A multi-species modelling approach to examine the impact of alternative climate change adaptation strategies on range shifting ability in a fragmented landscape

An individual-based model of animal dispersal and population dynamics was used to test the effects of different climate change adaptation strategies on species range shifting ability, namely the improvement of existing habitat, restoration of low quality habitat and creation of new habitat. These strategies were implemented on a landscape typical of fragmentation in the United Kingdom using spatial rules to differentiate between the allocation of strategies adjacent to or away from existing habitat patches. The total area being managed in the landscape was set at realistic levels based on recent habitat management trends. Eight species were parameterised to broadly represent different stage structure, population densities and modes of dispersal. Simulations were initialised with the species occupying 20% of the landscape and run for 100 years. As would be expected for a range of real taxa, range shifting abilities were dramatically different. This translated into large differences in their responses to the adaptation strategies. With conservative (0.5%) estimates of the area prescribed for climate change adaptation, few species display noticeable improvements in their range shifting, demonstrating the need for greater investment in future adaptation. With a larger (1%) prescribed area, greater range shifting improvements were found, although results were still species-specific. It was found that increasing the size of small existing habitat patches was the best way to promote range shifting, and that the creation of new stepping stone features, whilst beneficial to some species, did not have such broad effect across different species

Coupled land use and ecological models reveal emergence and feedbacks in socio-ecological systems

Acknowledgements: This work was supported by an EPSRC Doctoral Training Centre grant (EP/G03690X/1). Supplementary material (Appendix ECOG‐04039 at ). Appendix 1.Peer reviewedPublisher PD

Emerging Opportunities for Landscape Ecological Modelling

Landscape ecological modelling provides a vital means for understanding the interactions between geographical, climatic, and socio-economic drivers of land-use and the dynamics of ecological systems. This growing field is playing an increasing role in informing landscape spatial planning and management. Here, we review the key modelling approaches that are used in landscape modelling and in ecological modelling. We identify an emerging theme of increasingly detailed representation of process in both landscape and ecological modelling, with complementary suites of modelling approaches ranging from correlative, through aggregated process based approaches to models with much greater structural realism that often represent behaviours at the level of agents or individuals. We provide examples of the considerable progress that has been made at the intersection of landscape modelling and ecological modelling, while also highlighting that the majority of this work has to date exploited a relatively small number of the possible combinations of model types from each discipline. We use this review to identify key gaps in existing landscape ecological modelling effort and highlight emerging opportunities, in particular for future work to progress in novel directions by combining classes of landscape models and ecological models that have rarely been used together

Chapter 4. In search of relevant predictors for marine species distribution modelling using the MarineSPEED benchmark dataset

Aim: Ideally, datasets for species distribution modelling (SDM) contain evenly sampled records covering the entire distribution of the species, confirmed absences and auxiliary ecophysiological data allowing informed decisions on relevant predictors. Unfortunately, these criteria are rarely met for marine organisms for which distributions are too often only scantly characterized and absences generally not recorded. Here, we investigate predictor relevance as a function of modelling algorithms and settings for a global dataset of marine species.Location: Global marine.Methods: We selected well-studied and identifiable species from all major marine taxonomic groups. Distribution records were compiled from public sources (e.g., OBIS, GBIF, Reef Life Survey) and linked to environmental data from Bio-ORACLE and MARSPEC. Using this dataset, predictor relevance was analysed under different variations of modelling algorithms, numbers of predictor variables, cross-validation strategies, sampling bias mitigation methods, evaluation methods and ranking methods. SDMs for all combinations of predictors from eight correlation groups were fitted and ranked, from which the top five predictors were selected as the most relevant. Results: We collected two million distribution records from 514 species across 18 phyla. Mean sea surface temperature and calcite are, respectively, the most relevant and irrelevant predictors. A less clear pattern was derived from the other predictors. The biggest differences in predictor relevance were induced by varying the number of predictors, the modelling algorithm and the sample selection bias correction. The distribution data and associated environmental data are made available through the R package marinespeed and at http://marinespeed.org.Main conclusions: While temperature is a relevant predictor of global marine species distributions, considerable variation in predictor relevance is linked to the SDM set-up. We promote the usage of a standardized benchmark dataset (MarineSPEED) for methodological SDM studies

Tree loss impacts on ecological connectivity: Developing models for assessment

Trees along linear features are important landscape features, and their loss threatens ecological connectivity. Until recently, trees outside of woodlands (TOWs) were largely unmapped however; the development of innovation mapping techniques provides opportunities to understand the distribution of such trees and to apply spatially explicit models to explore the importance of trees for connectivity. In this study, we demonstrate the utility of models when investigating tree loss and impacts on connectivity. Specifically, we investigated the consequences of tree loss due to the removal of roadside trees, a common management response for diseased or damaged trees, on wider landscape functional connectivity. We simulated the loss of roadside trees within six focal areas of the south east of the UK. We used a spatially explicit individual-based modelling platform, RangeShifter, to model the movement of 81 hypothetical actively dispersing woodland breeding species across these agriculturally fragmented landscapes. We investigated the extent to which removal of trees, from roadsides within the wider landscape, affected the total number of successful dispersers in any given year and the number of breeding woodlands that became isolated through time. On average roadside trees accounted for < 2% of land cover, but removing 60% of them (~ 1.2% of land cover) nevertheless decreased the number of successful dispersers by up to 17%. The impact was greatest when roadside trees represented a greater proportion of canopy cover. The study therefore demonstrates that models such as RangeShifter can provide valuable tools for assessing the consequences of losing trees outside of woodlands

Contribution of spatially explicit models to climate change adaptation and mitigation plans for a priority forest habitat

Climate change will impact forest ecosystems, their biodiversity and the livelihoods they sustain. Several adaptation and mitigation strategies to counteract climate change impacts have been proposed for these ecosystems. However, effective implementation of such strategies requires a clear understanding of how climate change will influence the future distribution of forest ecosystems. This study uses maximum entropy modelling (MaxEnt) to predict environmentally suitable areas for cork oak (Quercus suber) woodlands, a socio-economically important forest ecosystem protected by the European Union Habitats Directive. Specifically, we use two climate change scenarios to predict changes in environmental suitability across the entire geographical range of the cork oak and in areas where stands were recently established. Up to 40 % of current environmentally suitable areas for cork oak may be lost by 2070, mainly in northern Africa and southern Iberian Peninsula. Almost 90 % of new cork oak stands are predicted to lose suitability by the end of the century, but future plantations can take advantage of increasing suitability in northern Iberian Peninsula and France. The predicted impacts cross-country borders, showing that a multinational strategy, will be required for cork oak woodland adaptation to climate change. Such a strategy must be regionally adjusted, featuring the protection of refugia sites in southern areas and stimulating sustainable forest management in areas that will keep long-term suitability. Afforestation efforts should also be promoted but must consider environmental suitability and land competition issues

Choice of predictor variables as a source of uncertainty in continental-scale species distribution modelling under climate change: a case study

Aim: species distribution modelling is commonly used to guide future conservation policies in the light of potential climate change. However, arbitrary decisions during the model-building process can affect predictions and contribute to uncertainty about where suitable climate space will exist. For many species, the key climatic factors limiting distributions are unknown. This paper assesses the uncertainty generated by using different climate predictor variable sets for modelling the impacts of climate change.Location: Europe, 10° W to 50° E and 30° N to 60° N.Methods: using 1453 presence pixels at 30 arcsec resolution for the great bustard (Otis tarda), predictions of future distribution were made based on two emissions scenarios, three general climate models and 26 sets of predictor variables. Twenty-six current models were created, and 156 for both 2050 and 2080. Map comparison techniques were used to compare predictions in terms of the quantity and the location of presences (map comparison kappa, MCK) and using a range change index (RCI). Generalized linear models (GLMs) were used to partition explained deviance in MCK and RCI among sources of uncertainty.Results: the 26 different variable sets achieved high values of AUC (area under the receiver operating characteristic curve) and yet introduced substantial variation into maps of current distribution. Differences between maps were even greater when distributions were projected into the future. Some 64–78% of the variation between future maps was attributable to choice of predictor variable set alone. Choice of general climate model and emissions scenario contributed a maximum of 15% variation and their order of importance differed for MCK and RCI.Main conclusions: generalized variable sets produce an unmanageable level of uncertainty in species distribution models which cannot be ignored. The use of sound ecological theory and statistical methods to check predictor variables can reduce this uncertainty, but our knowledge of species may be too limited to make more than arbitrary choices. When all sources of modelling uncertainty are considered together, it is doubtful whether ensemble methods offer an adequate solution. Future studies should explicitly acknowledge uncertainty due to arbitrary choices in the model-building process and develop ways to convey the results to decision-maker

Spatial methods for modelling species distributions

Species distribution modelling methods are used for a variety of applications including: to assess current patterns of biodiversity, to make predictions about the impacts of environmental and climate change, and to assist in conservation planning. However, important factors are often neglected both in the pre-processing of data (e.g. ignoring sampling bias), and in the construction of models (e.g. ignoring ecological processes). In terms of the pre-processing of data, recent improvements in distance sampling methods are used to convert count data to abundance estimates, utilising both distance and habitat data from a previously conducted bird count survey. Biotic interactions are studied using MaxEnt and pairs of virtual species; a novel iterative method is demonstrated, using each species prediction as a subsequent variable for the partner species. Population dynamics and dispersal are studied using RangeShifter, a recently developed individual-based model. A number of climate change adaptation actions are applied to a section of UK landscape data, and the range shifting ability of a set of focal species is measured. Many previous studies have predicted climate change impacts on species; some have started to incorporate simple measures of dispersal ability. This work demonstrates the importance of considering both dispersal and population dynamics when predicting the future distributions of species and assessing their ability to track climate change. Finally, dynamic feedbacks between species and their environment are studied by coupling RangeShifter with CRAFTY, a recently developed agent-based model of land-use dynamics. Socio-ecological system dynamics are crucial in determining species distributions, but have rarely been studied as a truly coupled system. The coupled model presented here is the first of its kind, modelling both animals and land-use agents at an individual level. A case study is presented, demonstrating the feedback mechanisms that exist between pollinators and farms that rely on them, and the potential risk posed by agricultural intensification