A novel approach to assessing the ecosystem‐wide impacts of reintroductions

Abstract

Reintroducing a species to an ecosystem can have significant impacts on the recipient ecological community. Although reintroductions can have striking and positive outcomes, they also carry risks; many well‐intentioned conservation actions have had surprising and unsatisfactory outcomes. A range of network‐based mathematical methods has been developed to make quantitative predictions of how communities will respond to management interventions. These methods are based on the limited knowledge of which species interact with each other and in what way. However, expert knowledge isn't perfect and can only take models so far. Fortunately, other types of data, such as abundance time series, is often available, but, to date, no quantitative method exists to integrate these various data types into these models, allowing more precise ecosystem‐wide predictions. In this paper, we develop mathematical methods that combine time‐series data of multiple species with knowledge of species interactions and we apply it to proposed reintroductions at Booderee National Park in Australia. There have been large fluctuations in species abundances at Booderee National Park in recent history, following intense feral fox (Vulpes vulpes) control, including the local extinction of the greater glider (Petauroides volans). These fluctuations can provide information about the system isn't readily obtained from a stable system, and we use them to inform models that we then use to predict potential outcomes of eastern quoll (Dasyurus viverrinus) and long‐nosed potoroo (Potorous tridactylus) reintroductions. One of the key species of conservation concern in the park is the Eastern Bristlebird (Dasyornis brachypterus), and we find that long‐nosed potoroo introduction would have very little impact on the Eastern Bristlebird population, while the eastern quoll introduction increased the likelihood of Eastern Bristlebird decline, although that depends on the strength and form of any possible interaction.

Restoring apex predators can reduce mesopredator abundances

“…Since the first reintroduction program of a large carnivore (brown bear Ursus arctos) motivated by conservation goals in 1937 (Samojlik et al, 2018), different reintroductions have been implemented across the world involving an important number of carnivore species, driven by different motivations, from ethical and moral responsibilities to utilitarian uses (Hayward et al, 2007;Levine et al, 2017). Conservation actions such as reintroductions or translocations are not exempt from controversy (e.g., Baker et al, 2019;Pérez et al, 2012), particularly in the case of apex carnivores (Hayward and Somers, Figure 2. Carnivore densities (estimated using spatial-capture-recapture models in the 1,020 ha study area) before lynx reintroduction (2014), one year (2015) and two years (2016) later (left).…”

Informing management decisions for ecological networks, using dynamic models calibrated to noisy time‐series data

“…In our application, SMC sampling estimates the joint posterior probability distribution for the model parameters in each simulated ecosystem, which includes estimation of initial species population biomass y i 0 , growth rates r i , interaction strengths α ij , measurement noise δ and the uncertainty in all of these quantities. This approach quantifies parameter uncertainty and propagates this uncertainty into scenario predictions via a model ensemble, thereby building upon a recently introduced ensemble ecosystem modelling framework (Baker et al , ). Using this method, we can compare the ecosystem parameters’ true values to their probability distributions obtained from model‐data fitting, to identify how much information about ecosystem parameter values can be gained from the synthetic data.…”

Where the Ecological Gaps Remain, a Modelers' Perspective

“…The use of Bayesian approaches and model ensembles to provide ranges of possible outcomes across model types is now becoming more common (e.g., Gharbia et al, 2016). The first use of model ensembles was in economics (Bates and Granger, 1969) but has now become a staple of many fieldseconomics, systematics, meteorology, and climatology-and is often now used when considering shifting species distributions (e.g., Araújo and New, 2007) or terrestrial ecosystem impacts (Baker et al, 2019). Model intercomparison projects (MIPs; e.g., Warszawski et al, 2014;Lotze et al, 2019) are also bringing together the world's modeling community to share outcomes, lessons and understanding, accelerating model improvements and rigor.…”