Predicting invasion probabilities of introduced non-native freshwater fishes according to climate change and management

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Anthropogenic induced climate change is resulting in warming temperatures and changes inprecipitation patterns. Whilst the scale of future temperature increases is emissiondependent, warming of up to 2oC is considered inevitable. A further aspect of global changeis introduced non-native species (INNS). Whilst only a small proportion of INNS developinvasions, the impacts on biodiversity can be irreversible.In temperate areas, the probabilities of many INNS to establish, spread and impact (i.e.invade) are predicted to increase under climate change. INNS management is assisted whenpredictions indicate the high-risk species for priority actions. Predictions of INNSdistributions are based on species distribution models that predict environmental suitabilitybased on abiotic data (e.g. climate). INNS impacts are predicted by empirical and modellingmethods. These approaches cannot, however, integrate how altered abiotic and bioticconditions affect emergent relationships between INNS and native communities, or predicthow management interventions can reduce invasion probabilities.By contrast, Agent Based Models (ABMs) explicitly consider the system components (agents)and attempt to understand how system properties emerge from interactions betweenagents and their environment. ABMs are increasingly powerful tools for exploring ecologicalinteractions in changing environments and within spatial and regulatory contexts. They havenot, however, been applied to understanding how climate change and managementinterventions interact to affect the invasion probabilities of INNS.Thus, using river basins in England as model areas, freshwater fishes as model INNS and theEnvironment Agency as Case Partner, the PhD develops novel ABMs to predict current andfuture INNS invasion probabilities according to climate change projections, hydrologicalconnectivity and management interventions. The ABMs will predict future INNSdistributions and impacts within and between English river basins, and over a range ofclimate and management scenarios. Objectives (O) will:O1 Select model INNS and generate data on current and future species-climaterelationships, species-species interactions, and identify appropriate managementinterventions;O2 Develop an initial ABM for predicting the dispersal and impact of an INNS within Englishriver basins according to current climate conditions and hydrological connectivity, and nomanagement interventions;O3 For each model INNS, parameterise the initial ABM to produce species-specific finalABMs to predict current INNS invasion probabilities within and between river basins; andO4 Use the final ABMs to predict how climate change and management interventions willinfluence future invasion probabilities of INNS in England.In O1, horizon scanning selects three model INNS that are not yet invasive in England andrepresent different life history strategies (intense r-selected to k). Data for modelparameterisation are then developed for each model INNS using field, laboratory andliterature approaches. An initial ABM model is developed in O2 in RANGESHIFTER usingrelevant spatial, ecological, hydrological and bioclimatic data. The final ABMs are producedin O3 by integrating parameters and management interventions from O1 with the ABMfrom O2. They are used predictively in O4 by integrating climate change projections withmanagement interventions to generate knowledge relevant for natural resourcemanagement.