Improving reintroduction success in large carnivores through individual-based modelling: How to reintroduce Eurasian lynx (Lynx lynx) to Scotland

Abstract

Globally, large carnivores have been heavily affected by habitat loss, fragmentation and persecution, sometimes resulting in local extinctions. With increasing recognition of topdown trophic cascades and complex predator-prey dynamics, reintroductions are of growing interest for restoration of ecosystem functioning. Many reintroductions have however failed, in part due to poor planning and inability to model complex eco-evolutionary processes to give reliable predictions. Using the case study of Eurasian lynx (Lynx lynx), a large predator being considered for reintroduction to Scotland, we demonstrate how an individual-based model that integrates demography with three distinct phases of dispersal (emigration, transfer and settlement) can be used to explore the relative suitability of three geographically-distant potential reintroduction sites, multi-site reintroductions and two founding population sizes. For a single-site reintroduction of 10 lynx, our simulation results show a clear hierarchy of suitability across all metrics. Reintroduction in the Kintyre Peninsula (west coast) consistently performed best, with a probability of population persistence at year 100 of 83%, and the Scottish component of Kielder Forest (southern Scotland) worst, with only a 21% chance of population persistence to year 100. Simultaneous two-site reintroduction in the Kintyre Peninsula and in Aberdeenshire (near the east coast) of 32 lynx gave a 96% persistence at 100 years. Our model was highly sensitive to survival, particularly of adults, highlighting this parameter's importance for reintroduction success. The results strongly indicate the potential viability of Eurasian lynx reintroduction to Scotland given the current cover of suitable woodland habitat. More generally, our work demonstrates how emerging modelling approaches incorporating increased realism in representing species' demography, ecology and dispersal can have high value for quick, inexpensive assessment of likely reintroduction success and for selection between alternative strategies.

Diffusion modeling reveals effects of multiple release sites and human activity on a recolonizing apex predator

“…Wolves recolonized the area at a rate of about 10 km per year [ 8 ], and their renewed presence mediated over-browsing by elk ( Cervus canadensis ) and allowed the previous vegetation structure to return, subsequently driving additional recovery across the ecosystem [ 5 ]. Many reintroductions are unsuccessful, however [ 9 , 10 ], because the distributions of resources, sources of mortality, and the physical environment—factors that influence recolonization—are highly variable through space and time [ 11 – 13 ]. Recolonization by apex predators is thus spatiotemporally dynamic, especially over large geographic areas that are characterized by finer-scale ecological variability [ 7 ].…”

RangeShifter 2.0: An extended and enhanced platform for modelling spatial eco-evolutionary dynamics and species’ responses to environmental changes

“…The main objective was to provide an individual-based, spatially-explicit modelling platform that integrated population dynamics with sophisticated dispersal behaviour, and that could be used for a variety of applications, from theory development to in-silico testing of management interventions. Indeed, since its release, RangeShifter has been used in studies addressing a range of issues, including testing the effectiveness of alternative management interventions to improve connectivity and population persistence (Aben et al, 2016;Henry et al, 2017), facilitating range expansion (Synes et al, 2015(Synes et al, , 2020, improving reintroduction success (Heikkinen et al, 2015;Ovenden et al, 2019), investigating range dynamics of invasive (Fraser et al, 2015;Dominguez Almela et al, 2020) and recovering species (Sun et al, 2016) and theoretically investigating how different traits and processes affect rate of range expansion (Bocedi, Zurell et al 2014;Henry et al, 2014;Barros et al, 2016;Santini et al, 2016). RangeShifter has also been coupled with CRAFTY (Murray-Rust et al, 2014), an agent-based model designed to explore the impact of land managers' behaviours on land-use change, showing that, in the example context of predicting interactions between crops and their pollinators in a changing agricultural landscape, models that integrate ecological processes with land managers' behaviours, together with their interactions and feed-backs can reveal important dynamics in land use change which might otherwise be missed (Synes et al, 2019;Willemen et al, 2019).…”

Exploring the impact of lesser-known social dynamics on wolf populations through an individual-based approach

“…We ran the original complete version of the model (M0) modifying one parameter value (Table 1) at a time. We increased and decrease the focused parameter value of 5% and run 200 replicates of 25 years simulations (Ovenden et al, 2019). The model was considered sensitive to a parameter if a model output (i.e., mean value over the 200 replicates) with the one modified parameter varies more than 20% from the original results (Kramer-Schadt et al, 2005;Ovenden et al, 2019).…”