Author
Ralph Charles Mac Nally
Other affiliations: Monash University, Cooperative Research Centre, University of Cambridge ...read more
Bio: Ralph Charles Mac Nally is an academic researcher from University of Melbourne. The author has contributed to research in topics: Species richness & Biodiversity. The author has an hindex of 59, co-authored 255 publications receiving 12980 citations. Previous affiliations of Ralph Charles Mac Nally include Monash University & Cooperative Research Centre.
Topics: Species richness, Biodiversity, Vegetation, Riparian zone, Floodplain
Papers published on a yearly basis
Papers
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TL;DR: In this article, it is suggested that if the two approaches do not agree upon which of the independent variables are likely to be'significant' then the deductions must be subject to doubt.
Abstract: In many large-scale conservation or ecological problems where experiments are intractable or unethical, regression methods are used to attempt to gauge the impact of a set of nominally independent variables (X) upon a dependent variable (Y). Workers often want to assert that a given X has a major influence on Y, and so, by using this indirection to infer a probable causal relationship. There are two difficulties apart from the demonstrability issue itself: (1) multiple regression is plagued by collinear relationships in X; and (2) any regression is designed to produce a function that in some way minimizes the overall difference between the observed and ‘predicted’ Ys, which does not necessarily equate to determining probable influence in a multivariate setting. Problem (1) may be explored by comparing two avenues, one in which a single ‘best’ regression model is sought and the other where all possible regression models are considered contemporaneously. It is suggested that if the two approaches do not agree upon which of the independent variables are likely to be ‘significant’, then the deductions must be subject to doubt.
934 citations
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Lancaster University1, Museu Paraense Emílio Goeldi2, Universidade Federal de Lavras3, Empresa Brasileira de Pesquisa Agropecuária4, Cornell University5, University of Canberra6, Arthur Rylah Institute for Environmental Research7, Escola Superior de Agricultura Luiz de Queiroz8, Federal University of Pará9, Universidade Federal de Viçosa10, University of Exeter11, National Institute for Space Research12, University of São Paulo13, Universidade Federal de Mato Grosso14, International Institute of Minnesota15, Stockholm Environment Institute16
TL;DR: In this article, the authors used a large data set of plants, birds and dung beetles (1,538, 460 and 156 species, respectively) sampled in 36 catchments in the Brazilian state of Para.
Abstract: Concerted political attention has focused on reducing deforestation, and this remains the cornerstone of most biodiversity conservation strategies. However, maintaining forest cover may not reduce anthropogenic forest disturbances, which are rarely considered in conservation programmes. These disturbances occur both within forests, including selective logging and wildfires, and at the landscape level, through edge, area and isolation effects. Until now, the combined effect of anthropogenic disturbance on the conservation value of remnant primary forests has remained unknown, making it impossible to assess the relative importance of forest disturbance and forest loss. Here we address these knowledge gaps using a large data set of plants, birds and dung beetles (1,538, 460 and 156 species, respectively) sampled in 36 catchments in the Brazilian state of Para. Catchments retaining more than 69–80% forest cover lost more conservation value from disturbance than from forest loss. For example, a 20% loss of primary forest, the maximum level of deforestation allowed on Amazonian properties under Brazil’s Forest Code, resulted in a 39–54% loss of conservation value: 96–171% more than expected without considering disturbance effects. We extrapolated the disturbance-mediated loss of conservation value throughout Para, which covers 25% of the Brazilian Amazon. Although disturbed forests retained considerable conservation value compared with deforested areas, the toll of disturbance outside Para’s strictly protected areas is equivalent to the loss of 92,000–139,000 km2 of primary forest. Even this lowest estimate is greater than the area deforested across the entire Brazilian Amazon between 2006 and 2015 (ref. 10). Species distribution models showed that both landscape and within-forest disturbances contributed to biodiversity loss, with the greatest negative effects on species of high conservation and functional value. These results demonstrate an urgent need for policy interventions that go beyond the maintenance of forest cover to safeguard the hyper-diversity of tropical forest ecosystems.
698 citations
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Australian National University1, Murdoch University2, Deakin University3, University of Melbourne4, University of Maine5, Carleton University6, University of Washington7, Monash University8, Charles Sturt University9, Commonwealth Scientific and Industrial Research Organisation10, Iowa State University11, Stanford University12, University of Central Florida13, University of Queensland14, University of Alberta15, University of Idaho16, University of Tennessee17, Northern Arizona University18, Wildlife Conservation Society19, The Nature Conservancy20, University of California, Santa Cruz21
TL;DR: Six major themes in the ecology and conservation of landscapes are assessed, including recognizing the importance of landscape mosaics, recognizing interactions between vegetation cover and vegetation configuration, and 13 important issues that need to be considered in developing approaches to landscape conservation.
Abstract: The management of landscapes for biological conservation and ecologically sustainable natural resource use are crucial global issues. Research for over two decades has resulted in a large literature, yet there is little consensus on the applicability or even the existence of general principles or broad considerations that could guide landscape conservation. We assess six major themes in the ecology and conservation of landscapes. We identify 13 important issues that need to be considered in developing approaches to landscape conservation. They include recognizing the importance of landscape mosaics (including the integration of terrestrial and aquatic areas), recognizing interactions between vegetation cover and vegetation configuration, using an appropriate landscape conceptual model, maintaining the capacity to recover from disturbance and managing landscapes in an adaptive framework. These considerations are influenced by landscape context, species assemblages and management goals and do not translate directly into on-the-ground management guidelines but they should be recognized by researchers and resource managers when developing guidelines for specific cases. Two crucial overarching issues are: (i) a clearly articulated vision for landscape conservation and (ii) quantifiable objectives that offer unambiguous signposts for measuring progress.
673 citations
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TL;DR: It is argued that there are actually two kinds of MR modelling: (1) seeking the best predictive model; and (2) isolating amounts of varianceributable to each predictor variable.
Abstract: Ecologists and conservation biologists frequently use multipleregression (MR) to try to identify factors influencing response variables suchas species richness or occurrence. Many frequently used regression methods maygenerate spurious results due to multicollinearity. argued that there are actually two kinds of MR modelling: (1)seeking the best predictive model; and (2) isolating amounts of varianceattributable to each predictor variable. The former has attracted most attentionwith a plethora of criteria (measures of model fit penalized for modelcomplexity – number of parameters) and Bayes-factor-based methods havingbeen proposed, while the latter has been little considered, althoughhierarchical methods seem promising (e.g. hierarchical partitioning). If the twoapproaches agree on which predictor variables to retain, then it is more likelythat meaningful predictor variables (of those considered) have been found. Therehas been a problem in that, while hierarchical partitioning allowed the rankingof predictor variables by amounts of independent explanatory power, there was no(statistical) way to decide which variables to retain. A solution usingrandomization of the data matrix coupled with hierarchical partitioning ispresented, as is an ecological example.
555 citations
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University of New South Wales1, Office of Environment and Heritage2, Venezuelan Institute for Scientific Research3, University of Melbourne4, Finnish Environment Institute5, Smithsonian Conservation Biology Institute6, EcoHealth Alliance7, Royal Botanic Gardens8, University of Salento9, International Union for Conservation of Nature and Natural Resources10, Macquarie University11, NatureServe12, Environment Agency13, University of Vienna14, Flinders University15, Landcare Research16, University of Idaho17, Deakin University18, Monash University19, Federal Agency for Nature Conservation20, University of Cambridge21
TL;DR: A new conceptual model for ecosystem risk assessment founded on a synthesis of relevant ecological theories is presented, providing a consistent, practical and theoretically grounded framework for establishing a systematic Red List of the world’s ecosystems.
Abstract: An understanding of risks to biodiversity is needed for planning action to slow current rates of decline and secure ecosystem services for future human use. Although the IUCN Red List criteria provide an effective assessment protocol for species, a standard global assessment of risks to higher levels of biodiversity is currently limited. In 2008, IUCN initiated development of risk assessment criteria to support a global Red List of ecosystems. We present a new conceptual model for ecosystem risk assessment founded on a synthesis of relevant ecological theories. To support the model, we review key elements of ecosystem definition and introduce the concept of ecosystem collapse, an analogue of species extinction. The model identifies four distributional and functional symptoms of ecosystem risk as a basis for assessment criteria: A) rates of decline in ecosystem distribution; B) restricted distributions with continuing declines or threats; C) rates of environmental (abiotic) degradation; and D) rates of disruption to biotic processes. A fifth criterion, E) quantitative estimates of the risk of ecosystem collapse, enables integrated assessment of multiple processes and provides a conceptual anchor for the other criteria. We present the theoretical rationale for the construction and interpretation of each criterion. The assessment protocol and threat categories mirror those of the IUCN Red List of species. A trial of the protocol on terrestrial, subterranean, freshwater and marine ecosystems from around the world shows that its concepts are workable and its outcomes are robust, that required data are available, and that results are consistent with assessments carried out by local experts and authorities. The new protocol provides a consistent, practical and theoretically grounded framework for establishing a systematic Red List of the world’s ecosystems. This will complement the Red List of species and strengthen global capacity to report on and monitor the status of biodiversity
491 citations
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TL;DR: Preface to the Princeton Landmarks in Biology Edition vii Preface xi Symbols used xiii 1.
Abstract: Preface to the Princeton Landmarks in Biology Edition vii Preface xi Symbols Used xiii 1. The Importance of Islands 3 2. Area and Number of Speicies 8 3. Further Explanations of the Area-Diversity Pattern 19 4. The Strategy of Colonization 68 5. Invasibility and the Variable Niche 94 6. Stepping Stones and Biotic Exchange 123 7. Evolutionary Changes Following Colonization 145 8. Prospect 181 Glossary 185 References 193 Index 201
14,171 citations
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21 Mar 2002TL;DR: An essential textbook for any student or researcher in biology needing to design experiments, sample programs or analyse the resulting data is as discussed by the authors, covering both classical and Bayesian philosophies, before advancing to the analysis of linear and generalized linear models Topics covered include linear and logistic regression, simple and complex ANOVA models (for factorial, nested, block, split-plot and repeated measures and covariance designs), and log-linear models Multivariate techniques, including classification and ordination, are then introduced.
Abstract: An essential textbook for any student or researcher in biology needing to design experiments, sample programs or analyse the resulting data The text begins with a revision of estimation and hypothesis testing methods, covering both classical and Bayesian philosophies, before advancing to the analysis of linear and generalized linear models Topics covered include linear and logistic regression, simple and complex ANOVA models (for factorial, nested, block, split-plot and repeated measures and covariance designs), and log-linear models Multivariate techniques, including classification and ordination, are then introduced Special emphasis is placed on checking assumptions, exploratory data analysis and presentation of results The main analyses are illustrated with many examples from published papers and there is an extensive reference list to both the statistical and biological literature The book is supported by a website that provides all data sets, questions for each chapter and links to software
9,509 citations
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University of Melbourne1, Stony Brook University2, City University of New York3, Princeton University4, University of Lausanne5, University of California, Berkeley6, University of Alaska Fairbanks7, National Institute of Water and Atmospheric Research8, Commonwealth Scientific and Industrial Research Organisation9, University of São Paulo10, University of Missouri11, Consejo Nacional de Ciencia y Tecnología12, University of Kansas13, Landcare Research14, AT&T15, McGill University16, James Cook University17, Swiss Federal Institute for Forest, Snow and Landscape Research18
TL;DR: This work compared 16 modelling methods over 226 species from 6 regions of the world, creating the most comprehensive set of model comparisons to date and found that presence-only data were effective for modelling species' distributions for many species and regions.
Abstract: Prediction of species' distributions is central to diverse applications in ecology, evolution and conservation science. There is increasing electronic access to vast sets of occurrence records in museums and herbaria, yet little effective guidance on how best to use this information in the context of numerous approaches for modelling distributions. To meet this need, we compared 16 modelling methods over 226 species from 6 regions of the world, creating the most comprehensive set of model comparisons to date. We used presence-only data to fit models, and independent presence-absence data to evaluate the predictions. Along with well-established modelling methods such as generalised additive models and GARP and BIOCLIM, we explored methods that either have been developed recently or have rarely been applied to modelling species' distributions. These include machine-learning methods and community models, both of which have features that may make them particularly well suited to noisy or sparse information, as is typical of species' occurrence data. Presence-only data were effective for modelling species' distributions for many species and regions. The novel methods consistently outperformed more established methods. The results of our analysis are promising for the use of data from museums and herbaria, especially as methods suited to the noise inherent in such data improve.
7,589 citations
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TL;DR: In this article, the authors suggest that the term "fragmentation" should be reserved for the breaking apart of habitat, independent of habitat loss, and that fragmentation per se has much weaker effects on biodiversity that are at least as likely to be positive as negative.
Abstract: ■ Abstract The literature on effects of habitat fragmentation on biodiversity is huge. It is also very diverse, with different authors measuring fragmentation in different ways and, as a consequence, drawing different conclusions regarding both the magnitude and direction of its effects. Habitat fragmentation is usually defined as a landscape-scale process involving both habitat loss and the breaking apart of habitat. Results of empirical studies of habitat fragmentation are often difficult to interpret because (a) many researchers measure fragmentation at the patch scale, not the landscape scale and (b) most researchers measure fragmentation in ways that do not distinguish between habitat loss and habitat fragmentation per se, i.e., the breaking apart of habitat after controlling for habitat loss. Empirical studies to date suggest that habitat loss has large, consistently negative effects on biodiversity. Habitat fragmentation per se has much weaker effects on biodiversity that are at least as likely to be positive as negative. Therefore, to correctly interpret the influence of habitat fragmentation on biodiversity, the effects of these two components of fragmentation must be measured independently. More studies of the independent effects of habitat loss and fragmentation per se are needed to determine the factors that lead to positive versus negative effects of fragmentation per se. I suggest that the term “fragmentation” should be reserved for the breaking apart of habitat, independent of habitat loss.
6,341 citations