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729 results on '"Wintle, Brendan, A."'

2. Ecological factors influencing invasive predator survival and movement: insights from a continental-scale study of feral cats in Australia

Author

Menon, Vishnu, McGregor, Hugh, Giljohann, Kate, Wintle, Brendan, Pascoe, Jack, Robley, Alan, Johnston, Michael, Fancourt, Bronwyn, Bengsen, Andrew, Buckmaster, Tony, Comer, Sarah, Hamer, Rowena, Friend, Tony, Jansen, Jeroen, Zewe, Frances, Fleming, Peter, Ballard, Guy, Moseby, Katherine, Gentle, Matthew, Scomparin, Cyril, Speed, James, Clausen, Lucy, Le Pla, Mark, and Hradsky, Bronwyn

Published
2024
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4. Correction: Ecological factors influencing invasive predator survival and movement: insights from a continental-scale study of feral cats in Australia

Author

Menon, Vishnu, McGregor, Hugh, Giljohann, Kate, Wintle, Brendan, Pascoe, Jack, Robley, Alan, Johnston, Michael, Fancourt, Bronwyn, Bengsen, Andrew, Buckmaster, Tony, Comer, Sarah, Hamer, Rowena, Friend, Tony, Jansen, Jeroen, Zewe, Frances, Fleming, Peter, Ballard, Guy, Moseby, Katherine, Gentle, Matthew, Scomparin, Cyril, Speed, James, Clausen, Lucy, Le Pla, Mark, and Hradsky, Bronwyn

Published
2024
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6. Predicting the ecological outcomes of global consumption

Author

Bal, Payal, Kapitza, Simon, Cadenhead, Natasha, Kompas, Tom, Van Ha, Pham, and Wintle, Brendan

Subjects
Quantitative Biology - Populations and Evolution, Quantitative Biology - Quantitative Methods
Abstract

Mapping pathways to achieving the sustainable development goals requires understanding and predicting how social, economic and political factors impact biodiversity. Trends in demography, economic growth, regional alliances and consumption behaviours can have profound effects on the environment by driving resource use and production. While these distant socio-economic drivers impact species and ecosystems at global scales, for example by driving greenhouse gas emissions and climate change, the most prevalent human impacts on biodiversity manifest through habitat loss and land use change decisions at finer scales. We provide the first integrated ecological-economic analysis pathway capable of supporting both national policy design challenges and global scale assessment of biodiversity risks posed by socio-economic drivers such as population growth, consumption and trade. To achieve this, we provide state-of-the-art integration of economic, land use, and biodiversity modelling, and illustrate its application using two case studies. We evaluate the national-level implications of change in trading conditions under a multi-lateral free trade agreement for the bird biodiversity of Vietnam. We review the implications for land-use and biodiversity under coupled socio-economic (Shared Socioeconomic Pathways) and climate (Resource Concentration Pathways) scenarios for Australia. Our study provides a roadmap for setting up high dimensional integrated analyses foe evaluating global priorities for protecting nature and livelihoods in vulnerable areas with the greatest conflicts for economic, social and environmental opportunities., Comment: 40 pages, 7 figures

Published
2020

7. Assessing biophysical and socio-economic impacts of climate change on avian biodiversity

Author

Kapitza, Simon, Van Ha, Pham, Kompas, Tom, Golding, Nick, Cadenhead, Natasha C. R., Bal, Payal, and Wintle, Brendan A.

Subjects
Quantitative Biology - Populations and Evolution, Quantitative Biology - Quantitative Methods
Abstract

Climate change threatens biodiversity directly by influencing biophysical variables that drive species' geographic distributions and indirectly through socio-economic changes that influence land use patterns, driven by global consumption, production and climate. To date, no detailed analyses have been produced that assess the relative importance of, or interaction between, these direct and indirect climate change impacts on biodiversity at large scales. Here, we apply a new integrated modelling framework to quantify the relative influence of biophysical and socio-economically mediated impacts on avian species in Vietnam and Australia. We find that socio-economically mediated impacts on suitable ranges are largely outweighed by biophysical impacts, but global shifts of production are likely to result in adverse impacts on habitats worldwide. By translating economic futures and shocks into spatially explicit predictions of biodiversity change, we now have the power to analyse in a consistent way outcomes for nature and people of any change to policy, regulation, trading conditions or consumption trend at any scale from sub-national to global., Comment: 4 figures, 30 pages double-spaced

Published
2020

10. Threat-abatement framework confirms habitat retention and invasive species management are critical to conserve Australia's threatened species

Author

Kearney, Stephen G., Watson, James E.M., Reside, April E., Fisher, Diana O., Maron, Martine, Doherty, Tim S., Legge, Sarah M., Woinarski, John C.Z., Garnett, Stephen T., Wintle, Brendan A., Ritchie, Euan G., Driscoll, Don A., Lindenmayer, David, Adams, Vanessa M., Ward, Michelle S., and Carwardine, Josie

Published
2023
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17. Yes, nature is complex. But saving our precious environment means finding ways to measure it.

Author

Wintle, Brendan, Bekessy, Sarah, O'Connor, Simon, and Geary, William

Subjects
GREENHOUSE gases, WORLD Heritage Sites, ENDANGERED species, POLLINATION by bees, TREE farms, FOOD tourism
Abstract

The article discusses the importance of measuring and understanding the impact of businesses on nature. It highlights that nature loss directly threatens the global economy and emphasizes the need for businesses to consider nature in their decision-making processes. The article explains that measuring the environmental impact of a business is essential and provides guidelines and tools to help businesses assess their impacts on nature. It also emphasizes the importance of collaboration between business and nature experts and the need for accessible data platforms and training programs. Overall, the article encourages businesses to become part of the solution to the nature crisis by measuring and addressing their impact on nature. [Extracted from the article]

Published
2024

18. Covariate influence in spatially autocorrelated occupancy and abundance data

Author

Bardos, David C., Guillera-Arroita, Gurutzeta, and Wintle, Brendan A.

Subjects
Quantitative Biology - Quantitative Methods, Statistics - Methodology, 62H11
Abstract

The autologistic model and related auto-models, commonly applied as autocovariate regression, offer distinct advantages for analysing spatially autocorrelated ecological data. However, comparative studies by Carl and K\"uhn (Ecol. Model., 2007, 207, 159), Dormann (Ecol. Model., 2007, 207, 234), Dormann et al. (Ecography, 2007, 30, 609) and Beale et al. (Ecol. Lett., 2010, 13, 246) concluded that autocovariate regression yields anomalous covariate parameter estimates. The last three studies were based on erroneous numerical evidence, due to violation of conditions (Besag, J. R. Stat. Soc., Ser. B, 1974, 36, 192) for auto-model validity. Here we show that after correcting these technical errors, a more fundamental conceptual error remains: the comparative studies are founded on a mathematically incorrect notion of bias, involving direct comparison of parameter estimates across models differing in mathematical structure. We develop a set of simulation-based measures of covariate influence that are directly comparable across models and apply them to examples from the abovementioned studies. We find that in these cases, the effect of auto-model parameters is similar to (and consistent with) corresponding linear model effects, due to a phenomenon within auto-models that we refer to as "covariate amplification". Thus, simple comparison of parameter magnitudes between structurally different models can be highly misleading. We demonstrate that the recent critique of auto-models is entirely unfounded. Correctly applied and interpreted, autocovariate regression provides a practical approach to inference for spatially autocorrelated species distribution or abundance data, while overcoming well-known limitations of generalized linear models., Comment: References updated

Published
2015

19. Valid auto-models for spatially autocorrelated occupancy and abundance data

Author

Bardos, David C., Guillera-Arroita, Gurutzeta, and Wintle, Brendan A.

Subjects
Quantitative Biology - Quantitative Methods, 62H11
Abstract

Auto-logistic and related auto-models, implemented approximately as autocovariate regression, provide simple and direct modelling of spatial dependence. The autologistic model has been widely applied in ecology since Augustin, Mugglestone and Buckland (J. Appl. Ecol., 1996, 33, 339) analysed red deer census data using a hybrid estimation approach, combining maximum pseudo-likelihood estimation with Gibbs sampling of missing data. However Dormann (Ecol. Model., 2007, 207, 234) questioned the validity of auto-logistic regression, giving examples of apparent underestimation of covariate parameters in analysis of simulated "snouter" data. Dormann et al. (Ecography, 2007, 30, 609) extended this analysis to auto-Poisson and auto-normal models, reporting similar anomalies. All the above studies employ neighbourhood weighting schemes inconsistent with conditions (Besag, J. R. Stat. Soc., Ser. B, 1974, 36, 192) required for auto-model validity; furthermore the auto-Poisson analysis fails to exclude cooperative interactions. We show that all "snouter" anomalies are resolved by correct auto-model implementation. Re-analysis of the red deer data shows that invalid neighbourhood weightings generate only small estimation errors for the full dataset, but larger errors occur on geographic subsamples. A substantial fraction of papers applying auto-logistic regression to ecological data use these invalid weightings, which are default options in the widely used "spdep" spatial dependence package for R. Auto-logistic analyses using invalid neighbourhood weightings will be erroneous to an extent that can vary widely. These analyses can easily be corrected by using valid neighbourhood weightings available in "spdep". The hybrid estimation approach for missing data is readily adapted for valid neighbourhood weighting schemes and is implemented here in R for application to sparse presence-absence data., Comment: Typos corrected in Table 1. Note that defaults in R package 'spdep' have changed in response to this paper; some results using defaults are therefore now version-dependent

Published
2015

20. Dealing with the risk of fire in carbon sequestration strategies: Diverse forests or plantation monocultures?

Author

Smith, Warwick J. S., Bekessy, Sarah A., Ward, Michelle, and Wintle, Brendan A.

Subjects
CARBON sequestration, EXTREME weather, CLIMATE change, CARBON pricing, ANIMAL populations
Abstract

Climate and land‐use change pose unprecedented threats to ecosystems, economies, and communities worldwide. To help mitigate the climate crisis, restoration is a rapidly growing industry used to offset carbon emissions. The most common approach is to plant fast‐growing monocultures with the aim of sequestering as much carbon as possible in the shortest time. However, there has been little economic analysis of planting options that explicitly address short and long‐term ecological risks such as fire, disease, and environmental change. Here we develop a method for quantifying ecological risks from fire to sequestration investments and show how these risks can be factored into an analysis of long‐term financial returns relative to opportunity costs. In the case study presented, we find that the apparent advantage of fast‐growing monoculture plantations is likely to be outweighed by the long‐term fire risks to the carbon stored in them. Our analytical framework provides a widely applicable approach to comparing planting options against each other and other land uses, considering key uncertainties. With climate change already manifesting through extreme weather events, rising sea levels, and shifting wildlife populations, our framework can be used to make informed decisions about the best solutions to increase carbon sequestration, reduce ecological risks, and reduce climate impacts with greater certainty. [ABSTRACT FROM AUTHOR]

Published
2024
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21. 10 years of decision‐making for biodiversity conservation actions: A systematic literature review.

Author

Beher, Jutta, Treml, Eric, and Wintle, Brendan

Subjects
BIODIVERSITY conservation, MULTIPLE criteria decision making, DELIBERATION, DECISION making, DECISION theory, PROTECTED areas
Abstract

Decision science emphasizes necessary elements required for robust decision‐making. By incorporating decision science principles, frameworks, and tools, it has been demonstrated that decision‐makers can increase the chances of achieving conservation aims. Setting measurable objectives, clearly documenting assumptions about the impact of available actions on a specific threat or problem, explicitly considering constraints, exploring and characterizing uncertainty, and structured deliberation on trade‐offs have been identified as key elements of successful decision‐making. We quantify the extent to which these five elements were utilized in published examples of decision making in conservation in both academic and conservation practice between 2009 and 2018. We found that less than 50% of identified examples included all five elements, with differences in the degree of decision science applied across five commonly used decision support approaches: adaptive management (AM), systematic conservation planning (SCP), structured decision making (SDM), multi‐criteria decision analysis, and cost‐effectiveness analysis. Example applications that utilized the SDM framework were limited in numbers but used on average more than 50% of the five key elements we considered. Although SCP and AM constituted the majority of examples, they were more prevalent in academic studies rather than management applications. SCP and AM examples were widespread in protected area planning, threat abatement, and restoration. Strong geographic bias exists in documented conservation activities that deploy all five decision science elements. [ABSTRACT FROM AUTHOR]

Published
2024
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28. Identifying technology solutions to bring conservation into the innovation era

Author

Iacona, Gwenllian, Ramachandra, Anurag, McGowan, Jennifer, Davies, Alasdair, Joppa, Lucas, Koh, Lian Pin, Fegraus, Eric, Game, Edward, Guillera-Arroita, Gurutzeta, Harcourt, Rob, Indraswari, Karlina, Lahoz-Monfort, José J, Oliver, Jessica L, Possingham, Hugh P, Ward, Adrian, Watson, David W, Watson, James EM, Wintle, Brendan A, and Chadès, Iadine

Published
2019

32. Open access solutions for biodiversity journals : Do not replace one problem with another

Author

Peterson, A. Townsend, Anderson, Robert P., Beger, Maria, Bolliger, Janine, Brotons, Lluís, Burridge, Christopher P., Cobos, Marlon E., Cuervo-Robayo, Angela P., Di Minin, Enrico, Diez, Jeffrey, Elith, Jane, Embling, Clare B., Escobar, Luis E., Essl, Franz, Feeley, Kenneth J., Hawkes, Lucy, Jiménez-García, Daniel, Jimenez, Laura, Green, David M., Knop, Eva, Kühn, Ingolf, Lahoz-Monfort, José J., Lira-Noriega, Andres, Lobo, Jorge M., Loyola, Rafael, Nally, Ralph Mac, Machado-Stredel, Fernando, Martínez-Meyer, Enrique, McCarthy, Michael, Merow, Cory, Nori, Javier, Nuñez-Penichet, Claudia, Osorio-Olvera, Luis, Pyšek, Petr, Rejmánek, Marcel, Ricciardi, Anthony, Robertson, Mark, Soto, Octavio Rojas, Romero-Alvarez, Daniel, Roura-Pascual, Núria, Santini, Luca, Schoeman, David S., Schröder, Boris, Soberon, Jorge, Strubbe, Diederik, Thuiller, Wilfried, Traveset, Anna, Treml, Eric A., Václavík, Tomáš, Varela, Sara, Watson, James E. M., Wiersma, Yolanda, Wintle, Brendan, Yañez-Arenas, Carlos, and Zurell, Damaris

Published
2019

39. Model averaging in ecology : a review of Bayesian, information-theoretic, and tactical approaches for predictive inference

Author

Dormann, Carsten F., Calabrese, Justin M., Guillera-Arroita, Gurutzeta, Matechou, Eleni, Bahn, Volker, Bartoń, Kamil, Beale, Colin M., Ciuti, Simone, Elith, Jane, Gerstner, Katharina, Guelat, Jérôme, Keil, Petr, Lahoz-Monfort, José J., Pollock, Laura J., Reineking, Björn, Roberts, David R., Schröder, Boris, Thuiller, Wilfried, Warton, David I., Wintle, Brendan A., Wood, Simon N., Wüest, Rafael O., and Hartig, Florian

Published
2018

41. Predicting species distributions for conservation decisions.

Author

Guisan, Antoine, Tingley, Reid, Baumgartner, John B, Naujokaitis-Lewis, Ilona, Sutcliffe, Patricia R, Tulloch, Ayesha IT, Regan, Tracey J, Brotons, Lluis, McDonald-Madden, Eve, Mantyka-Pringle, Chrystal, Martin, Tara G, Rhodes, Jonathan R, Maggini, Ramona, Setterfield, Samantha A, Elith, Jane, Schwartz, Mark W, Wintle, Brendan A, Broennimann, Olivier, Austin, Mike, Ferrier, Simon, Kearney, Michael R, Possingham, Hugh P, and Buckley, Yvonne M

Subjects
Decision Making, Ecology, Conservation of Natural Resources, Decision Support Techniques, Models, Theoretical, Research Design, Endangered Species, Biological invasions, conservation planning, critical habitats, environmental suitability, reserve selection, species distribution model, structured decision making, translocation, Models, Theoretical, Ecological Applications, Evolutionary Biology
Abstract

Species distribution models (SDMs) are increasingly proposed to support conservation decision making. However, evidence of SDMs supporting solutions for on-ground conservation problems is still scarce in the scientific literature. Here, we show that successful examples exist but are still largely hidden in the grey literature, and thus less accessible for analysis and learning. Furthermore, the decision framework within which SDMs are used is rarely made explicit. Using case studies from biological invasions, identification of critical habitats, reserve selection and translocation of endangered species, we propose that SDMs may be tailored to suit a range of decision-making contexts when used within a structured and transparent decision-making process. To construct appropriate SDMs to more effectively guide conservation actions, modellers need to better understand the decision process, and decision makers need to provide feedback to modellers regarding the actual use of SDMs to support conservation decisions. This could be facilitated by individuals or institutions playing the role of 'translators' between modellers and decision makers. We encourage species distribution modellers to get involved in real decision-making processes that will benefit from their technical input; this strategy has the potential to better bridge theory and practice, and contribute to improve both scientific knowledge and conservation outcomes.

Published
2013

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