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

Anthropogenic induced climate change is resulting in warming temperatures and changes in
precipitation patterns. Whilst the scale of future temperature increases is emission
dependent, warming of up to 2oC is considered inevitable. A further aspect of global change
is introduced non-native species (INNS). Whilst only a small proportion of INNS develop
invasions, 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 when
predictions indicate the high-risk species for priority actions. Predictions of INNS
distributions are based on species distribution models that predict environmental suitability
based on abiotic data (e.g. climate). INNS impacts are predicted by empirical and modelling
methods. These approaches cannot, however, integrate how altered abiotic and biotic
conditions affect emergent relationships between INNS and native communities, or predict
how 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 between
agents and their environment. ABMs are increasingly powerful tools for exploring ecological
interactions in changing environments and within spatial and regulatory contexts. They have
not, however, been applied to understanding how climate change and management
interventions interact to affect the invasion probabilities of INNS.
Thus, using river basins in England as model areas, freshwater fishes as model INNS and the
Environment Agency as Case Partner, the PhD develops novel ABMs to predict current and
future INNS invasion probabilities according to climate change projections, hydrological
connectivity and management interventions. The ABMs will predict future INNS
distributions and impacts within and between English river basins, and over a range of
climate and management scenarios. Objectives (O) will:
O1 Select model INNS and generate data on current and future species-climate
relationships, species-species interactions, and identify appropriate management
interventions;
O2 Develop an initial ABM for predicting the dispersal and impact of an INNS within English
river basins according to current climate conditions and hydrological connectivity, and no
management interventions;
O3 For each model INNS, parameterise the initial ABM to produce species-specific final
ABMs to predict current INNS invasion probabilities within and between river basins; and
O4 Use the final ABMs to predict how climate change and management interventions will
influence future invasion probabilities of INNS in England.
In O1, horizon scanning selects three model INNS that are not yet invasive in England and
represent different life history strategies (intense r-selected to k). Data for model
parameterisation are then developed for each model INNS using field, laboratory and
literature approaches. An initial ABM model is developed in O2 in RANGESHIFTER using
relevant spatial, ecological, hydrological and bioclimatic data. The final ABMs are produced
in O3 by integrating parameters and management interventions from O1 with the ABM
from O2. They are used predictively in O4 by integrating climate change projections with
management interventions to generate knowledge relevant for natural resource
management.