scholarly journals Fishing for Feral Cats in a Naturally Fragmented Rocky Landscape Using Movement Data

2021 ◽  
Vol 13 (23) ◽  
pp. 4925
Author(s):  
Sandra D. Williamson ◽  
Richard van Dongen ◽  
Lewis Trotter ◽  
Russell Palmer ◽  
Todd P. Robinson
Keyword(s):  
Species Distribution ◽  
Species Distribution Models ◽  
Brownian Bridge ◽  
Control Strategies ◽  
Population Level ◽  
Home Ranges ◽  
Distribution Models ◽  
Control Effort ◽  
Feral Cats ◽  
Movement Data

Feral cats are one of the most damaging predators on Earth. They can be found throughout most of Australia’s mainland and many of its larger islands, where they are adaptable predators responsible for the decline and extinction of many species of native fauna. Managing feral cat populations to mitigate their impacts is a conservation priority. Control strategies can be better informed by knowledge of the locations that cats frequent the most. However, this information is rarely captured at the population level and therefore requires modelling based on observations of a sample of individuals. Here, we use movement data from collared feral cats to estimate home range sizes by gender and create species distribution models in the Pilbara bioregion of Western Australia. Home ranges were estimated using dynamic Brownian bridge movement models and split into 50% and 95% utilisation distribution contours. Species distribution models used points intersecting with the 50% utilisation contours and thinned by spacing points 500 m apart to remove sampling bias. Male cat home ranges were between 5 km2 (50% utilisation) and 34 km2 (95% utilisation), which were approximately twice the size of the female cats studied (2–17 km2). Species distribution modelling revealed a preference for low-lying riparian habitats with highly productive vegetation cover and a tendency to avoid newly burnt areas and topographically complex, rocky landscapes. Conservation management can benefit by targeting control effort in preferential habitat.

Habitat suitability, live abundance and their link to road mortality of Tasmanian wildlife

2019 ◽  
Vol 46 (3) ◽  
pp. 236
Author(s):  
Hanh K. D. Nguyen ◽  
Matthew W. Fielding ◽  
Jessie C. Buettel ◽  
Barry W. Brook
Keyword(s):  
Species Distribution ◽  
Species Distribution Models ◽  
Geographical Location ◽  
Population Level ◽  
Road Mortality ◽  
Distribution Models ◽  
Live Animal ◽  
The Road ◽  
Animal Abundance ◽  
Climate Suitability

ContextTasmania has been called the roadkill capital of Australia. However, little is known about the population-level impact of vehicle mortality on native mammals in the island state. AimsThe aims were to investigate the predictability of roadkill on a given route, based on models of species distribution and live animal abundance for three marsupial species in Tasmania – the Tasmanian pademelon (Thylogale billardierii), Bennett’s wallaby (Macropus rufogriseus) and the bare-nosed wombat (Vombatus ursinus) – and to assess the possibility of predicting the magnitude of state-wide road mortality based on live animal abundance. MethodsRoad mortality of the three species was measured on eight 15-km road segments in south-eastern Tasmania, during 16 weeks over the period 2016–17. Climate suitability was predicted using state-wide geographical location records, using species distribution models, and counts of these species from 190 spotlight survey roads. Key resultsThe Tasmanian pademelons were the most frequently killed animal encountered over the study period. Live abundance, predicted by fitting models to spotlight counts, did not correlate with this fatality rate for any species. However, the climate suitability index generated by the species distribution models was strongly predictive for wombat roadkill, and moderately so for pademelons. ConclusionsAlthough distributional and wildlife abundance records are commonly available and well described by models based on climate, vegetation and land-use predictors, this approach to climate suitability modelling has limited predictability for roadkill counts on specific routes. ImplicationsRoad-specific factors, such as characteristics of the road infrastructure, nearby habitats and behavioural traits, seem to be required to explain roadkill frequency. Determining their relative importance will require spatial analysis of roadkill locations.

scholarly journals A Robust Prediction Model for Species Distribution Using Bagging Ensembles with Deep Neural Networks

2021 ◽  
Vol 13 (8) ◽  
pp. 1495
Author(s):  
Jehyeok Rew ◽  
Yongjang Cho ◽  
Eenjun Hwang
Keyword(s):  
Neural Networks ◽  
Species Distribution ◽  
Best Practice ◽  
Deep Neural Networks ◽  
Species Distribution Models ◽  
Species Distribution Model ◽  
Environmental Data ◽  
Distribution Model ◽  
Distribution Models ◽  
Robust Prediction

Species distribution models have been used for various purposes, such as conserving species, discovering potential habitats, and obtaining evolutionary insights by predicting species occurrence. Many statistical and machine-learning-based approaches have been proposed to construct effective species distribution models, but with limited success due to spatial biases in presences and imbalanced presence-absences. We propose a novel species distribution model to address these problems based on bootstrap aggregating (bagging) ensembles of deep neural networks (DNNs). We first generate bootstraps considering presence-absence data on spatial balance to alleviate the bias problem. Then we construct DNNs using environmental data from presence and absence locations, and finally combine these into an ensemble model using three voting methods to improve prediction accuracy. Extensive experiments verified the proposed model’s effectiveness for species in South Korea using crowdsourced observations that have spatial biases. The proposed model achieved more accurate and robust prediction results than the current best practice models.

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