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Modeling the distribution of a wide-ranging invasive species using the sampling efforts of expert and citizen scientists.

ABSTRACT: In its invasive range in Australia, the European rabbit threatens the persistence of native flora and fauna and damages agricultural production. Understanding its distribution and ecological niche is critical for developing management plans to reduce populations and avoid further biodiversity and economic losses.We developed an ensemble of species distribution models (SDMs) to determine the geographic range limits and habitat suitability of the rabbit in Australia. We examined the advantage of incorporating data collected by citizens (separately and jointly with expert data) and explored issues of spatial biases in occurrence data by implementing different approaches to generate pseudo-absences. We evaluated the skill of our model using three approaches: cross-validation, out-of-region validation, and evaluation of the covariate response curves according to expert knowledge of rabbit ecology.Combining citizen and expert occurrence data improved model skill based on cross-validation, spatially reproduced important aspects of rabbit ecology, and reduced the need to extrapolate results beyond the studied areas.Our ensemble model projects that rabbits are distributed across approximately two thirds of Australia. Annual maximum temperatures >25°C and annual minimum temperatures >10°C define, respectively, the southern and northern most range limits of its distribution. In the arid and central regions, close access to permanent water (?~ 0.4 km) and reduced clay soil composition (~20%-50%) were the major factors influencing the probability of occurrence of rabbits. Synthesis and applications. Our results show that citizen science data can play an important role in managing invasive species by providing missing information on occurrences in regions not surveyed by experts because of logistics or financial constraints. The additional sampling effort provided by citizens can improve the capacity of SDMs to capture important elements of a species ecological niche, improving the capacity of statistical models to accurately predict the geographic range of invasive species.

SUBMITTER: Roy-Dufresne E

PROVIDER: S-EPMC6802020 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature

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Modeling the distribution of a wide-ranging invasive species using the sampling efforts of expert and citizen scientists.

Roy-Dufresne Emilie E Saltré Frédérik F Cooke Brian D BD Mellin Camille C Mutze Greg G Cox Tarnya T Fordham Damien A DA

Ecology and evolution 20190919 19

In its invasive range in Australia, the European rabbit threatens the persistence of native flora and fauna and damages agricultural production. Understanding its distribution and ecological niche is critical for developing management plans to reduce populations and avoid further biodiversity and economic losses.We developed an ensemble of species distribution models (SDMs) to determine the geographic range limits and habitat suitability of the rabbit in Australia. We examined the advantage of i ...[more]

PMID: 31641454

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Project description:Experts use knowledge to infer the distribution of species based on fuzzy logical assumptions about the relationship between species and the environment. Thus, expert knowledge is amenable to fuzzy logic modelling, which give to propositions a continuous truth value between 0 and 1. In species distribution modelling, fuzzy logic may also be used to model, from a number of records, the degree to which conditions are favourable to the occurrence of a species. Therefore, fuzzy logic operations can be used to compare and combine models based on expert knowledge and species records. Here, we applied fuzzy logic modelling to the distribution of amphibians in Uruguay as inferred from expert knowledge and from observed records to infer favourable locations, with favourability being the commensurable unit for both kinds of data sources. We compared the results for threatened species, species considered by experts to be ubiquitous, and non-threatened, non-ubiquitous species. We calculated the fuzzy intersection of models based on both knowledge sources to obtain a unified prediction of favourable locations. Models based on expert knowledge involved a larger number of variables and were less affected by sampling bias. Models based on experts had the same overprediction rate for the three types of species, whereas models based on species records had a lower prediction rate for ubiquitous species. Models based on expert knowledge performed equally as well or better than corresponding models based on species records for threatened species, even when they had to discriminate and classify the same set of records used to build the models based on species records. For threatened species, expert models predicted more restrictive favourable territories than those predicted based on records. Observed records generated the best-fitted models for non-threatened non-ubiquitous species, and ubiquitous species. Fuzzy modelling permitted the objective comparison of the potential of expert knowledge and incomplete distribution records to infer the territories favourable for different species. Distribution of threatened species was able to be better explained by subjective expert knowledge, while for generalist species models based on observed data were more accurate. These results have implications for the correct use of expert knowledge in conservation planning.

Project description:Recent advances in non-invasively collected samples have opened up new and exciting opportunities for wildlife research. Different types of samples, however, involve different limitations and certain physiological markers (e.g., C-peptide, oxytocin) can only be reliably measured from urine. Common collection methods for urine to date work best for arboreal animals and large volumes of urine. Sufficient recovery of urine is thus still difficult for wildlife biologists, particularly for terrestrial and small bodied animals. We tested three collection devices (two commercially available saliva swabs, Salivette synthetic and cotton, and cotton First aid swabs) against a control to permit the collection of small volumes of urine from the ground. We collected urine samples from captive and wild macaques, and humans, measured volume recovery, and analyzed concentrates of selected physiological markers (creatinine, C-peptide, and neopterin). The Salivette synthetic device was superior to the two alternative devices. Concentrations of creatinine, absolute C-peptide, C-peptide per creatinine, absolute neopterin, and neopterin per creatinine measured in samples collected with this device did not differ significantly from the control and were also strongly correlated to it. Fluid recovery was also best for this device. The least suitable device is the First aid collection device; we found that while absolute C-peptide and C-peptide per creatinine concentrations did not differ significantly from the control, creatinine concentrations were significantly lower than the control. In addition, these concentrations were either not or weakly correlated to the control. The Salivette cotton device provided intermediate results, although these concentrations were strongly correlated to the control. Salivette synthetic swabs seem to be useful devices for the collection of small amounts of urine from the ground destined for the assessment of physiological parameters. They thus provide new opportunities for field studies to incorporate physiological markers, particularly on smaller bodied and terrestrial animals and where urine collection is difficult.

Project description:Using existing data can be a reliable and cost-effective way to predict species distributions, and particularly useful for recovering or expanding species. We developed a current gray wolf (Canis lupus) distribution model for the western Great Lakes region, USA, and evaluated the spatial transferability of single-state models to the region. This study is the first assessment of transferability in a wide-ranging carnivore, as well as one of few developed for large spatial extents. We collected 3500 wolf locations from winter surveys in Minnesota (2017-2019), Wisconsin (2019-2020), and Michigan (2017-2020). We included 10 variables: proportion of natural cover, pastures, and crops; distance to natural cover, agriculture, developed land, and water; major and minor road density; and snowfall (1-km res.). We created a regional ensemble distribution by weight-averaging eight models based on their performance. We also developed single-state models, and estimated spatial transferability using two approaches: state cross-validation and extrapolation. We assessed performance by quantifying correlations, receiver operating characteristic curves (ROC), sensitivities, and two niche similarity indices. The regional area estimated to be most suitable for wolves during winter (threshold = maximum sensitivity/specificity) was 106,465 km2 (MN = 48,083 km2, WI = 27,757 km2, MI = 30,625 km2) and correctly predicted 88% of wolf locations analyzed. Increasing natural cover and distance to crops were consistently important for determining regional and single-state wolf distribution. Extrapolation (vs. cross-validation) produced results with the greatest performance metrics, and were most similar to the regional model, yet good internal performance was unrelated to greater extrapolation performance. Factors influencing species distributions are scale-dependent and can vary across areas due to behavioral plasticity. When extending inferences beyond the current occurrence of individuals, assessing variation in ecology such as habitat selection, as well as methodological factors including model performance, will be critical to avoid poor scientific interpretations and develop effective conservation applications. In particular, accurate distribution models for recovering or recovered carnivores can be used to develop plans for habitat management, quantify potential of unoccupied habitat, assess connectivity modeling, and mitigate conflict, facilitating long-term species persistence.

Dataset Information

Modeling the distribution of a wide-ranging invasive species using the sampling efforts of expert and citizen scientists.

Publications

Modeling the distribution of a wide-ranging invasive species using the sampling efforts of expert and citizen scientists.

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