conversely, when species mutualism dependency was explicit in the SCM simulations the disturbed networks were disproportionately more robust to very large co-extinction cascades, potentially caused by non-random patterns of interaction between species differing in dependence on mutualism. 6. Our results showed disturbance altered the size and the distribution of interspecific interactions in the networks to affect their robustness to co-extinction cascades. Controlling for effects due to network size and the interspecific variation in demographic dependence on mutualism can improve insight into properties conferring the structural robustness of networks to environmental changes.

Niche shifts of nonnative plants can occur when they colonize novel climatic conditions. However, the mechanistic basis for niche shifts during invasion is poorly understood and has rarely been captured within species distribution models. We quantified the consequence of between-population variation in phenology for invasion of common ragweed (Ambrosia artemisiifolia L.) across Europe. Ragweed is of serious concern because of its harmful effects as a crop weed and because of its impact on public health as a major aeroallergen. We developed a forward mechanistic species distribution model based on responses of ragweed development rates to temperature and photoperiod. The model was parameterized and validated from the literature and by reanalyzing data from a reciprocal common garden experiment in which native and invasive populations were grown within and beyond the current invaded range. It could therefore accommodate between-population variation in the physiological requirements for flowering, and predict the potentially invaded ranges of individual populations. Northern- origin populations that were established outside the generally accepted climate envelope of the species had lower thermal requirements for bud development, suggesting local adaptation of phenology had occurred during the invasion. The model predicts that this will extend the potentially invaded range northward and increase the average suitability across Europe by 90% in the current climate and 20% in the future climate. Therefore, trait variation observed at the population scale can trigger a climatic niche shift at the biogeographic scale. For ragweed, earlier flowering phenology in established northern populations could allow the species to spread beyond its current invasive range, substantially increasing its risk to agriculture and public health. Mechanistic species distribution models offer the possibility to represent niche shifts by varying the traits and niche responses of individual populations. Ignoring such effects could substantially underestimate the extent and impact of invasions.

Xylella fastidiosa is an important plant pathogen that attacks several plants of economic importance. Once restricted to the Americas, the bacterium, which causes olive quick decline syndrome, was discovered near Lecce, Italy in 2013. Since the initial outbreak, it has invaded 23,000 ha of olives in the Apulian Region, southern Italy, and is of great concern throughout Mediterranean basin. Therefore, predicting its spread and estimating the efficacy of control are of utmost importance. As data on this invasive infectious disease are poor, we have developed a spatially-explicit simulation model for X. fastidiosa to provide guidance for predicting spread in the early stages of invasion and inform management strategies. The model qualitatively and quantitatively predicts the patterns of spread. We model control zones currently employed in Apulia, showing that increasing buffer widths decrease infection risk beyond the control zone, but this may not halt the spread completely due to stochastic long-distance jumps caused by vector dispersal. Therefore, management practices should aim to reduce vector long-distance dispersal. We find optimal control scenarios that minimise control effort while reducing X. fastidiosa spread maximally—suggesting that increasing buffer zone widths should be favoured over surveillance efforts as control budgets increase. Our model highlights the importance of non-olive hosts which increase the spread rate of the disease and may lead to an order of magnitude increase in risk. Many aspects of X. fastidiosa disease invasion remain uncertain and hinder forecasting; we recommend future studies investigating quantification of the infection growth rate, and short and long distance dispersal.

The drivers of spatial variation in ragweed pollen concentrations, contributing to severe allergic rhinitis and asthma, are poorly quantified. We analysed the spatiotemporal variability in 16-year (1995–2010) annual total (66 stations) and annual total (2010) (162 stations) ragweed pollen counts and 8 independent variables (start, end and duration of the ragweed pollen season, maximum daily and calendar day of the maximum daily ragweed pollen counts, last frost day in spring, first frost day in fall and duration of the frost-free period) for Europe (16 years, 1995–2010) as a function of geographical coordinates. Then annual total pollen counts, annual daily peak pollen counts and date of this peak were regressed against frost-related variables, daily mean temperatures and daily precipitation amounts. To achieve this, we assembled the largest ragweed pollen data set to date for Europe. The dependence of the annual total ragweed pollen counts and the eight independent variables against geographical coordinates clearly distinguishes the three highly infected areas: the Pannonian Plain, Western Lombardy and the Rhone-Alpes region. All the eight variables are sensitive to longitude through its temperature dependence. They are also sensitive to altitude, due to the progressively colder climate with increasing altitude. Both annual total pollen counts and the maximum daily pollen counts depend on the start and the duration of the ragweed pollen season. However, no significant changes were detected in either the eight independent variables as a function of increasing latitude. This is probably due to a mixed climate induced by strong geomorphological inhomogeneities in Europe.

Accurate models for species' distributions are needed to forecast the progress and impacts of alien invasive species and assess potential range-shifting driven by global change. Although this has traditionally been achieved through data-driven correlative modelling, robustly extrapolating these models into novel climatic conditions is challenging. Recently, a small number of process-based or mechanistic distribution models have been developed to complement the correlative approaches. However, tests of these models are lacking, and there are very few process-based models for invasive species. We develop a method for estimating the range of a globally invasive species, common ragweed (Ambrosia artemisiifolia L.), from a temperature- and photoperiod-driven phenology model. The model predicts the region in which ragweed can reach reproductive maturity before frost kills the adult plants in autumn. This aligns well with the poleward and high-elevation range limits in its native North America and in invaded Europe, clearly showing that phenological constraints determine the cold range margins of the species. Importantly, this is a ?forward? prediction made entirely independently of the distribution data. Therefore, it allows a confident and biologically informed forecasting of further invasion and range shifting driven by climate change. For ragweed, such forecasts are extremely important as the species is a serious crop weed and its airborne pollen is a major cause of allergy and asthma in humans. Our results show that phenology can be a key determinant of species' range margins, so integrating phenology into species distribution models offers great potential for the mechanistic modelling of range dynamics.

1.The phenology of many species has been shown to shift under climate change. However, because species respond at different rates, ecological communities may be disrupted leading to species extinctions and loss of ecosystem services. Hence, there is a need to monitor and understand phenological change. 2.Population data, gathered by standardised monitoring schemes, can be used to this end. However, such schemes require significant organisation and financial resources. Distribution data (georeferenced biological records with dates) are easier and cheaper to collect and may be an unexploited resource for phenology analyses. This would allow analysis of more taxa from more regions of the world. However, distribution data are potentially biased due to the unstandardised behaviour of biological recorders. 3.Here, the ability of distribution data record dates to accurately predict phenology is investigated by using the British butterfly fauna as a model system. We used the total number of distribution records per unit time across Great Britain as a proxy for butterfly abundance. Phenology metrics of mean flight date and flight period length were then calculated from the resulting abundance–time relationships for each year in a 15‐year time series. These estimates were validated against those generated from a standardised‐effort population monitoring scheme. 4.We analysed 1 078 328 records from 30 British butterflies and found that distribution data accurately predicted the mean flight date for 22 of the 30 species tested. Flight period length was only predicted accurately for seven of 30 species. 5.We found a nonlinear but consistent positive relationship between the accuracy of mean flight date estimates and sample size (number of records) at both inter‐ and intraspecific scales. Our results suggest that a threshold sample size of c. 6500 distribution records (430 per year) is a pragmatic compromise between accuracy and recording effort, leading to little loss of accuracy in phenology predictions (an average decrease in accuracy of 2·9 days was observed). 6.The results suggest that distribution data are a potentially useful resource for phenology research. This may allow practitioners to monitor particular regions and previously unstudied species relatively cheaply using existing mapping schemes.

The establishment of protected areas, such as Natura 2000, is a common approach to curbing biodiversity loss. But many of these areas are owned or managed by private actors. Policies indicate that their involvement should be encouraged to ensure long term success. However, to date there have been no systematic evaluations of whether local actor involvement in the management of protected areas does in fact contribute to the conservation of biodiversity, which is the expressed policy goal. Research incorporating both qualitative and quantitative data was carried out in three case studies in Scotland where local actor input was required in the development and/or implementation of Natura 2000 management plans. No relationship was found between the characteristics of the process of stakeholder involvement and stakeholders’ perceptions of future biodiversity outcomes. Social outcomes of increased stakeholder involvement, such as increased trust, did however increase the perceived likelihood of positive future biodiversity outcomes. The findings indicate that efforts aimed at increasing stakeholder involvement in the management of protected areas need to consider making processes more independent, and acknowledge and address underlying biodiversity conflicts. The findings also emphasise the need to evaluate multi-level conservation efforts in terms of processes, social outcomes and biodiversity outcomes.

Biological invasions are a major driver of global change, for which models can attribute causes, assess impacts and guide management. However, invasion models typically focus on spread from known introduction points or non-native distributions and ignore the transport processes by which species arrive. Here, we developed a simulation model to understand and describe plant invasion at a continental scale, integrating repeated transport through trade pathways, unintentional release events and the population dynamics and local anthropogenic dispersal that drive subsequent spread. We used the model to simulate the invasion of Europe by common ragweed (Ambrosia artemisiifolia), a globally invasive plant that causes serious harm as an aeroallergen and crop weed. Simulations starting in 1950 accurately reproduced ragweed's current distribution, including the presence of records in climatically unsuitable areas as a result of repeated introduction. Furthermore, the model outputs were strongly correlated with spatial and temporal patterns of ragweed pollen concentrations, which are fully independent of the calibration data. The model suggests that recent trends for warmer summers and increased volumes of international trade have accelerated the ragweed invasion. For the latter, long distance dispersal because of trade within the invaded continent is highlighted as a key invasion process, in addition to import from the native range. Biosecurity simulations, whereby transport through trade pathways is halted, showed that effective control is only achieved by early action targeting all relevant pathways. We conclude that invasion models would benefit from integrating introduction processes (transport and release) with spread dynamics, to better represent propagule pressure from native sources as well as mechanisms for long-distance dispersal within invaded continents. Ultimately, such integration may facilitate better prediction of spatial and temporal variation in invasion risk and provide useful guidance for management strategies to reduce the impacts of invasion.

1. A mechanistic understanding of seed dispersal is important for understanding and predicting dispersal patterns and spatial population dynamics. We analysed a mechanistic model for the ballistic dispersal of the widespread invasive species Impatiens glandulifera. The model deterministically simulates individual seed trajectories, including the interaction between seeds and atmospheric winds. It is fully specified using independently measured plant traits and wind velocities. 2. To parameterise the model we conducted field surveys and analysed high speed video footage of seed release. We explicitly incorporated intraspecific variation in key biological parameters (seed size and mass, release height, velocity and direction), as well as variation in wind velocities. This parameter variation introduces process-based stochasticity to the deterministic model, allowing the derivation of population-level dispersal kernels. 3. Analysis of the parameterised model showed complex interactions between plant dispersal parameters and wind conditions. For example, the optimal seed launch angle in still wind is 32° above horizontal, but this rises in stronger tailwinds. In a headwind, it can be optimal to either launch the seed straight upwards or slightly below the horizontal. Headwinds also blow projected seeds back towards the source, initially decreasing but then increasing net dispersal distance, which can reverse the positive effect of seed release height on dispersal distance. 4. To validate the model we performed a field experiment in which seeds were trapped up to 6 m from potted I. glandulifera plants. Seed dispersal was biased in the direction of the prevailing wind, which was only reproduced by simulations of the model that included the wind-modification of ballistic trajectories. The correspondence between the model and the data was good (R2 = 0.882), but improved if seed release was biased to occur in more windy conditions (R2 = 0.945) as might occur through mechanical disturbance to the seed pods. 5. Synthesis: Our validated model provides a functional understanding of seed dispersal in I. glandulifera. The model reveals surprisingly complex interactions between the ballistic dispersal mechanism and the wind and shows how population-level dispersal patterns emerge from intraspecific variation in dispersal traits.

Aim: To assess the relative impacts of spatial, local environmental and habitat connectivity on the structure of aquatic macrophyte communities in lakes designated for their conservation value. Location: Selected lakes of conservation importance all over Scotland, representing a wide variety of lake habitat types and associated macrophyte communities. Methods: Local environmental variables and species occurrence were measured in the field. Spatial variables were generated using principal coordinates of neighbour matrices (PCNM) analysis. Connectivity between each lake and its neighbours was defined as either (i) all lakes within a radius of 5, 10, 25, 50, 75 or 100 km; (ii) all lakes in same river system; or (iii) all lakes in the same catchment and upstream of the lake. Using variance partitioning within canonical correspondence analysis, the relative impact of E = local environment, S = space and C = lake connectivity was compared on submerged (n = 119 lakes) and emergent (n = 96 lakes) macrophyte assemblages. Results: Local environmental conditions, such as total phosphorus, alkalinity/conductivity and the presence of invasive species, as well as spatial gradients were key drivers of observed variation in macrophyte communities; e.g., for submerged macrophytes, a combination of local to moderate factors relating to water chemistry and broad-scale gradients reflecting elevation and climate are important. Spatially structured environmental variables explained a large portion of observed variation. Main conclusions: Our findings confirmed the need to manage local environmental pressures such as eutrophication, but suggested that the traditional catchment approach was insufficient. The spatial aggregation of environmental and connectivity factors indicated that a landscape scale approach should be used in lake management to augment the risk assessment to conservation species from the deterioration of suitable lake sites over broad biogeographic areas.

Aim: Species distribution models are increasingly used to predict the impacts of global change on whole ecological communities by modelling the individualistic niche responses of large numbers of species. However, it is not clear whether this single-species ensemble approach is preferable to community-wide strategies that represent interspecific associations or shared responses to environmental gradients. Here, we test the performance of two multi-species modelling approaches against equivalent single-species models. Location: Great Britain. Methods: Single- and multi-species distribution models were fitted for 701 native British plant species at a 10-km grid scale. Two machine learning methods were used – classification and regression trees (CARTs) and artificial neural networks (ANNs). The single-species versions are widely used in ecology but their multivariate extensions are less well known and have not previously been evaluated against one another. We compared their abilities to predict species distributions, community compositions and species richness in an independent geographical region reserved from model-fitting. Results: The single- and multi-species models performed similarly, although the community models gave slightly poorer predictive accuracy by all measures. However, from the point of view of the whole community they were much simpler than the array of single-species models, involving orders of magnitude fewer parameters. Multi-species approaches also left greater residual spatial autocorrelation than the individualistic models and, contrary to expectation, were relatively less accurate for rarer species. However, the fitted multi-species response curves had lower tendency for pronounced discontinuities that are unlikely to be a feature of realized niche responses. Main conclusions: Although community distribution models were slightly less accurate than single-species models, they offered a highly simplified way of modelling spatial patterns in British plant diversity. Moreover, an advantage of the multi-species approach was that the modelling of shared environmental responses resolved more realistic response curves. However, there was a slight tendency for community models to predict rare species less accurately, which is potentially disadvantageous for conservation applications. We conclude that multi-species distribution models may have potential for understanding and predicting the structure of ecological communities, but were slightly inferior to single-species ensembles for our data.

1. We use population synchrony as a new empirical method to assess functional connectivity – the permeability of landscapes given species dispersal attributes. Functional connectivity is important because well‐connected metapopulations are expected to be more resistant to stochastic events causing extinction. 2. A variety of factors impact population synchrony, and we attempt to account for several of these (shared climate, distance, habitat similarity and location within the range) before investigating impacts of the suitability of the landscape between populations – a proxy for permeability to dispersal. 3. For the Speckled Wood butterfly (Pararge aegeria), we find that population synchrony is positively correlated with landscape suitability, suggesting that synchrony might be used to measure functional connectivity. 4. The importance of landscape suitability for population synchrony shows a humped relationship with distance – suitability has no effect between 0 and 20 km, then showing a significant positive effect above 20 km but with reduced (still significant) effect from 160 to 200 km. This suggests that relatively close populations may exchange sufficient migrants for synchronisation, regardless of the matrix suitability. In contrast, more separate populations are synchronised only where the landscape permits functional connectivity, most likely through dispersal between intermediate stepping‐stone populations. 5. Synthesis and applications. We show that patterns of synchrony in long‐term monitoring data can be used to estimate functional connectivity of landscapes. As such, this technique might be used to test and prioritise the effectiveness of land management for conservation of species and to mitigate the effects of climate change.

Understanding the mechanisms by which global climate change and habitat loss impact upon biodiversity is essential in order to mitigate any negative impacts. One such impact may be changes to population synchrony (defined as correlated fluctuations in the density of separate populations). It is well established that synchrony depends on both dispersal ability and correlated environmental conditions, for example shared climate. However, what is not clear is whether differences in habitat or position within a species' range also mediate synchrony. Since synchronous metapopulations are thought to be more extinction-prone, establishing the drivers of synchrony has clear conservation implications. Using three butterfly species (Maniola jurtina, Pyronia tithonus and Aphantopus hyperantus) we investigated the effects of habitat similarity and range position on population synchrony, after accounting for the effects of distance and climate. Range position was present in all minimum adequate models, though non-significant using Mantel randomization tests in one case. We show that M. jurtina and P. tithonus synchrony is not consistent across species' ranges, with marginal populations showing more synchronous dynamics. Increased climatic constraints on marginal populations, leading to a narrower range of suitable microhabitats may be responsible for this, which is supported by the result that habitat similarity between sites was also positively correlated with population synchrony. As the landscape becomes increasingly homogeneous, overall population synchrony may be expected to rise. We conclude that habitat modification and climate change have the capacity to drive changes in population synchrony that could make species more vulnerable to extinction.

Aim Species distribution models (SDMs) are used to infer niche responses and predict climate change-induced range shifts. However, their power to distinguish real from chance associations between spatially autocorrelated distribution and environmental data at continental scales has been questioned. Here, this is investigated at a regional (10 km) scale by modelling the distributions of 100 plant species native to the United Kingdom. Location UK Methods SDMs fitted using real climate data were compared to those utilising simulated climate gradients. The simulated gradients preserve the exact values and spatial structure of the real ones, but have no causal relationships with any species and so represent an appropriate null model. SDMs were fitted as generalised linear models or by the Random Forest machine learning algorithm and were either non-spatial or included spatially-explicit trend surfaces or autocovariates as predictors. Results GLMs erroneously detected significant effects (P

Repeated biomass harvesting for human livelihood needs can cause significant changes in forest structure, composition and diversity. Such impacts have often been associated with the distance to villages and their size, but the effects of individual villages in relation to their characteristics have been little studied. Focussing on the issues around communities' impacts on the forest, this research aimed to understand how resource extraction by two Buffer Zone villages in Nepal's Bardia National Park affects forest structure and diversity. Tree density, basal area, diameter at breast height (DBH), species richness, two diversity indices and disturbance indices were recorded in 108 plots in 12 transects, from March till May 2005, within the park along the village boundaries, where resource extraction occurs, and in the core park area which is isolated from human disturbance. Forest subject to resource extraction had a lower density of trees, smaller DBH and lower species richness and diversity. Forest disturbance declined with distance from the villages but the two villages differed widely in their impact on the forest. Resource use was more intensive in Shivapur village and percentage of trees lopped, average lopping intensity and numbers of dung piles were statistically significant. This was mainly due to the lack of access to Buffer Zone community Forest for Shivapur households. Interviews with villagers suggested that there have been changes in species availability compared to 5 years ago. Households also underestimated the impact of their resource extraction, which may prevent them from changing their behaviour to benefit conservation. It is clear that understanding rural communities' needs, their framing of resource extraction and utilisation, and dependence on forest resources will be very helpful for long term conservation measures.

Upland peat soils represent a large terrestrial carbon store and as such have the potential to be either an ongoing net sink of carbon or a significant net source of carbon. In the UK many upland peats are managed for a range of purposes but these purposes have rarely included carbon stewardship. However, there is now an opportunity to consider whether management practices could be altered to enhance storage of carbon in upland peats. Further, there are now voluntary and regulated carbon trading schemes operational throughout Europe that mean stored carbon, if verified, could have an economic and tradeable value. This means that new income streams could become available for upland management. The ‘Sustainable Uplands’ RELU project has developed a model for calculating carbon fluxes from peat soils that covers all carbon uptake and release pathways (e.g. fluvial and gaseous pathways). The model has been developed so that the impact of common management options within UK upland peats can be considered. The model was run for a decade from 1997–2006 and applied to an area of 550 km 2 of upland peat soils in the Peak District. The study estimates that the region is presently a net sink of − 62 ktonnes CO 2 equivalent at an average export of − 136 tonnes CO 2 equivalent/km 2 /yr. If management interventions were targeted across the area the total sink could increase to − 160 ktonnes CO 2 /yr at an average export of − 219 tonnes CO 2 equivalent/km 2 /yr. However, not all interventions resulted in a benefit; some resulted in increased losses of CO 2 equivalents. Given present costs of peatland restoration and value of carbon offsets, the study suggests that 51% of those areas, where a carbon benefit was estimated by modelling for targeted action of management interventions, would show a profit from carbon offsetting within 30 years. However, this percentage is very dependent upon the price of carbon used.

We investigated whether signals of known dispersal processes and habitat patch turnover could be detected in a snapshot of the distribution of the tansy leaf beetle Chrysolina graminis among patches of its host plant tansy Tanacetum vulgare. Beetle occupancy in 1305 patches was analysed using autologistic generalised additive models (GAMs). These model spatial autocorrelation with an autocovariate calculated as the distance-weighted rate of occupancy among neighbouring patches. The autocovariate that best explained beetle occupancy was one which represented the active search for patches during beetle dispersal, included a distance weight that closely matched a previously fitted dispersal kernel and had neighbourhood sizes encompassing ∼ 95% of known dispersal distances. Autocovariates distinguishing between neighbours on the same and opposite riverbanks outperformed those that did not, revealing the river as a barrier to dispersal. Differentiating between up and downstream autocorrelation did not improve model fit, as is consistent with the beetle's lack of directional bias in dispersal. Habitat connectivity (the extent to which it was surrounded by other patches) did not appear to affect beetle occupancy in the field, while positive effects were found for distributions simulated from the GAM. We argue that this reflects a non-equilibrium distribution driven by slow responses to high rates of habitat patch turnover due to limited dispersal ability. Our findings suggest that presence/absence snapshots can reveal patterns of dispersal and be used to test whether species' ranges are at equilibrium. Such information is important for effective conservation so the possibility of inferring these patterns from distribution data is an appealing one.

1. It is widely appreciated that management shapes the dynamics of many ecological systems, but ecologists rarely consider the reverse interaction, that is, the ecological influences on management decisions. Reciprocal feedback between management and ecology can cause complex system behaviour. Therefore, better predictions about how external policy-drivers or climate change will affect semi-natural ecosystems may be made when both the ecological and human dimensions are considered. 2. We develop a spatially-explicit model of moorland vegetation dynamics and management decisions about sheep grazing and heather burning in the Peak District National Park, UK. Competition between dwarf shrubs, bracken and graminoids is mediated by grazing, dwarf shrub age (determined by burning rotation) and environmental gradients. Management decisions depend on vegetation cover in a model parameterized through interviews with upland managers. 3. Current management regimes are designed to reverse historical dwarf shrub losses, and simulations suggest that this reversal should occur in the future. After equilibration, grazing densities fall and dwarf shrubs have expanded from their current distribution, mainly at the expense of graminoids. This causes more land to come under managed burning, but current intensities are maintained. 4. Enforcing winter or summer grazing densities influences model vegetation cover and causes other aspects of the management strategy to adapt. For example, when summer grazing is banned, dwarf shrub cover increases and there is a shift towards grouse moor management. 5. Simulations with warmer temperatures indicate that climate change may increase bracken invasion of the moorland and prevent re-vegetation of bare peat. This is associated with a reduction in managed burning that causes the dwarf shrub community to become dominated by the older, degenerate growth phase. 6. Synthesis and applications. Our model suggests that current management paradigms could achieve their aim of restoring historically degraded moorland over the coming century, but that climate change may prevent this from occurring. One application of the model would be to try to design management paradigms that are robust to this. As such, models of coupled human–natural systems can provide a valuable tool for assessing the impacts of policy decisions and climate change on semi-natural ecosystems at landscape scales.

Movement underpins animal spatial ecology and is often modelled as habitat-dependent correlated random walks. Here, we develop such a model for the flightless tansy leaf beetle Chrysolina graminis moving within and between patches of its host plant tansy Tanacetum vulgare. To parameterize the model, beetle movement paths on timescales of minutes were observed in uniform plots of tansy and inter-patch matrix (meadow) vegetation. Movement lasted longer, covered greater distances and had narrower turning angles in the matrix. Simulations of the model emulated an independent two-season multi-patch mark-resight study at daily timescales and included variable boundary-mediated behaviour affecting the probability of leaving habitat patches. As boundaries in the model became stronger there were disproportionately large decreases in net displacements, inter-patch movements and the proportion of beetles in the matrix. The model produced realistic patterns of population-level displacement over periods up to 13 days with fully permeable boundaries for one dataset and strong boundaries for the other. This may be explained by the heights of the tansy patches in each study, as beetles will be unable to cross the boundary near the top of a patch that emerges from the matrix. The simulations demonstrate the important effects of boundary behaviour on displacement patterns and indicate temporal and spatial variability in permeability. Realistic models of movement must therefore include behaviour at habitat boundaries.

Robust modelling tools for predicting the spread and dispersal of plant pests are required for effective risk assessment of the impact of both novel and existing pests in the EU. The ongoing project OC/EFSA/PLH/2012/01 reviews the current state of the art in pest spread modelling to evaluate the fitness of alternative modelling strategies for various aspects of risk assessment. We report on the first phase of this project – an extensive literature review to assess the scientific literature on plant pest spread modelling combined with a cluster analysis of the results of the search to identify groups of model strategies. Using systematic review methodology, we implemented robust and step-wise screening criteria. The final assessment yielded 468 scientific articles describing spatial models of pest spread or dispersal, within which 478 unique models are reported. For clustering, 27 data fields describing each model’s representation of time, space, the pest organism(s) and the host plant(s) were populated. Subsequently multi-variate statistical clustering of the data suggested partitioning the data into eight clusters, or model strategies. We describe the differences among model clusters both in terms of the clustering data and for a number of other data fields characterising the ways in which the models are used. Clusters are significantly differentiated according to the functional and taxonomic groups of the pests and host plants and the ways in which the models were parameterised and analysed. We reveal important deficiencies in current modelling, including a lack of multi-species models, lack of evolutionary models and under-representation of spread driven by multiple pest entry or introduction. We describe how we envisage integrating this analysis in a Decision Support Tool for the assessment of the fitness of the model strategies for pest risk assessments. Such a tool will be of high importance for the EFSA Panel on Plant Health.