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Johnston ASA. Predicting emergent animal biodiversity patterns across multiple scales. GLOBAL CHANGE BIOLOGY 2024; 30:e17397. [PMID: 38984852 DOI: 10.1111/gcb.17397] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Revised: 06/05/2024] [Accepted: 06/14/2024] [Indexed: 07/11/2024]
Abstract
Restoring biodiversity-based resilience and ecosystem multi-functionality needs to be informed by more accurate predictions of animal biodiversity responses to environmental change. Ecological models make a substantial contribution to this understanding, especially when they encode the biological mechanisms and processes that give rise to emergent patterns (population, community, ecosystem properties and dynamics). Here, a distinction between 'mechanistic' and 'process-based' ecological models is established to review existing approaches. Mechanistic and process-based ecological models have made key advances to understanding the structure, function and dynamics of animal biodiversity, but are typically designed to account for specific levels of biological organisation and spatiotemporal scales. Cross-scale ecological models, which predict emergent co-occurring biodiversity patterns at interacting scales of space, time and biological organisation, is a critical next step in predictive ecology. A way forward is to first capitalise on existing models to systematically evaluate the ability of scale-explicit mechanisms and processes to predict emergent patterns at alternative scales. Such model intercomparisons will reveal mechanism to process transitions across fine to broad scales, overcome approach-specific barriers to model realism or tractability and identify gaps which necessitate the development of new fundamental principles. Key challenges surrounding model complexity and uncertainty would need to be addressed, and while opportunities from big data can streamline the integration of multiple scale-explicit biodiversity patterns, ambitious cross-scale field studies are also needed. Crucially, overcoming cross-scale ecological modelling challenges would unite disparate fields of ecology with the common goal of improving the evidence-base to safeguard biodiversity and ecosystems under novel environmental change.
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2
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Vollert SA, Drovandi C, Adams MP. Unlocking ensemble ecosystem modelling for large and complex networks. PLoS Comput Biol 2024; 20:e1011976. [PMID: 38483981 DOI: 10.1371/journal.pcbi.1011976] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Revised: 03/26/2024] [Accepted: 03/07/2024] [Indexed: 03/27/2024] Open
Abstract
The potential effects of conservation actions on threatened species can be predicted using ensemble ecosystem models by forecasting populations with and without intervention. These model ensembles commonly assume stable coexistence of species in the absence of available data. However, existing ensemble-generation methods become computationally inefficient as the size of the ecosystem network increases, preventing larger networks from being studied. We present a novel sequential Monte Carlo sampling approach for ensemble generation that is orders of magnitude faster than existing approaches. We demonstrate that the methods produce equivalent parameter inferences, model predictions, and tightly constrained parameter combinations using a novel sensitivity analysis method. For one case study, we demonstrate a speed-up from 108 days to 6 hours, while maintaining equivalent ensembles. Additionally, we demonstrate how to identify the parameter combinations that strongly drive feasibility and stability, drawing ecological insight from the ensembles. Now, for the first time, larger and more realistic networks can be practically simulated and analysed.
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Affiliation(s)
- Sarah A Vollert
- Centre for Data Science, Queensland University of Technology, Brisbane, Queensland, Australia
- School of Mathematical Sciences, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Christopher Drovandi
- Centre for Data Science, Queensland University of Technology, Brisbane, Queensland, Australia
- School of Mathematical Sciences, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Matthew P Adams
- Centre for Data Science, Queensland University of Technology, Brisbane, Queensland, Australia
- School of Mathematical Sciences, Queensland University of Technology, Brisbane, Queensland, Australia
- School of Chemical Engineering, The University of Queensland, St Lucia, Australia
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3
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Yoshida K, Hata K, Kawakami K, Hiradate S, Osawa T, Kachi N. Predicting ecosystem changes by a new model of ecosystem evolution. Sci Rep 2023; 13:15353. [PMID: 37717039 PMCID: PMC10505200 DOI: 10.1038/s41598-023-42529-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Accepted: 09/11/2023] [Indexed: 09/18/2023] Open
Abstract
In recent years, computer simulation has been increasingly used to predict changes in actual ecosystems. In these studies, snapshots of ecosystems at certain points in time were instantly constructed without considering their evolutionary histories. However, it may not be possible to correctly predict future events unless their evolutionary processes are considered. In this study, we developed a new ecosystem model for reproducing the evolutionary process on an oceanic island, targeting Nakoudojima Island of the Ogasawara Islands. This model successfully reproduced the primitive ecosystem (the entire island covered with forest) prior to the invasion of alien species. Also, by adding multiple alien species to this ecosystem, we were able to reproduce temporal changes in the ecosystem of Nakoudojima Island after invasion of alien species. Then, we performed simulations in which feral goats were eradicated, as had actually been done on the island; these suggested that after the eradication of feral goats, forests were unlikely to be restored. In the ecosystems in which forests were not restored, arboreous plants with a high growth rate colonized during the early stage of evolution. As arboreous plants with a high growth rate consume a large amount of nutrient in soil, creating an oligotrophic state. As a result, plants cannot grow, and animal species that rely on plants cannot maintain their biomass. Consequently, many animals and plants become extinct as they cannot endure disturbances by alien species, and the ecosystem loses its resilience. Therefore, even if feral goats are eradicated, forests are not restored. Thus, the founder effect from the distant past influences future ecosystem changes. Our findings show that it is useful to consider the evolutionary process of an ecosystem in predicting its future events.
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Affiliation(s)
- Katsuhiko Yoshida
- Biodiversity Division, National Institute for Environmental Studies, 16-2 Onogawa, Tsukuba, Ibaraki, 305-8506, Japan.
| | - Kenji Hata
- College of Commerce, Nihon University, 5-2-1 Kinuta, Setagaya, Tokyo, 157-8570, Japan
- Department of Biological Sciences, Graduate School of Science, Tokyo Metropolitan University, 1-1 Minami-Osawa, Hachioji, Tokyo, 192-0397, Japan
| | - Kazuto Kawakami
- Forestry and Forest Products Research Institute, 1 Matsunosato, Tsukuba, Ibaraki, 305-8687, Japan
| | - Syuntaro Hiradate
- Division of Bioproduction Environmental Sciences, Department of Agro-environmental Sciences, Faculty of Agriculture, Kyushu University, 744 Moto-Oka, Nishi-ku, Fukuoka, 819-0395, Japan
| | - Takeshi Osawa
- Department of Tourism Science, Graduate School of Urban Environmental Sciences, Tokyo Metropolitan University, 1-1 Minami-Osawa, Hachioji, Tokyo, 192-0397, Japan
| | - Naoki Kachi
- Department of Biological Sciences, Graduate School of Science, Tokyo Metropolitan University, 1-1 Minami-Osawa, Hachioji, Tokyo, 192-0397, Japan
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4
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Integrated Population Models: Achieving Their Potential. JOURNAL OF STATISTICAL THEORY AND PRACTICE 2023. [DOI: 10.1007/s42519-022-00302-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
AbstractPrecise and accurate estimates of abundance and demographic rates are primary quantities of interest within wildlife conservation and management. Such quantities provide insight into population trends over time and the associated underlying ecological drivers of the systems. This information is fundamental in managing ecosystems, assessing species conservation status and developing and implementing effective conservation policy. Observational monitoring data are typically collected on wildlife populations using an array of different survey protocols, dependent on the primary questions of interest. For each of these survey designs, a range of advanced statistical techniques have been developed which are typically well understood. However, often multiple types of data may exist for the same population under study. Analyzing each data set separately implicitly discards the common information contained in the other data sets. An alternative approach that aims to optimize the shared information contained within multiple data sets is to use a “model-based data integration” approach, or more commonly referred to as an “integrated model.” This integrated modeling approach simultaneously analyzes all the available data within a single, and robust, statistical framework. This paper provides a statistical overview of ecological integrated models, with a focus on integrated population models (IPMs) which include abundance and demographic rates as quantities of interest. Four main challenges within this area are discussed, namely model specification, computational aspects, model assessment and forecasting. This should encourage researchers to explore further and develop new practical tools to ensure that full utility can be made of IPMs for future studies.
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5
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Monsalve-Bravo GM, Lawson BAJ, Drovandi C, Burrage K, Brown KS, Baker CM, Vollert SA, Mengersen K, McDonald-Madden E, Adams MP. Analysis of sloppiness in model simulations: Unveiling parameter uncertainty when mathematical models are fitted to data. SCIENCE ADVANCES 2022; 8:eabm5952. [PMID: 36129974 PMCID: PMC9491719 DOI: 10.1126/sciadv.abm5952] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Accepted: 08/10/2022] [Indexed: 06/15/2023]
Abstract
This work introduces a comprehensive approach to assess the sensitivity of model outputs to changes in parameter values, constrained by the combination of prior beliefs and data. This approach identifies stiff parameter combinations strongly affecting the quality of the model-data fit while simultaneously revealing which of these key parameter combinations are informed primarily by the data or are also substantively influenced by the priors. We focus on the very common context in complex systems where the amount and quality of data are low compared to the number of model parameters to be collectively estimated, and showcase the benefits of this technique for applications in biochemistry, ecology, and cardiac electrophysiology. We also show how stiff parameter combinations, once identified, uncover controlling mechanisms underlying the system being modeled and inform which of the model parameters need to be prioritized in future experiments for improved parameter inference from collective model-data fitting.
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Affiliation(s)
- Gloria M. Monsalve-Bravo
- School of Earth and Environmental Sciences, The University of Queensland, St Lucia, QLD 4072, Australia
- Centre for Biodiversity and Conservation Science, The University of Queensland, St Lucia, QLD 4072, Australia
- School of Chemical Engineering, The University of Queensland, St Lucia, QLD 4072, Australia
| | - Brodie A. J. Lawson
- Centre for Data Science, Queensland University of Technology, Brisbane, QLD 4001, Australia
- ARC Centre of Excellence for Mathematical and Statistical Frontiers, Queensland University of Technology, Brisbane, QLD 4001, Australia
- School of Mathematical Sciences, Queensland University of Technology, Brisbane, QLD 4001, Australia
- ARC Centre of Excellence for Plant Success in Nature and Agriculture, Queensland University of Technology, Brisbane, QLD 4001, Australia
| | - Christopher Drovandi
- Centre for Data Science, Queensland University of Technology, Brisbane, QLD 4001, Australia
- ARC Centre of Excellence for Mathematical and Statistical Frontiers, Queensland University of Technology, Brisbane, QLD 4001, Australia
- School of Mathematical Sciences, Queensland University of Technology, Brisbane, QLD 4001, Australia
| | - Kevin Burrage
- ARC Centre of Excellence for Mathematical and Statistical Frontiers, Queensland University of Technology, Brisbane, QLD 4001, Australia
- School of Mathematical Sciences, Queensland University of Technology, Brisbane, QLD 4001, Australia
- ARC Centre of Excellence for Plant Success in Nature and Agriculture, Queensland University of Technology, Brisbane, QLD 4001, Australia
- Department of Computer Science, University of Oxford, Oxford OX1 3QD, UK
| | - Kevin S. Brown
- Department of Pharmaceutical Sciences, Oregon State University, Corvallis, OR 97331, USA
- Department of Chemical, Biological, & Environmental Engineering, Oregon State University, Corvallis, OR 97331, USA
| | - Christopher M. Baker
- School of Mathematics and Statistics, The University of Melbourne, Parkville, VIC 3010, Australia
- Melbourne Centre for Data Science, The University of Melbourne, Parkville, VIC 3010, Australia
- Centre of Excellence for Biosecurity Risk Analysis, The University of Melbourne, Parkville, VIC 3010, Australia
| | - Sarah A. Vollert
- Centre for Data Science, Queensland University of Technology, Brisbane, QLD 4001, Australia
- ARC Centre of Excellence for Mathematical and Statistical Frontiers, Queensland University of Technology, Brisbane, QLD 4001, Australia
- School of Mathematical Sciences, Queensland University of Technology, Brisbane, QLD 4001, Australia
| | - Kerrie Mengersen
- Centre for Data Science, Queensland University of Technology, Brisbane, QLD 4001, Australia
- ARC Centre of Excellence for Mathematical and Statistical Frontiers, Queensland University of Technology, Brisbane, QLD 4001, Australia
- School of Mathematical Sciences, Queensland University of Technology, Brisbane, QLD 4001, Australia
| | - Eve McDonald-Madden
- School of Earth and Environmental Sciences, The University of Queensland, St Lucia, QLD 4072, Australia
- Centre for Biodiversity and Conservation Science, The University of Queensland, St Lucia, QLD 4072, Australia
| | - Matthew P. Adams
- School of Chemical Engineering, The University of Queensland, St Lucia, QLD 4072, Australia
- Centre for Data Science, Queensland University of Technology, Brisbane, QLD 4001, Australia
- ARC Centre of Excellence for Mathematical and Statistical Frontiers, Queensland University of Technology, Brisbane, QLD 4001, Australia
- School of Mathematical Sciences, Queensland University of Technology, Brisbane, QLD 4001, Australia
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6
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Clark‐Wolf TJ, Hahn PG, Brelsford E, Francois J, Hayes N, Larkin B, Ramsey P, Pearson DE. Preventing a series of unfortunate events: using qualitative models to improve conservation. J Appl Ecol 2022. [DOI: 10.1111/1365-2664.14231] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Affiliation(s)
- T. J. Clark‐Wolf
- Wildlife Biology Program, Department of Ecosystem and Conservation Sciences, W.A. Franke College of Forestry and Conservation University of Montana Missoula, MT 59812 USA
- Current Address: Center for Ecosystem Sentinels, Department of Biology University of Washington Seattle, WA 98195 USA
| | - Philip G. Hahn
- Department of Entomology and Nematology University of Florida Gainesville, FL 32608 USA
| | - Eric Brelsford
- Stamen, 2017 Mission St Suite 300 San Francisco, CA 94110 USA
| | - Jaleen Francois
- Stamen, 2017 Mission St Suite 300 San Francisco, CA 94110 USA
| | - Nicolette Hayes
- Stamen, 2017 Mission St Suite 300 San Francisco, CA 94110 USA
| | - Beau Larkin
- MPG Ranch, 19400 Lower Woodchuck Road Florence, MT 59833 USA
| | - Philip Ramsey
- MPG Ranch, 19400 Lower Woodchuck Road Florence, MT 59833 USA
| | - Dean E. Pearson
- Rocky Mountain Research Station, U.S. Department of Agriculture Forest Service Missoula, MT 59801 USA
- Division of Biological Sciences University of Montana Missoula, MT 59812 USA
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7
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Peterson KA, Barnes MD, Jeynes‐Smith C, Cowen S, Gibson L, Sims C, Baker CM, Bode M. Reconstructing lost ecosystems: A risk analysis framework for planning multispecies reintroductions under severe uncertainty. J Appl Ecol 2021. [DOI: 10.1111/1365-2664.13965] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Affiliation(s)
- Katie A. Peterson
- School of Mathematical Sciences Queensland University of Technology Brisbane Qld Australia
- ARC Centre of Excellence for Coral Reef Studies James Cook University Townsville Qld Australia
- National Socio‐Environmental Synthesis Center University of Maryland Annapolis MD USA
| | - Megan D. Barnes
- Biodiversity and Conservation Science Western Australian Department of Biodiversity, Conservation and Attractions Perth WA Australia
| | - Cailan Jeynes‐Smith
- School of Mathematical Sciences Queensland University of Technology Brisbane Qld Australia
| | - Saul Cowen
- Biodiversity and Conservation Science Western Australian Department of Biodiversity, Conservation and Attractions Perth WA Australia
- School of Biological Sciences The University of Western Australia Perth WA Australia
| | - Lesley Gibson
- Biodiversity and Conservation Science Western Australian Department of Biodiversity, Conservation and Attractions Perth WA Australia
- School of Biological Sciences The University of Western Australia Perth WA Australia
| | - Colleen Sims
- Biodiversity and Conservation Science Western Australian Department of Biodiversity, Conservation and Attractions Perth WA Australia
| | - Christopher M. Baker
- School of Mathematics and Statistics The University of Melbourne Melbourne Vic. Australia
- Melbourne Centre for Data Science The University of Melbourne Melbourne Vic. Australia
- Centre of Excellence for Biosecurity Risk Analysis The University of Melbourne Melbourne Vic. Australia
| | - Michael Bode
- School of Mathematical Sciences Queensland University of Technology Brisbane Qld Australia
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8
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Rendall AR, Sutherland DR, Baker CM, Raymond B, Cooke R, White JG. Managing ecosystems in a sea of uncertainty: invasive species management and assisted colonizations. ECOLOGICAL APPLICATIONS : A PUBLICATION OF THE ECOLOGICAL SOCIETY OF AMERICA 2021; 31:e02306. [PMID: 33595860 DOI: 10.1002/eap.2306] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/10/2020] [Revised: 10/22/2020] [Accepted: 11/13/2020] [Indexed: 06/12/2023]
Abstract
Managing ecosystems in the face of complex species interactions, and the associated uncertainty, presents a considerable ecological challenge. Altering those interactions via actions such as invasive species management or conservation translocations can result in unintended consequences, supporting the need to be able to make more informed decisions in the face of this uncertainty. We demonstrate the utility of ecosystem models to reduce uncertainty and inform future ecosystem management. We use Phillip Island, Australia, as a case study to investigate the impacts of two invasive species management options and consider whether a critically endangered mammal is likely to establish a population in the presence of invasive species. Qualitative models are used to determine the effects of apex predator removal (feral cats) and invasive prey removal (rabbits, rats, and mice). We extend this approach using Ensemble Ecosystem Models to consider how suppression, rather than eradication influences the species community; and consider whether an introduction of the critically endangered eastern barred bandicoot is likely to be successful in the presence of invasive species. Our analysis revealed the potential for unintended outcomes associated with feral cat control operations, with rats and rabbits expected to increase in abundance. A strategy based on managing prey species appeared to have the most ecosystem-wide benefits, with rodent control showing more favorable responses than a rabbit control strategy. Eastern barred bandicoots were predicted to persist under all feral cat control levels (including no control). Managing ecosystems is a complex and imprecise process. However, qualitative modeling and ensemble ecosystem modeling address uncertainty and are capable of improving and optimizing management practices. Our analysis shows that the best conservation outcomes may not always be associated with the top-down control of apex predators, and land managers should think more broadly in relation to managing bottom-up processes as well. Challenges faced in continuing to conserve biodiversity mean new, bolder, conservation actions are needed. We suggest that endangered species are capable of surviving in the presence of feral cats, potentially opening the door for more conservation translocations.
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Affiliation(s)
- Anthony R Rendall
- School of Life and Environmental Sciences, Deakin University, Geelong, Victoria, 3220, Australia
- Centre for Integrative Ecology, School of Life and Environmental Sciences, Faculty of Science, Engineering and the Built Environment, Burwood Campus, Burwood, Victoria, 3125, Australia
| | - Duncan R Sutherland
- Conservation Department, Phillip Island Nature Parks, Cowes, Victoria, 3922, Australia
| | - Christopher M Baker
- School of Mathematics and Statistics, The University of Melbourne, Melbourne, Victoria, 3010, Australia
- Melbourne Centre for Data Science, The University of Melbourne, Melbourne, Victoria, 3010, Australia
- Centre of Excellence for Biosecurity Risk Analysis, The University of Melbourne, Melbourne, Victoria, 3010, Australia
| | - Ben Raymond
- Australian Antarctic Division, Department of Agriculture, Water and the Environment, Kingston, Tasmania, 7050, Australia
- Institute for Marine and Antarctic Studies, University of Tasmania, Hobart, Tasmania, 7000, Australia
| | - Raylene Cooke
- School of Life and Environmental Sciences, Deakin University, Geelong, Victoria, 3220, Australia
| | - John G White
- School of Life and Environmental Sciences, Deakin University, Geelong, Victoria, 3220, Australia
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9
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Peterson K, Bode M. Using ensemble modeling to predict the impacts of assisted migration on recipient ecosystems. CONSERVATION BIOLOGY : THE JOURNAL OF THE SOCIETY FOR CONSERVATION BIOLOGY 2021; 35:678-687. [PMID: 32538472 DOI: 10.1111/cobi.13571] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/12/2019] [Revised: 06/01/2020] [Accepted: 06/05/2020] [Indexed: 06/11/2023]
Abstract
Assisted migration is a controversial conservation measure that aims to protect threatened species by moving part of their population outside its natural range. Although this could save species from extinction, it also introduces a range of risks. The magnitude of the threat to recipient ecosystems has not been investigated quantitatively, despite being the most common criticism leveled at the action. We used an ensemble modeling framework to estimate the risks of assisted migration to existing species within ecosystems. With this approach, we calculated the consequences of an assisted migration project across a very large combination of translocated species and recipient ecosystems. We predicted the probability of a successful assisted migration and the number of local extinctions would result from establishment of the translocated species. Using an ensemble of 1.5×106 simulated 15-species recipient ecosystems, we estimated that translocated species will successfully establish in 83% of cases if introduced to stable, high-quality habitats. However, assisted migration projects were estimated to cause an average of 0.6 extinctions and 5% of successful translocations triggered 4 or more local extinctions. Quantifying the impacts to species within recipient ecosystems is critical to help managers weigh the benefits and negative consequences of assisted migration.
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Affiliation(s)
- Katie Peterson
- ARC Centre of Excellence for Coral Reef Studies, Sir George Fisher Research Building, James Cook University, 1 James Cook Drive, Douglas, QLD, 4814, Australia
| | - Michael Bode
- School of Mathematical Sciences, Queensland University of Technology, 2 George Street, Brisbane, QLD, 4000, Australia
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10
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Haller‐Bull V, Bode M. Modeling herbivore functional responses causing boom-bust dynamics following predator removal. Ecol Evol 2021; 11:2209-2220. [PMID: 33717449 PMCID: PMC7920789 DOI: 10.1002/ece3.7185] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2020] [Revised: 11/10/2020] [Accepted: 12/16/2020] [Indexed: 11/07/2022] Open
Abstract
Native biodiversity is threatened by invasive species in many terrestrial and marine systems, and conservation managers have demonstrated successes by responding with eradication or control programs. Although invasive species are often the direct cause of threat to native species, ecosystems can react in unexpected ways to their removal or reduction. Here, we use theoretical models to predict boom-bust dynamics, where the removal of predatory or competitive pressure from a native herbivore results in oscillatory population dynamics (boom-bust), which can endanger the native species' population in the short term. We simulate control activities, applied to multiple theoretical three-species Lotka-Volterra ecosystem models consisting of vegetation, a native herbivore, and an invasive predator. Based on these communities, we then develop a predictive tool that-based on relative parameter values-predicts whether control efforts directed at the invasive predator will lead to herbivore release followed by a crash. Further, by investigating the different functional responses, we show that model structure, as well as model parameters, are important determinants of conservation outcomes. Finally, control strategies that can mitigate these negative consequences are identified. Managers working in similar data-poor ecosystems can use the predictive tool to assess the probability that their system will exhibit boom-bust dynamics, without knowing exact community parameter values.
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Affiliation(s)
- Vanessa Haller‐Bull
- School of Mathematical SciencesQueensland University of TechnologyBrisbaneQldAustralia
- ACEMS, Australian Research Council Centre of Excellence for Mathematical and Statistical FrontiersBrisbaneQldAustralia
- AIMS@JCUAustralian Institute of Marine ScienceTownsvilleQldAustralia
| | - Michael Bode
- School of Mathematical SciencesQueensland University of TechnologyBrisbaneQldAustralia
- ACEMS, Australian Research Council Centre of Excellence for Mathematical and Statistical FrontiersBrisbaneQldAustralia
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11
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Robinson NM, Blanchard W, MacGregor C, Brewster R, Dexter N, Lindenmayer DB. Finding food in a novel environment: The diet of a reintroduced endangered meso-predator to mainland Australia, with notes on foraging behaviour. PLoS One 2020; 15:e0243937. [PMID: 33332425 PMCID: PMC7746155 DOI: 10.1371/journal.pone.0243937] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2020] [Accepted: 11/30/2020] [Indexed: 12/01/2022] Open
Abstract
Translocated captive-bred predators are less skilled at hunting than wild-born predators and more prone to starvation post-release. Foraging in an unfamiliar environment presents many further risks to translocated animals. Knowledge of the diet and foraging behaviour of translocated animals is therefore an important consideration of reintroductions. We investigated the diet of the endangered meso-predator, the eastern quoll Dasyurus viverrinus. We also opportunistically observed foraging behaviour, enabling us to examine risks associated with foraging. Sixty captive-bred eastern quolls were reintroduced to an unfenced reserve on mainland Australia (where introduced predators are managed) over a two year period (2018, 2019). Quolls were supplementary fed macropod meat but were also able to forage freely. Dietary analysis of scats (n = 56) revealed that quolls ate macropods, small mammals, birds, invertebrates, fish, reptiles and frogs, with some between-year differences in the frequency of different diet categories. We also observed quolls hunting live prey. Quolls utilised supplementary feeding stations, indicating that this may be an important strategy during the establishment phase. Our study demonstrated that, in a novel environment, captive-bred quolls were able to locate food and hunt live prey. However, foraging was not without risks; with the ingestion of toxic substances and foraging in dangerous environments found to be potentially harmful. Knowledge of the diet of reintroduced fauna in natural landscapes is important for understanding foraging behaviour and evaluating habitat suitability for future translocations and management.
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Affiliation(s)
- Natasha M. Robinson
- Fenner School of Environment and Society, The Australian National University, Canberra, Australian Capital Territory, Australia
- National Environmental Science Program, Threatened Species Recovery Hub, Fenner School of Environment and Society, The Australian National University, Canberra, Australian Capital Territory, Australia
- * E-mail:
| | - Wade Blanchard
- Fenner School of Environment and Society, The Australian National University, Canberra, Australian Capital Territory, Australia
| | - Christopher MacGregor
- Fenner School of Environment and Society, The Australian National University, Canberra, Australian Capital Territory, Australia
- National Environmental Science Program, Threatened Species Recovery Hub, Fenner School of Environment and Society, The Australian National University, Canberra, Australian Capital Territory, Australia
| | - Rob Brewster
- Rewilding Australia, Sydney, New South Wales, Australia
| | - Nick Dexter
- Booderee National Park, Jervis Bay, Jervis Bay Territory, Australia
| | - David B. Lindenmayer
- Fenner School of Environment and Society, The Australian National University, Canberra, Australian Capital Territory, Australia
- National Environmental Science Program, Threatened Species Recovery Hub, Fenner School of Environment and Society, The Australian National University, Canberra, Australian Capital Territory, Australia
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12
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Baker CM, Bode M. Recent advances of quantitative modeling to support invasive species eradication on islands. CONSERVATION SCIENCE AND PRACTICE 2020. [DOI: 10.1111/csp2.246] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022] Open
Affiliation(s)
- Christopher M. Baker
- School of Mathematics and Statistics, The University of Melbourne Melbourne Victoria Australia
- Melbourne Centre for Data Science, The University of Melbourne Melbourne Victoria Australia
- Centre of Excellence for Biosecurity Risk Analysis The University of Melbourne Melbourne Victoria Australia
| | - Michael Bode
- School of Mathematical Sciences, Queensland University of Technology Brisbane Queensland Australia
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13
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A guide to ecosystem models and their environmental applications. Nat Ecol Evol 2020; 4:1459-1471. [PMID: 32929239 DOI: 10.1038/s41559-020-01298-8] [Citation(s) in RCA: 45] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2019] [Accepted: 08/04/2020] [Indexed: 12/12/2022]
Abstract
Applied ecology has traditionally approached management problems through a simplified, single-species lens. Repeated failures of single-species management have led us to a new paradigm - managing at the ecosystem level. Ecosystem management involves a complex array of interacting organisms, processes and scientific disciplines. Accounting for interactions, feedback loops and dependencies between ecosystem components is therefore fundamental to understanding and managing ecosystems. We provide an overview of the main types of ecosystem models and their uses, and discuss challenges related to modelling complex ecological systems. Existing modelling approaches typically attempt to do one or more of the following: describe and disentangle ecosystem components and interactions; make predictions about future ecosystem states; and inform decision making by comparing alternative strategies and identifying important uncertainties. Modelling ecosystems is challenging, particularly when balancing the desire to represent many components of an ecosystem with the limitations of available data and the modelling objective. Explicitly considering different forms of uncertainty is therefore a primary concern. We provide some recommended strategies (such as ensemble ecosystem models and multi-model approaches) to aid the explicit consideration of uncertainty while also meeting the challenges of modelling ecosystems.
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14
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Sharp JA, Browning AP, Mapder T, Baker CM, Burrage K, Simpson MJ. Designing combination therapies using multiple optimal controls. J Theor Biol 2020; 497:110277. [PMID: 32294472 DOI: 10.1016/j.jtbi.2020.110277] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2019] [Revised: 02/21/2020] [Accepted: 04/06/2020] [Indexed: 01/31/2023]
Abstract
Strategic management of populations of interacting biological species routinely requires interventions combining multiple treatments or therapies. This is important in key research areas such as ecology, epidemiology, wound healing and oncology. Despite the well developed theory and techniques for determining single optimal controls, there is limited practical guidance supporting implementation of combination therapies. In this work we use optimal control theory to calculate optimal strategies for applying combination therapies to a model of acute myeloid leukaemia. We present a versatile framework to systematically explore the trade-offs that arise in designing combination therapy protocols using optimal control. We consider various combinations of continuous and bang-bang (discrete) controls, and we investigate how the control dynamics interact and respond to changes in the weighting and form of the pay-off characterising optimality. We demonstrate that the optimal controls respond non-linearly to treatment strength and control parameters, due to the interactions between species. We discuss challenges in appropriately characterising optimality in a multiple control setting and provide practical guidance for applying multiple optimal controls. Code used in this work to implement multiple optimal controls is available on GitHub.
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Affiliation(s)
- Jesse A Sharp
- School of Mathematical Sciences, Queensland University of Technology (QUT), Australia; ARC Centre of Excellence for Mathematical and Statistical Frontiers, QUT, Australia.
| | - Alexander P Browning
- School of Mathematical Sciences, Queensland University of Technology (QUT), Australia; ARC Centre of Excellence for Mathematical and Statistical Frontiers, QUT, Australia
| | - Tarunendu Mapder
- School of Mathematical Sciences, Queensland University of Technology (QUT), Australia; ARC Centre of Excellence for Mathematical and Statistical Frontiers, QUT, Australia
| | - Christopher M Baker
- School of Mathematical Sciences, Queensland University of Technology (QUT), Australia; ARC Centre of Excellence for Mathematical and Statistical Frontiers, QUT, Australia; School of Mathematics and Statistics, The University of Melbourne, Australia
| | - Kevin Burrage
- School of Mathematical Sciences, Queensland University of Technology (QUT), Australia; ARC Centre of Excellence for Mathematical and Statistical Frontiers, QUT, Australia; Department of Computer Science, University of Oxford, UK (Visiting Professor)
| | - Matthew J Simpson
- School of Mathematical Sciences, Queensland University of Technology (QUT), Australia
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15
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Ollivier M, Lesieur V, Raghu S, Martin JF. Characterizing ecological interaction networks to support risk assessment in classical biological control of weeds. CURRENT OPINION IN INSECT SCIENCE 2020; 38:40-47. [PMID: 32088650 DOI: 10.1016/j.cois.2019.12.002] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/01/2019] [Revised: 12/04/2019] [Accepted: 12/14/2019] [Indexed: 06/10/2023]
Abstract
A key element in weed biological control is the selection of a biological control agent that minimizes the risks of non-target attack and indirect effects on the recipient community. Network ecology is a promising approach that could help decipher tritrophic interactions in both the native and the invaded ranges, to complement quarantine-based host-specificity tests and gain insights on potential interactions of biological control agents. This review highlights practical questions addressed by networks, including 1) biological control agent selection, based on specialization indices, 2) risk assessment of biological control agent release into a novel environment, via particular patterns of association such as apparent competition between agent(s) and native herbivore(s), 3) network comparisons through structural metrics, 4) potential of network modelling and 5) limits of network construction methods.
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Affiliation(s)
- Melodie Ollivier
- CBGP, Montpellier SupAgro, INRAE, CIRAD, IRD, Univ Montpellier, Montpellier, France.
| | - Vincent Lesieur
- CBGP, Montpellier SupAgro, INRAE, CIRAD, IRD, Univ Montpellier, Montpellier, France; CSIRO Health and Biosecurity, European Laboratory, Montferrier sur Lez, 34980, France
| | | | - Jean-François Martin
- CBGP, Montpellier SupAgro, INRAE, CIRAD, IRD, Univ Montpellier, Montpellier, France
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16
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Adams MP, Sisson SA, Helmstedt KJ, Baker CM, Holden MH, Plein M, Holloway J, Mengersen KL, McDonald-Madden E. Informing management decisions for ecological networks, using dynamic models calibrated to noisy time-series data. Ecol Lett 2020; 23:607-619. [PMID: 31989772 DOI: 10.1111/ele.13465] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2019] [Revised: 06/13/2019] [Accepted: 12/27/2019] [Indexed: 12/25/2022]
Abstract
Well-intentioned environmental management can backfire, causing unforeseen damage. To avoid this, managers and ecologists seek accurate predictions of the ecosystem-wide impacts of interventions, given small and imprecise datasets, which is an incredibly difficult task. We generated and analysed thousands of ecosystem population time series to investigate whether fitted models can aid decision-makers to select interventions. Using these time-series data (sparse and noisy datasets drawn from deterministic Lotka-Volterra systems with two to nine species, of known network structure), dynamic model forecasts of whether a species' future population will be positively or negatively affected by rapid eradication of another species were correct > 70% of the time. Although 70% correct classifications is only slightly better than an uninformative prediction (50%), this classification accuracy can be feasibly improved by increasing monitoring accuracy and frequency. Our findings suggest that models may not need to produce well-constrained predictions before they can inform decisions that improve environmental outcomes.
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Affiliation(s)
- Matthew P Adams
- School of Earth and Environmental Sciences, The University of Queensland, St Lucia, Qld, 4072, Australia.,Centre for Biodiversity and Conservation Science, The University of Queensland, St Lucia, Qld, 4072, Australia.,ARC Centre of Excellence for Mathematical and Statistical Frontiers, The University of Queensland, St Lucia, Qld, 4072, Australia
| | - Scott A Sisson
- School of Mathematics and Statistics, The University of New South Wales, Sydney, NSW, 2052, Australia
| | - Kate J Helmstedt
- School of Mathematical Sciences, Queensland University of Technology, Brisbane, Qld, 4001, Australia.,ARC Centre of Excellence for Mathematical and Statistical Frontiers, Queensland University of Technology, Brisbane, Qld, 4001, Australia
| | - Christopher M Baker
- Centre for Biodiversity and Conservation Science, The University of Queensland, St Lucia, Qld, 4072, Australia.,School of Mathematical Sciences, Queensland University of Technology, Brisbane, Qld, 4001, Australia.,School of Biological Sciences, The University of Queensland, St Lucia, Qld, 4072, Australia.,CSIRO Ecosystem Sciences, Ecosciences Precinct, Dutton Park, Qld, 4102, Australia.,Centre of Excellence for Environmental Decisions, The University of Queensland, St Lucia, Qld, 4072, Australia
| | - Matthew H Holden
- Centre for Biodiversity and Conservation Science, The University of Queensland, St Lucia, Qld, 4072, Australia.,School of Biological Sciences, The University of Queensland, St Lucia, Qld, 4072, Australia.,Centre of Excellence for Environmental Decisions, The University of Queensland, St Lucia, Qld, 4072, Australia.,Centre for Applications in Natural Resource Mathematics, School of Mathematics and Physics, The University of Queensland, St Lucia, Qld, 4072, Australia
| | - Michaela Plein
- School of Earth and Environmental Sciences, The University of Queensland, St Lucia, Qld, 4072, Australia.,Centre for Biodiversity and Conservation Science, The University of Queensland, St Lucia, Qld, 4072, Australia.,Administration de la Nature et des Forêts, 6, rue de la Gare, 6731, Grevenmacher, Luxembourg
| | - Jacinta Holloway
- School of Mathematical Sciences, Queensland University of Technology, Brisbane, Qld, 4001, Australia.,ARC Centre of Excellence for Mathematical and Statistical Frontiers, Queensland University of Technology, Brisbane, Qld, 4001, Australia
| | - Kerrie L Mengersen
- School of Mathematical Sciences, Queensland University of Technology, Brisbane, Qld, 4001, Australia.,ARC Centre of Excellence for Mathematical and Statistical Frontiers, Queensland University of Technology, Brisbane, Qld, 4001, Australia
| | - Eve McDonald-Madden
- School of Earth and Environmental Sciences, The University of Queensland, St Lucia, Qld, 4072, Australia.,Centre for Biodiversity and Conservation Science, The University of Queensland, St Lucia, Qld, 4072, Australia.,Centre of Excellence for Environmental Decisions, The University of Queensland, St Lucia, Qld, 4072, Australia
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17
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Baker CM, Plein M, Shaikh R, Bode M. Simultaneous invasive alien predator eradication delivers the best outcomes for protected island species. Biol Invasions 2019. [DOI: 10.1007/s10530-019-02161-z] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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18
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Fulton EA, Blanchard JL, Melbourne-Thomas J, Plagányi ÉE, Tulloch VJD. Where the Ecological Gaps Remain, a Modelers' Perspective. Front Ecol Evol 2019. [DOI: 10.3389/fevo.2019.00424] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
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