School of BioSciences - Research Publications
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ItemIdentifying technology solutions to bring conservation into the innovation era(WILEY, 2019-12)Innovation has the potential to enable conservation science and practice to keep pace with the escalating threats to global biodiversity, but this potential will only be realized if such innovations are designed and developed to fulfill specific needs and solve well‐defined conservation problems. We propose that business‐world strategies for assessing the practicality of innovation can be applied to assess the viability of innovations, such as new technology, for addressing biodiversity conservation challenges. Here, we outline a five‐step, “lean start‐up” based approach for considering conservation innovation from a business‐planning perspective. Then, using three prominent conservation initiatives – Marxan (software), Conservation Drones (technology support), and Mataki (wildlife‐tracking devices) – as case studies, we show how considering proposed initiatives from the perspective of a conceptual business model can support innovative technologies in achieving desired conservation outcomes.
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ItemForecasting species range dynamics with process-explicit models: matching methods to applications(WILEY, 2019-11)Knowing where species occur is fundamental to many ecological and environmental applications. Species distribution models (SDMs) are typically based on correlations between species occurrence data and environmental predictors, with ecological processes captured only implicitly. However, there is a growing interest in approaches that explicitly model processes such as physiology, dispersal, demography and biotic interactions. These models are believed to offer more robust predictions, particularly when extrapolating to novel conditions. Many process-explicit approaches are now available, but it is not clear how we can best draw on this expanded modelling toolbox to address ecological problems and inform management decisions. Here, we review a range of process-explicit models to determine their strengths and limitations, as well as their current use. Focusing on four common applications of SDMs - regulatory planning, extinction risk, climate refugia and invasive species - we then explore which models best meet management needs. We identify barriers to more widespread and effective use of process-explicit models and outline how these might be overcome. As well as technical and data challenges, there is a pressing need for more thorough evaluation of model predictions to guide investment in method development and ensure the promise of these new approaches is fully realised.
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ItemModel averaging in ecology: a review of Bayesian, information-theoretic, and tactical approaches for predictive inference(WILEY, 2018-11)Abstract In ecology, the true causal structure for a given problem is often not known, and several plausible models and thus model predictions exist. It has been claimed that using weighted averages of these models can reduce prediction error, as well as better reflect model selection uncertainty. These claims, however, are often demonstrated by isolated examples. Analysts must better understand under which conditions model averaging can improve predictions and their uncertainty estimates. Moreover, a large range of different model averaging methods exists, raising the question of how they differ in their behaviour and performance. Here, we review the mathematical foundations of model averaging along with the diversity of approaches available. We explain that the error in model‐averaged predictions depends on each model's predictive bias and variance, as well as the covariance in predictions between models, and uncertainty about model weights. We show that model averaging is particularly useful if the predictive error of contributing model predictions is dominated by variance, and if the covariance between models is low. For noisy data, which predominate in ecology, these conditions will often be met. Many different methods to derive averaging weights exist, from Bayesian over information‐theoretical to cross‐validation optimized and resampling approaches. A general recommendation is difficult, because the performance of methods is often context dependent. Importantly, estimating weights creates some additional uncertainty. As a result, estimated model weights may not always outperform arbitrary fixed weights, such as equal weights for all models. When averaging a set of models with many inadequate models, however, estimating model weights will typically be superior to equal weights. We also investigate the quality of the confidence intervals calculated for model‐averaged predictions, showing that they differ greatly in behaviour and seldom manage to achieve nominal coverage. Our overall recommendations stress the importance of non‐parametric methods such as cross‐validation for a reliable uncertainty quantification of model‐averaged predictions.
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ItemUsing decision science to evaluate global biodiversity indices(WILEY, 2021-04)Global biodiversity indices are used to measure environmental change and progress toward conservation goals, yet few indices have been evaluated comprehensively for their capacity to detect trends of interest, such as declines in threatened species or ecosystem function. Using a structured approach based on decision science, we qualitatively evaluated 9 indices commonly used to track biodiversity at global and regional scales against 5 criteria relating to objectives, design, behavior, incorporation of uncertainty, and constraints (e.g., costs and data availability). Evaluation was based on reference literature for indices available at the time of assessment. We identified 4 key gaps in indices assessed: pathways to achieving goals (means objectives) were not always clear or relevant to desired outcomes (fundamental objectives); index testing and understanding of expected behavior was often lacking; uncertainty was seldom acknowledged or accounted for; and costs of implementation were seldom considered. These gaps may render indices inadequate in certain decision-making contexts and are problematic for indices linked with biodiversity targets and sustainability goals. Ensuring that index objectives are clear and their design is underpinned by a model of relevant processes are crucial in addressing the gaps identified by our assessment. Uptake and productive use of indices will be improved if index performance is tested rigorously and assumptions and uncertainties are clearly communicated to end users. This will increase index accuracy and value in tracking biodiversity change and supporting national and global policy decisions, such as the post-2020 global biodiversity framework of the Convention on Biological Diversity.
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ItemNo Preview AvailableCross-validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure(WILEY, 2017-08)Ecological data often show temporal, spatial, hierarchical (random effects), or phylogenetic structure. Modern statistical approaches are increasingly accounting for such dependencies. However, when performing cross‐validation, these structures are regularly ignored, resulting in serious underestimation of predictive error. One cause for the poor performance of uncorrected (random) cross‐validation, noted often by modellers, are dependence structures in the data that persist as dependence structures in model residuals, violating the assumption of independence. Even more concerning, because often overlooked, is that structured data also provides ample opportunity for overfitting with non‐causal predictors. This problem can persist even if remedies such as autoregressive models, generalized least squares, or mixed models are used. Block cross‐validation, where data are split strategically rather than randomly, can address these issues. However, the blocking strategy must be carefully considered. Blocking in space, time, random effects or phylogenetic distance, while accounting for dependencies in the data, may also unwittingly induce extrapolations by restricting the ranges or combinations of predictor variables available for model training, thus overestimating interpolation errors. On the other hand, deliberate blocking in predictor space may also improve error estimates when extrapolation is the modelling goal. Here, we review the ecological literature on non‐random and blocked cross‐validation approaches. We also provide a series of simulations and case studies, in which we show that, for all instances tested, block cross‐validation is nearly universally more appropriate than random cross‐validation if the goal is predicting to new data or predictor space, or for selecting causal predictors. We recommend that block cross‐validation be used wherever dependence structures exist in a dataset, even if no correlation structure is visible in the fitted model residuals, or if the fitted models account for such correlations.
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ItemThreatened species impact assessments: survey effort requirements based on criteria for cumulative impacts(WILEY, 2015-06)Abstract Aim Environmental impact assessments (EIAs) often involve establishing whether a species of concern is present at the site considered for development. When surveys falsely conclude that sites are unoccupied, species prevalence in the region is cumulatively reduced. We argue that setting an acceptable level of induced decline in species occurrence provides a defensible strategy to determine minimum survey effort requirements. We investigate methods for setting such requirements. Location Eastern Australia, although we demonstrate methods applicable wherever species detection data are available to inform survey design. Methods We use probability theory to investigate required survey effort when aiming to limit decline in species occurrence. We use optimization tools to provide a method that, in addition, minimizes overall survey costs. We demonstrate the methods using data for an Australian gliding marsupial. Results A method based on ensuring a constant probability of occupied site misclassification directly links with a prescribed acceptable decline in occurrence. Optimization results indicate that, under particular conditions, a cost‐efficient survey effort allocation can be achieved by setting a constant posterior probability of occupancy at sites where the species is not detected, provided the target level is set in accordance with the acceptable decline in occurrence. Our results provide a critical examination of the approach recently proposed by Wintle et al. (2012) for determining minimum survey effort requirements. Main conclusions EIA survey effort requirements should explicitly link uncertainty in establishing species absence with the broader consequences of failing to detect species presence in places subject to proposed impacts. A direct method, which involves keeping a constant probability of occupied site misclassification, only requires information about species detectability. Alternatively, a method that minimizes overall survey costs can be used. This approach also requires occupancy probability estimates so its performance relies on availability of an informative species distribution model.
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ItemNo Preview AvailableIs my species distribution model fit for purpose? Matching data and models to applications(WILEY, 2015-03)Abstract Species distribution models (SDMs) are used to inform a range of ecological, biogeographical and conservation applications. However, users often underestimate the strong links between data type, model output and suitability for end‐use. We synthesize current knowledge and provide a simple framework that summarizes how interactions between data type and the sampling process (i.e. imperfect detection and sampling bias) determine the quantity that is estimated by a SDM. We then draw upon the published literature and simulations to illustrate and evaluate the information needs of the most common ecological, biogeographical and conservation applications of SDM outputs. We find that, while predictions of models fitted to the most commonly available observational data (presence records) suffice for some applications, others require estimates of occurrence probabilities, which are unattainable without reliable absence records. Our literature review and simulations reveal that, while converting continuous SDM outputs into categories of assumed presence or absence is common practice, it is seldom clearly justified by the application's objective and it usually degrades inference. Matching SDMs to the needs of particular applications is critical to avoid poor scientific inference and management outcomes. This paper aims to help modellers and users assess whether their intended SDM outputs are indeed fit for purpose.
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ItemNo Preview AvailableDeep-sea diversity patterns are shaped by energy availability(NATURE PUBLISHING GROUP, 2016-05-19)The deep ocean is the largest and least-explored ecosystem on Earth, and a uniquely energy-poor environment. The distribution, drivers and origins of deep-sea biodiversity remain unknown at global scales. Here we analyse a database of more than 165,000 distribution records of Ophiuroidea (brittle stars), a dominant component of sea-floor fauna, and find patterns of biodiversity unlike known terrestrial or coastal marine realms. Both patterns and environmental predictors of deep-sea (2,000-6,500 m) species richness fundamentally differ from those found in coastal (0-20 m), continental shelf (20-200 m), and upper-slope (200-2,000 m) waters. Continental shelf to upper-slope richness consistently peaks in tropical Indo-west Pacific and Caribbean (0-30°) latitudes, and is well explained by variations in water temperature. In contrast, deep-sea species show maximum richness at higher latitudes (30-50°), concentrated in areas of high carbon export flux and regions close to continental margins. We reconcile this structuring of oceanic biodiversity using a species-energy framework, with kinetic energy predicting shallow-water richness, while chemical energy (export productivity) and proximity to slope habitats drive deep-sea diversity. Our findings provide a global baseline for conservation efforts across the sea floor, and demonstrate that deep-sea ecosystems show a biodiversity pattern consistent with ecological theory, despite being different from other planetary-scale habitats.
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ItemNo Preview AvailableValid auto-models for spatially autocorrelated occupancy and abundance data(WILEY-BLACKWELL, 2015-10)Summary Spatially autocorrelated species abundance or distribution data sets typically generate spatially autocorrelated residuals in generalized linear models; a broader modelling framework is therefore required. Auto‐logistic and related auto‐models, implemented approximately as autocovariate regression, provide simple and direct modelling of spatial population processes. The auto‐logistic model has been widely applied in ecology since Augustin, Mugglestone and Buckland (Journal of Applied Ecology, 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 (Ecological Modelling, 2007, 207, 234) questioned the validity of auto‐logistic regression even for fully observed data, giving examples of apparent underestimation of covariate parameters in analysis of simulated ‘snouter’ data. Dormann et al. (Ecography, 2007, 30, 609) extended this critique to auto‐Poisson and certain auto‐normal models, finding again that autocovariate‐regression estimates for covariate parameters bore little resemblance to values employed to generate ‘snouter’ data. We note that all the above studies employ neighbourhood weighting schemes inconsistent with auto‐model definitions; in the auto‐Poisson case, a further inconsistency was the failure to exclude cooperative interactions. We investigate the impact of these implementation errors on auto‐model estimation using both empirical and simulated data sets. We show that when ‘snouter’ data are reanalysed using valid weightings, very different estimates are obtained for covariate parameters. For auto‐logistic and auto‐normal models, the new estimates agree closely with values used to generate the ‘snouter’ simulations. Re‐analysis of the red deer data shows that invalid neighbourhood weightings generate only small estimation errors for the full data set, but larger errors occur on geographic subsamples. A substantial fraction of papers employing auto‐logistic regression use these invalid neighbourhood weightings, which were embedded as default options in the widely used ‘spdep’ spatial dependence package for R. Auto‐logistic analyses conducted 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.