School of BioSciences - Research Publications
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ItemEfficient effort allocation in line-transect distance sampling of high-density species: When to walk further, measure less-often and gain precision(WILEY, 2021-06)Abstract Line‐transect distance sampling is widely used to estimate population densities using distances of observed targets from transect lines to model detectability. When the target taxa are high density, the frequent measuring of distances may make the method seem impractical. We present a method that improves the efficiency of distance sampling when the target species occurs at high density. Only a proportion of targets are measured to model the detection function, and the time saved on the survey is then used to cover a longer total length of transect and accrue a larger ‘count only’ sample. This approach can improve the precision of the population density estimate when the cost of measuring the distance to a detected target is more than half the cost of walking to the next target. We find the optimal proportion of distances to measure that minimises the variance of the density estimate for a fixed survey budget. We quantify how much this optimised strategy increases the precision of the density estimate compared with conventional line‐transect distance sampling. We then use simulated distance sampling data to test our expressions, and illustrate circumstances under which the optimised approach would be beneficial using distance sampling data on high‐density plants. The simulations indicate that the optimised method delivers benefits in precision, but the magnitude of the benefit is lower than predicted from our expressions, which are based on an asymptotic approximation of the variance. We apply an adjustment to the predicted benefit equation to account for this difference, and show that, in all three plant case studies, the optimised approach could improve the precision gained from a distance sampling survey between 20% and 50%. This new approach could broaden the ecological contexts in which distance sampling is applied, to include estimation of densities of abundant taxa where plots are conventionally used. The method may have interesting applications for other survey types, including multispecies surveys or those using cues or signs that occur at high density.
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ItemDefining and evaluating predictions of joint species distribution models(WILEY, 2021-03)Abstract Joint species distribution models (JSDMs) simultaneously model the distributions of multiple species, while accounting for residual co‐occurrence patterns. Despite increasing adoption of JSDMs in the literature, the question of how to define and evaluate JSDM predictions has only begun to be explored. We define four different JSDM prediction types that correspond to different aspects of species distribution and community assemblage processes. Marginal predictions are environment‐only predictions akin to predictions from single‐species models; joint predictions simultaneously predict entire community assemblages; and conditional marginal and conditional joint predictions are made at the species or assemblage level, conditional on the known occurrence state of one or more species at a site. We define five different classes of metrics that can be used to evaluate these types of predictions: threshold‐dependent, threshold‐independent, community dissimilarity, species richness and likelihood metrics. We illustrate different prediction types and evaluation metrics using a case study in which we fit a JSDM to a frog occurrence dataset collected in Melbourne, Australia. Joint species distribution models present opportunities to investigate the facets of species distribution and community assemblage processes that are not possible to explore with single‐species models. We show that there are a variety of different metrics available to evaluate JSDM predictions, and that choice of prediction type and evaluation metric should closely match the questions being investigated.
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ItemData Integration for Large-Scale Models of Species Distributions(ELSEVIER SCIENCE LONDON, 2020-01)With the expansion in the quantity and types of biodiversity data being collected, there is a need to find ways to combine these different sources to provide cohesive summaries of species' potential and realized distributions in space and time. Recently, model-based data integration has emerged as a means to achieve this by combining datasets in ways that retain the strengths of each. We describe a flexible approach to data integration using point process models, which provide a convenient way to translate across ecological currencies. We highlight recent examples of large-scale ecological models based on data integration and outline the conceptual and technical challenges and opportunities that arise.
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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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ItemTraits explain invasion of alien plants into tropical rainforests(WILEY, 2021-05)1. The establishment of new botanic gardens in tropical regions highlights a need for weed risk assessment tools suitable for tropical ecosystems. The relevance of plant traits for invasion into tropical rainforests has not been well studied.2. Working in and around four botanic gardens in Indonesia where 590 alien species have been planted, we estimated the effect of four plant traits, plus time since species introduction, on: (a) the naturalization probability and (b) abundance (density) of naturalized species in adjacent native tropical rainforests; and (c) the distance that naturalized alien plants have spread from the botanic gardens.3. We found that specific leaf area (SLA) strongly differentiated 23 naturalized from 78 non-naturalized alien species (randomly selected from 577 non-naturalized species) in our study. These trends may indicate that aliens with high SLA, which had a higher probability of naturalization, benefit from at least two factors when establishing in tropical forests: high growth rates and occupation of forest gaps. Naturalized aliens had high SLA and tended to be short. However, plant height was not significantly related to species' naturalization probability when considered alongside other traits.4. Alien species that were present in the gardens for over 30 years and those with small seeds also had higher probabilities of becoming naturalized, indicating that garden plants can invade the understorey of closed canopy tropical rainforests, especially when invading species are shade tolerant and have sufficient time to establish.5. On average, alien species that were not animal dispersed spread 78 m further into the forests and were more likely to naturalize than animal-dispersed species. We did not detect relationships between the measured traits and estimated density of naturalized aliens in the adjacent forests.6. Synthesis: Traits were able to differentiate alien species from botanic gardens that naturalized in native forest from those that did not; this is promising for developing trait-based risk assessment in the tropics. To limit the risk of invasion and spread into adjacent native forests, we suggest tropical botanic gardens avoid planting alien species with fast carbon capture strategies and those that are shade tolerant.
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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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ItemAssessing the accuracy of density-independent demographic models for predicting species ranges(WILEY, 2021-03)Accurately predicting species ranges is a primary goal of ecology. Demographic distribution models (DDMs), which correlate underlying vital rates (e.g. survival and reproduction) with environmental conditions, can potentially predict species ranges through time and space. However, tests of DDM accuracy across wide ranges of species' life histories are surprisingly lacking. Using simulations of 1.5 million hypothetical species' range dynamics, we evaluated when DDMs accurately predicted future ranges, to provide clear guidelines for the use of this emerging approach. We limited our study to deterministic demographic models ignoring density dependence, since these models are the most commonly used in the literature. We found that density‐independent DDMs overpredicted extinction if populations were near carrying capacity in the locations where demographic data were available. However, DDMs accurately predicted species ranges if demographic data were limited to sites with mean initial abundance less than one half of carrying capacity. Additionally, the DDMs required demographic data from at least 25 sites, over a short time‐interval (< 10 time‐steps), as populations initially below carrying capacity can saturate in long‐term studies. For species with demographic data from many low density sites, DDMs predicted occurrence more accurately than correlative species distribution models (SDMs) in locations where the species eventually persisted, but not where the species went extinct. These results were insensitive to differences in simulated dispersal, levels of environmental stochasticity, the effects of the environmental variables and the functional forms of density dependence. Our findings suggest that deterministic, density‐independent DDMs are appropriate for applications where locating all possible sites the species might occur in is prioritized over reducing false presence predictions in absent sites. This makes DDMs a promising tool for mapping invasion risk. However, demographic data are often collected at sites where a species is abundant. Density‐independent DDMs are inappropriate in this case.
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ItemPopulation Status of a Cryptic Top Predator: An Island-Wide Assessment of Tigers in Sumatran Rainforests(PUBLIC LIBRARY SCIENCE, 2011-11-02)Large carnivores living in tropical rainforests are under immense pressure from the rapid conversion of their habitat. In response, millions of dollars are spent on conserving these species. However, the cost-effectiveness of such investments is poorly understood and this is largely because the requisite population estimates are difficult to achieve at appropriate spatial scales for these secretive species. Here, we apply a robust detection/non-detection sampling technique to produce the first reliable population metric (occupancy) for a critically endangered large carnivore; the Sumatran tiger (Panthera tigris sumatrae). From 2007-2009, seven landscapes were surveyed through 13,511 km of transects in 394 grid cells (17×17 km). Tiger sign was detected in 206 cells, producing a naive estimate of 0.52. However, after controlling for an unequal detection probability (where p = 0.13±0.017; ±S.E.), the estimated tiger occupancy was 0.72±0.048. Whilst the Sumatra-wide survey results gives cause for optimism, a significant negative correlation between occupancy and recent deforestation was found. For example, the Northern Riau landscape had an average deforestation rate of 9.8%/yr and by far the lowest occupancy (0.33±0.055). Our results highlight the key tiger areas in need of protection and have led to one area (Leuser-Ulu Masen) being upgraded as a 'global priority' for wild tiger conservation. However, Sumatra has one of the highest global deforestation rates and the two largest tiger landscapes identified in this study will become highly fragmented if their respective proposed roads networks are approved. Thus, it is vital that the Indonesian government tackles these threats, e.g. through improved land-use planning, if it is to succeed in meeting its ambitious National Tiger Recovery Plan targets of doubling the number of Sumatran tigers by 2022.