Early Engagement of Stakeholders with Individual-Based Modeling Can Inform Research for Improving Invasive Species Management: The Round Goby as a Case Study

Abstract

Individual-based models (IBMs) incorporating realistic representations of key range-front processes such as dispersal can be used as tools to investigate the dynamics of invasive species. Managers can apply insights from these models to take effective action to prevent further spread and prioritize measures preventing establishment of invasive species. We highlight here how early-stage IBMs (constructed under constraints of time and data availability) can also play an important role in defining key research priorities for providing key information on the biology of an invasive species in order that subsequent models can provide robust insight into potential management interventions. The round goby, Neogobius melanostomus, is currently spreading through the Baltic Sea, with major negative effects being reported in the wake of its invasion. Together with stakeholders, we parameterize an IBM to investigate the goby's potential spread pattern throughout the Gulf of Gdansk and the Baltic Sea. Model parameters were assigned by integrating information obtained through stakeholder interaction, from scientific literature, or estimated using an inverse modeling approach when not available. IBMs can provide valuable direction to research on invasive species even when there is limited data and/or time available to parameterize/fit them to the degree to which we might aspire in an ideal world. Co-development of models with stakeholders can be used to recognize important invasion patterns, in addition to identifying and estimating unknown environmental parameters, thereby guiding the direction of future research. Well-parameterized and validated models are not required in the earlier stages of the modeling cycle where their main utility is as a tool for thought.

Integrating an individual-based model with approximate Bayesian computation to predict the invasion of a freshwater fish provides insights into dispersal and range expansion dynamics

“…Thus, when these species are introduced into a new region, whether intentionally or accidentally (Gozlan et al 2010a), the data available for predicting their invasiveness are often limited (Top et al 2018), resulting in poorly informed model parameters (Heikkinen et al 2014;Urban et al 2016) and final models with high uncertainty (Parry et al 2013). We showed that monitoring programmes can provide dispersal time series that, when coupled with ABC methods, can help overcome this lack of prior data, enabling more robust predictions of the dispersal dynamics and future invasiveness of the modelled species (Neeson et al 2012;Barros et al 2016;Samson et al 2017).…”

“…The platform RangeShifter was used to develop the IBM, as it allows the development of spatially explicit IBMs in which the three key dispersal phases of emigration, transfer and settlement are represented independently (Bocedi et al 2014;Samson et al 2017). A customised version of RangeShifter v. 2.0 was used, which incorporated code to estimate model parameters by ABC given suitable high-level observed data.…”

“…The transfer of individuals was modelled using the stochastic movement simulator (SMS). This models how the individual moves on a cell-by-cell basis, as determined by relative costs (in the sense of the least cost path approach) and a tendency to maintain a correlated path (directional persistence) (Palmer et al 2011;Coulon et al 2015;Samson et al 2017). Our habitat quality raster was incorporated, so that a dispersing individual would be more likely to move into the wider of two branches when reaching a confluence.…”

“…Predictive approaches that provide realistic representations of real-life invasions and enable scenario testing can develop understanding of the dispersal dynamics of invasive species (Bocedi et al 2014;Samson et al 2017). A range of modelling approaches exist for predicting the dynamics of range expansions, including analytical methods such as integro-difference modelling (Gilbert et al 2014(Gilbert et al , 2017 and stochastic simulations, including individual-based models (IBMs).…”

“…The performance of invasion IBMs can, however, be improved when empirical data are available that enable model fitting and enable the parameters that most strongly influence the predicted patterns to be identified (Phang et al 2016), but these data are rarely available. For example, although Samson et al (2017) investigated the spread of invasive round goby Neogobius melanostomus using the 'RangeShifter' platform (Bocedi et al 2014), with model parameters developed from stakeholder interaction, scientific literature and inverse modelling approaches, the model could not be calibrated fully due to an absence of empirical data on their invasion. When empirical data are available, the modelling processes can utilise these to derive more robust estimates of model parameters by the application of inverse fitting techniques, such as approximate Bayesian computation (ABC;van der Vaart et al 2015).…”