Graeme Newell

Bio: Graeme Newell is an academic researcher from Arthur Rylah Institute for Environmental Research. The author has contributed to research in topics: Habitat fragmentation & Tree-kangaroo. The author has an hindex of 24, co-authored 35 publications receiving 3092 citations. Previous affiliations of Graeme Newell include Cooperative Research Centre.

Selecting thresholds for the prediction of species occurrence with presence‐only data

Abstract: Aim Species distribution models have been widely used to tackle ecological, evolutionary and conservation problems. Most species distribution modelling techniques produce continuous suitability predictions, but many real applications (e.g. reserve design, species invasion and climate change impact assessment) and model evaluations require binary outputs, and thresholds are needed for these transformations. Although there are many threshold selection methods for presence/absence data, it is unclear whether these are suitable for presence-only data. In this paper, we investigate mathematically and empirically which of the existing threshold selection methods can be used confidently with presence-only data. Location We used real spatially explicit environmental data derived from the western part of the state of Victoria, south-eastern Australia, and simulated species distributions within this area. Methods Thirteen existing threshold selection methods were investigated mathematically to see whether the same threshold can be produced using either presence/absence data or presence-only data. We further adopted a simulation approach, created many virtual species with differing prevalences in a real landscape in south-eastern Australia, generated data sets with different proportions of pseudo-absences, built eight types of models with four modelling techniques, and investigated the behaviours of four threshold selection methods in these situations. Results Three threshold selection methods were not affected by pseudo-absences, including max SSS (which is based on maximizing the sum of sensitivity and specificity), the prevalence of model training data and the mean predicted value of a set of random points. Max SSS produced higher sensitivity in most cases and higher true skill statistic and kappa in many cases than the other methods. The other methods produced different thresholds from presence-only data to those determined from presence/absence data. Main conclusions Max SSS is a promising method for threshold selection when only presence data are available.

Assessing the quality of native vegetation: The 'habitat hectares' approach

Abstract: Summary Assessments of the ‘quality’, condition or status of stands of native vegetation or habitat are now commonplace and are often an essential component of ecological studies and planning processes. Even when soundly based upon ecological principles, these assessments are usually highly subjective and involve implicit value judgements. The present paper describes a novel approach to vegetation or habitat quality assessment (habitat hectares approach) that can be used in almost all types of terrestrial vegetation. It is based on explicit comparisons between existing vegetation features and those of ‘benchmarks’ representing the average characteristics of mature stands of native vegetation of the same community type in a ‘natural’ or ‘undisturbed’ condition. Components of the index incorporate vegetation physiognomy and critical aspects of viability (e.g. degree of regeneration, impact of weeds) and spatial considerations (e.g. area, distribution and connectivity of remnant vegetation in the broader landscape). The approach has been developed to assist in making more objective and explicit decisions about where scarce conservation resources are allocated. Although the approach does not require an intimate botanical knowledge, it is believed to be ecologically valid and useful in many contexts. Importantly, the index does not provide a definitive statement on conservation status nor habitat suitability for individual species. It purposefully takes a ‘broad-brush’ approach and is primarily intended for use by people involved with making environmentally sensitive planning and management decisions, but may be useful within environmental research programmes. The ‘habitat hectares’ approach is subject to further research and ongoing refinement and constructive feedback is sought from practitioners.

On the selection of thresholds for predicting species occurrence with presence-only data.

Abstract: Presence-only data present challenges for selecting thresholds to transform species distribution modeling results into binary outputs. In this article, we compare two recently published threshold selection methods (maxSSS and maxF pb) and examine the effectiveness of the threshold-based prevalence estimation approach. Six virtual species with varying prevalence were simulated within a real landscape in southeastern Australia. Presence-only models were built with DOMAIN, generalized linear model, Maxent, and Random Forest. Thresholds were selected with two methods maxSSS and maxF pb with four presence-only datasets with different ratios of the number of known presences to the number of random points (KP-RP ratio). Sensitivity, specificity, true skill statistic, and F measure were used to evaluate the performance of the results. Species prevalence was estimated as the ratio of the number of predicted presences to the total number of points in the evaluation dataset. Thresholds selected with maxF pb varied as the KP-RP ratio of the threshold selection datasets changed. Datasets with the KP-RP ratio around 1 generally produced better results than scores distant from 1. Results produced by We conclude that maxFpb had specificity too low for very common species using Random Forest and Maxent models. In contrast, maxSSS produced consistent results whichever dataset was used. The estimation of prevalence was almost always biased, and the bias was very large for DOMAIN and Random Forest predictions. We conclude that maxF pb is affected by the KP-RP ratio of the threshold selection datasets, but maxSSS is almost unaffected by this ratio. Unbiased estimations of prevalence are difficult to be determined using the threshold-based approach.

Measuring and comparing the accuracy of species distribution models with presence–absence data

Abstract: Species distribution models have been widely used to predict species distributions for various purposes, including conservation planning, and climate change impact assessment. The success of these applications relies heavily on the accuracy of the models. Various measures have been proposed to assess the accuracy of the models. Rigorous statistical analysis should be incorporated in model accuracy assessment. However, since relevant information about the statistical properties of accuracy measures is scattered across various disciplines, ecologists find it difficult to select the most appropriate ones for their research. In this paper, we review accuracy measures that are currently used in species distribution modelling (SDM), and introduce additional metrics that have potential applications in SDM. For the commonly used measures (which are also intensively studied by statisticians), including overall accuracy, sensitivity, specificity, kappa, and area and partial area under the ROC curves, promising methods to construct confidence intervals and statistically compare the accuracy between two models are given. For other accuracy measures, methods to estimate standard errors are given, which can be used to construct approximate confidence intervals. We also suggest that as general tools, computer-intensive methods, especially bootstrap and randomization methods can be used in constructing confidence intervals and statistical tests if suitable analytic methods cannot be found. Usually, these computer-intensive methods provide robust results.

How Useful Are Species Distribution Models for Managing Biodiversity under Future Climates

Abstract: Climate change presents unprecedented challenges for biological conservation. Agencies are increasingly looking to modeled projections of species' distributions under future climates to inform management strategies. As government scientists with a responsibility to communicate the best available science to our policy colleagues, we question whether current modeling approaches and outputs are practically useful. Here, we synthesize conceptual problems with species distribution models (SDMs) associated with interspecific interactions, dispersal, ecological equilibria and time lags, evolution, and the sampling of niche space. Although projected SDMs have undoubtedly been critical in alerting us to the magnitude of climate change impacts, we conclude that until they offer insights that are more precise than what we can derive from basic ecological theory, we question their utility in deciding how to allocate scarce funds to large-scale conservation projects.

Thinking fast and slow.

Abstract: In 1974 an article appeared in Science magazine with the dry-sounding title “Judgment Under Uncertainty: Heuristics and Biases” by a pair of psychologists who were not well known outside their discipline of decision theory. In it Amos Tversky and Daniel Kahneman introduced the world to Prospect Theory, which mapped out how humans actually behave when faced with decisions about gains and losses, in contrast to how economists assumed that people behave. Prospect Theory turned Economics on its head by demonstrating through a series of ingenious experiments that people are much more concerned with losses than they are with gains, and that framing a choice from one perspective or the other will result in decisions that are exactly the opposite of each other, even if the outcomes are monetarily the same. Prospect Theory led cognitive psychology in a new direction that began to uncover other human biases in thinking that are probably not learned but are part of our brain’s wiring.

Impacts of climate change on the future of biodiversity.

Abstract: Many studies in recent years have investigated the effects of climate change on the future of biodiversity. In this review, we first examine the different possible effects of climate change that can operate at individual, population, species, community, ecosystem and biome scales, notably showing that species can respond to climate change challenges by shifting their climatic niche along three non-exclusive axes: time (e.g. phenology), space (e.g. range) and self (e.g. physiology). Then, we present the principal specificities and caveats of the most common approaches used to estimate future biodiversity at global and sub-continental scales and we synthesise their results. Finally, we highlight several challenges for future research both in theoretical and applied realms. Overall, our review shows that current estimates are very variable, depending on the method, taxonomic group, biodiversity loss metrics, spatial scales and time periods considered. Yet, the majority of models indicate alarming consequences for biodiversity, with the worst-case scenarios leading to extinction rates that would qualify as the sixth mass extinction in the history of the earth.

Citation classic - optimal foraging - a selective review of theory and tests

Abstract: Beginning with Emlen (1966) and MacArthur and Pianka (1966) and extending through the last ten years, several authors have sought to predict the foraging behavior of animals by means of mathematical models. These models are very similar,in that they all assume that the fitness of a foraging animal is a function of the efficiency of foraging measured in terms of some "currency" (Schoener, 1971) -usually energy- and that natural selection has resulted in animals that forage so as to maximize this fitness. As a result of these similarities, the models have become known as "optimal foraging models"; and the theory that embodies them, "optimal foraging theory." The situations to which optimal foraging theory has been applied, with the exception of a few recent studies, can be divided into the following four categories: (1) choice by an animal of which food types to eat (i.e., optimal diet); (2) choice of which patch type to feed in (i.e., optimal patch choice); (3) optimal allocation of time to different patches; and (4) optimal patterns and speed of movements. In this review we discuss each of these categories separately, dealing with both the theoretical developments and the data that permit tests of the predictions. The review is selective in the sense that we emphasize studies that either develop testable predictions or that attempt to test predictions in a precise quantitative manner. We also discuss what we see to be some of the future developments in the area of optimal foraging theory and how this theory can be related to other areas of biology. Our general conclusion is that the simple models so far formulated are supported are supported reasonably well by available data and that we are optimistic about the value both now and in the future of optimal foraging theory. We argue, however, that these simple models will requre much modification, espicially to deal with situations that either cannot easily be put into one or another of the above four categories or entail currencies more complicated that just energy.

A practical guide to MaxEnt for modeling species' distributions: what it does, and why inputs and settings matter

Abstract: The MaxEnt software package is one of the most popular tools for species distribution and environmental niche modeling, with over 1000 published applications since 2006. Its popularity is likely for two reasons: 1) MaxEnt typically outperforms other methods based on predictive accuracy and 2) the software is particularly easy to use. MaxEnt users must make a number of decisions about how they should select their input data and choose from a wide variety of settings in the software package to build models from these data. The underlying basis for making these decisions is unclear in many studies, and default settings are apparently chosen, even though alternative settings are often more appropriate. In this paper, we provide a detailed explanation of how MaxEnt works and a prospectus on modeling options to enable users to make informed decisions when preparing data, choosing settings and interpreting output. We explain how the choice of background samples reflects prior assumptions, how nonlinear functions of environmental variables (features) are created and selected, how to account for environmentally biased sampling, the interpretation of the various types of model output and the challenges for model evaluation. We demonstrate MaxEnt’s calculations using both simplified simulated data and occurrence data from South Africa on species of the flowering plant family Proteaceae. Throughout, we show how MaxEnt’s outputs vary in response to different settings to highlight the need for making biologically motivated modeling decisions.

Confounding factors in the detection of species responses to habitat fragmentation

Abstract: Habitat loss has pervasive and disruptive impacts on biodiversity in habitat remnants. The magnitude of the ecological impacts of habitat loss can be exacerbated by the spatial arrangement -- or fragmentation -- of remaining habitat. Fragmentation per se is a landscape-level phenomenon in which species that survive in habitat remnants are confronted with a modified environment of reduced area, increased isolation and novel ecological boundaries. The implications of this for individual organisms are many and varied, because species with differing life history strategies are differentially affected by habitat fragmentation. Here, we review the extensive literature on species responses to habitat fragmentation, and detail the numerous ways in which confounding factors have either masked the detection, or prevented the manifestation, of predicted fragmentation effects. Large numbers of empirical studies continue to document changes in species richness with decreasing habitat area, with positive, negative and no relationships regularly reported. The debate surrounding such widely contrasting results is beginning to be resolved by findings that the expected positive species-area relationship can be masked by matrix-derived spatial subsidies of resources to fragment-dwelling species and by the invasion of matrix-dwelling species into habitat edges. Significant advances have been made recently in our understanding of how species interactions are altered at habitat edges as a result of these changes. Interestingly, changes in biotic and abiotic parameters at edges also make ecological processes more variable than in habitat interiors. Individuals are more likely to encounter habitat edges in fragments with convoluted shapes, leading to increased turnover and variability in population size than in fragments that are compact in shape. Habitat isolation in both space and time disrupts species distribution patterns, with consequent effects on metapopulation dynamics and the genetic structure of fragment-dwelling populations. Again, the matrix habitat is a strong determinant of fragmentation effects within remnants because of its role in regulating dispersal and dispersal-related mortality, the provision of spatial subsidies and the potential mediation of edge-related microclimatic gradients. We show that confounding factors can mask many fragmentation effects. For instance, there are multiple ways in which species traits like trophic level, dispersal ability and degree of habitat specialisation influence species-level responses. The temporal scale of investigation may have a strong influence on the results of a study, with short-term crowding effects eventually giving way to long-term extinction debts. Moreover, many fragmentation effects like changes in genetic, morphological or behavioural traits of species require time to appear. By contrast, synergistic interactions of fragmentation with climate change, human-altered disturbance regimes, species interactions and other drivers of population decline may magnify the impacts of fragmentation. To conclude, we emphasise that anthropogenic fragmentation is a recent phenomenon in evolutionary time and suggest that the final, long-term impacts of habitat fragmentation may not yet have shown themselves.