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Counting the books while the library burns: why conservation monitoring programs need a plan for action.
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- Frontiers in Ecology & the Environment, 2013, v. 11, n. 10, p. 549, doi. 10.1890/120220
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10 years of decision‐making for biodiversity conservation actions: A systematic literature review.
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- Conservation Science & Practice, 2024, v. 6, n. 7, p. 1, doi. 10.1111/csp2.13170
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Linking species distribution models with structured expert elicitation for predicting management effectiveness.
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- Conservation Science & Practice, 2023, v. 5, n. 12, p. 1, doi. 10.1111/csp2.13038
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A threatened species index for Australian birds.
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- Conservation Science & Practice, 2021, v. 3, n. 2, p. 1, doi. 10.1111/csp2.322
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Developing a two‐way learning monitoring program for Mankarr (Greater Bilby) in the Western Desert, Western Australia.
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- Ecological Management & Restoration, 2022, v. 23, p. 129, doi. 10.1111/emr.12543
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Getting our Act together to improve Indigenous leadership and recognition in biodiversity management.
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- Ecological Management & Restoration, 2022, v. 23, p. 33, doi. 10.1111/emr.12523
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Some practical suggestions for improving engagement between researchers and policy-makers in natural resource management.
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- Ecological Management & Restoration, 2008, v. 9, n. 3, p. 182, doi. 10.1111/j.1442-8903.2008.00416.x
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Extinct or still out there? Disentangling influences on extinction and rediscovery helps to clarify the fate of species on the edge.
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- Global Change Biology, 2017, v. 23, n. 2, p. 621, doi. 10.1111/gcb.13421
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Unpacking the mechanisms captured by a correlative species distribution model to improve predictions of climate refugia.
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- Global Change Biology, 2016, v. 22, n. 7, p. 2425, doi. 10.1111/gcb.13280
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Modelling both dominance and species distribution provides a more complete picture of changes to mangrove ecosystems under climate change.
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- Global Change Biology, 2015, v. 21, n. 8, p. 3005, doi. 10.1111/gcb.12930
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Incorporating spatial autocorrelation into species distribution models alters forecasts of climate-mediated range shifts.
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- Global Change Biology, 2014, v. 20, n. 8, p. 2566, doi. 10.1111/gcb.12598
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Plant extinction risk under climate change: are forecast range shifts alone a good indicator of species vulnerability to global warming?
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- Global Change Biology, 2012, v. 18, n. 4, p. 1357, doi. 10.1111/j.1365-2486.2011.02614.x
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Model averaging in ecology: a review of Bayesian, information‐theoretic, and tactical approaches for predictive inference.
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- Ecological Monographs, 2018, v. 88, n. 4, p. 485, doi. 10.1002/ecm.1309
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An info-gap approach to power and sample size calculations.
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- Environmetrics, 2007, v. 18, n. 2, p. 189, doi. 10.1002/env.811
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Combining financial costs and statistical power to optimize monitoring to detect recoveries of species after megafire.
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- Global Ecology & Biogeography, 2022, v. 31, n. 10, p. 2147, doi. 10.1111/geb.13554
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Evaluating 318 continental-scale species distribution models over a 60-year prediction horizon: what factors influence the reliability of predictions?
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- Global Ecology & Biogeography, 2017, v. 26, n. 3, p. 371, doi. 10.1111/geb.12545
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Is my species distribution model fit for purpose? Matching data and models to applications.
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- Global Ecology & Biogeography, 2015, v. 24, n. 3, p. 276, doi. 10.1111/geb.12268
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Imperfect detection impacts the performance of species distribution models.
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- Global Ecology & Biogeography, 2014, v. 23, n. 4, p. 504, doi. 10.1111/geb.12138
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Hydroperiod is the main driver of the spatial pattern of dominance in mangrove communities.
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- Global Ecology & Biogeography, 2013, v. 22, n. 7, p. 806, doi. 10.1111/geb.12063
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The predictive performance of process‐explicit range change models remains largely untested.
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- Ecography, 2023, v. 2023, n. 4, p. 1, doi. 10.1111/ecog.06048
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Ignoring Imperfect Detection in Biological Surveys Is Dangerous: A Response to ‘Fitting and Interpreting Occupancy Models'.
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- PLoS ONE, 2014, v. 9, n. 7, p. 1, doi. 10.1371/journal.pone.0099571
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The use of dynamic landscape metapopulation models for forest management: a case study of the red-backed salamander.
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- Canadian Journal of Forest Research, 2012, v. 42, n. 6, p. 1091, doi. 10.1139/x2012-068
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Design considerations for rapid biodiversity reconnaissance surveys and long‐term monitoring to assess the impact of wildfire.
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- Diversity & Distributions, 2022, v. 28, n. 3, p. 559, doi. 10.1111/ddi.13427
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Environmental context and differences between native and invasive observed niches of Batrachochytrium salamandrivorans affect invasion risk assessments in the Western Palaearctic.
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- Diversity & Distributions, 2018, v. 24, n. 12, p. 1788, doi. 10.1111/ddi.12795
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Weak environmental controls on the composition and diversity of medium and large‐sized vertebrate assemblages in neotropical rain forests of the Guiana Shield.
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- Diversity & Distributions, 2018, v. 24, n. 11, p. 1545, doi. 10.1111/ddi.12790
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Modelling the spatial variation of vital rates: An evaluation of the strengths and weaknesses of correlative species distribution models.
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- Diversity & Distributions, 2017, v. 23, n. 8, p. 841, doi. 10.1111/ddi.12586
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Estimation of the occupancy of butterflies in diverse biogeographic regions.
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- Diversity & Distributions, 2017, v. 23, n. 1, p. 1, doi. 10.1111/ddi.12504
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Predicting and understanding spatio-temporal dynamics of species recovery: implications for Asian crested ibis Nipponia nippon conservation in China.
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- Diversity & Distributions, 2016, v. 22, n. 8, p. 893, doi. 10.1111/ddi.12460
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Front Cover.
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- Diversity & Distributions, 2016, v. 22, n. 8, p. i, doi. 10.1111/ddi.12467
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Multiscale distribution models for conserving widespread species: the case of sloth bear Melursus ursinus in India.
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- Diversity & Distributions, 2015, v. 21, n. 9, p. 1087, doi. 10.1111/ddi.12335
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Threatened species impact assessments: survey effort requirements based on criteria for cumulative impacts.
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- Diversity & Distributions, 2015, v. 21, n. 6, p. 620, doi. 10.1111/ddi.12311
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Global and local evolutionary and ecological distinctiveness of terrestrial mammals: identifying priorities across scales.
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- Diversity & Distributions, 2015, v. 21, n. 5, p. 548, doi. 10.1111/ddi.12320
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Accounting for false positives improves estimates of occupancy from key informant interviews.
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- Diversity & Distributions, 2014, v. 20, n. 2, p. 223, doi. 10.1111/ddi.12151
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Integrating ensemble species distribution modelling and statistical phylogeography to inform projections of climate change impacts on species distributions.
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- Diversity & Distributions, 2013, v. 19, n. 12, p. 1480, doi. 10.1111/ddi.12098
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Designing occupancy surveys and interpreting non-detection when observations are imperfect.
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- Diversity & Distributions, 2012, v. 18, n. 4, p. 417, doi. 10.1111/j.1472-4642.2011.00874.x
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Hotspots of plant invasion predicted by propagule pressure and ecosystem characteristics.
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- Diversity & Distributions, 2011, v. 17, n. 6, p. 1099, doi. 10.1111/j.1472-4642.2011.00794.x
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steps: Software for spatially and temporally explicit population simulations.
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- Methods in Ecology & Evolution, 2020, v. 11, n. 4, p. 596, doi. 10.1111/2041-210X.13354
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200 EXPERTS DISSECTED THE BLACK SUMMER BUSHFIRES IN UNPRECEDENTED DETAIL. HERE ARE SIX LESSONS TO HEED.
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- Geography Bulletin, 2023, v. 55, n. 2, p. 50
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More than one route to PhD success.
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- Nature, 2006, v. 443, n. 7112, p. 720, doi. 10.1038/nj7112-720b
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Equilibrium Modeling for Environmental Science: Exploring the Nexus of Economic Systems and Environmental Change.
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- Earth's Future, 2021, v. 9, n. 9, p. 1, doi. 10.1029/2020EF001923
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Factors influencing the residency of bettongs using one‐way gates to exit a fenced reserve.
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- Austral Ecology, 2020, v. 45, n. 7, p. 858, doi. 10.1111/aec.12898
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Predators, fire or resources: What drives the distribution of herbivores in fragmented mesic forests?
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- Austral Ecology, 2020, v. 45, n. 3, p. 329, doi. 10.1111/aec.12861
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Impacts of climate change and urban development on the spotted marsh frog ( Limnodynastes tasmaniensis).
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- Austral Ecology, 2013, v. 38, n. 1, p. 11, doi. 10.1111/j.1442-9993.2012.02365.x
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When have we looked hard enough? A novel method for setting minimum survey effort protocols for flora surveys.
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- Austral Ecology, 2008, v. 33, n. 8, p. 986, doi. 10.1111/j.1442-9993.2008.01869.x
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Fauna habitat modelling and mapping: A review and case study in the Lower Hunter Central Coast region of NSW.
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- Austral Ecology, 2005, v. 30, n. 7, p. 719, doi. 10.1111/j.1442-9993.2005.01514.x
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Allocating monitoring effort in the face of unknown unknowns B. A. Wintle, M. C. Runge and S. A. Bekessy Monitoring and the unknown unknowns.
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- Ecology Letters, 2010, v. 13, n. 11, p. 1325, doi. 10.1111/j.1461-0248.2010.01514.x
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Zero tolerance ecology: improving ecological inference by modelling the source of zero observations.
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- Ecology Letters, 2005, v. 8, n. 11, p. 1235, doi. 10.1111/j.1461-0248.2005.00826.x
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Dealing with Cumulative Biodiversity Impacts in Strategic Environmental Assessment: A New Frontier for Conservation Planning.
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- Conservation Letters, 2017, v. 10, n. 2, p. 195, doi. 10.1111/conl.12260
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Analysis of Trade-Offs Between Biodiversity, Carbon Farming and Agricultural Development in Northern Australia Reveals the Benefits of Strategic Planning.
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- Conservation Letters, 2017, v. 10, n. 1, p. 94, doi. 10.1111/conl.12255
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Climate and Fire Scenario Uncertainty Dominate the Evaluation of Options for Conserving the Great Desert Skink.
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- Conservation Letters, 2016, v. 9, n. 3, p. 181, doi. 10.1111/conl.12202
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