Maximum entropy niche-based modelling of seasonal changes in little bustard (Tetrax tetrax) distribution

Suárez‐Seoane, Susana; Morena, Eladio L. García de la; Prieto, Manuel B. Morales; Osborne, Patrick E.; Juana, Eduardo de

doi:10.1016/j.ecolmodel.2008.07.035

Cited by 86 publications

(51 citation statements)

References 53 publications

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“…In such predictions, year-to-year variability is averaged out. While there has been some work on seasonal models (Suárez-Seoane et al 2008), we believe our work to be the first application of a species distribution model on the weekly timescale. (Table 2) suggest that the habitat preferences of right whales are not static.…”

Section: Discussionmentioning

confidence: 99%

Weekly predictions of North Atlantic right whale Eubalaena glacialis habitat reveal influence of prey abundance and seasonality of habitat preferences

Pendleton¹,

Sullivan²,

Brown³

et al. 2012

Endang. Species. Res.

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Primary sources of mortality and serious injury to endangered North Atlantic right whales Eubalaena glacialis are vessel strikes and entanglement in fishing gear. All management plans depend on knowing when and where right whales are likely to be present. We tested the feasibility of a system designed to predict potential right whale habitat on a weekly time scale. The system paired right whale occurrence records with a collection of data layers including: results from a coupled biological−physical model of Calanus finmarchicus (the primary prey of right whales), satellite-derived sea surface temperature and chlorophyll, and bathymetry. Using these data, we trained seasonal habitat models and projected them onto environmental data for each 8 d period from January to June, 2002 to 2006. Two hypotheses were tested: (1) that right whale environmental preferences change from season to season and (2) that modeled prey concentration is an important predictor of the distribution of right whales. To test H 1 , we trained, tested, and compared models for 3 time periods: winter, spring, and winter and spring combined. To test H 2 , we trained and tested models with and without C. finmarchicus. Predictions of habitat suitability were highly dynamic within and across years. Our results support the hypothesis that right whale environmental preferences change between winter and spring. The inclusion of modeled C. finmarchicus abundance improved the accuracy of habitat suitability predictions.

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Section: Discussionmentioning

confidence: 99%

Weekly predictions of North Atlantic right whale Eubalaena glacialis habitat reveal influence of prey abundance and seasonality of habitat preferences

Pendleton¹,

Sullivan²,

Brown³

et al. 2012

Endang. Species. Res.

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“…Suárez-Seoane et al 2008) and to forecast distribution into novel areas (e.g. Verbruggen et al 2013).…”

Section: Seasonal Redistributionmentioning

confidence: 99%

Seasonal variations of wild boar Sus scrofa distribution in agricultural landscapes: a species distribution modelling approach

Мorelle

Lejeune

2014

Eur J Wildl Res

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Seasonal changes in the distribution of animals can have an important impact on the ecosystem. In agricultural landscapes, the seasonal movement of the wild boar Sus scrofa towards field crops often results in damages and makes control of the population difficult. The plasticity of the species to cope with different habitats is largely proven, but the environmental and human drivers of this seasonal habitat shift at the population scale remain largely unknown. Using MaxEnt and two seasonally distinct presence datasets, we contrasted the distribution of wild boar in southern Belgium during the growing and hunting seasons to (i) analyse seasonal drivers of the distribution and (ii) forecast the potential spread of the species north to its current distribution. We demonstrated that during the growing season, wild boar range almost double, owing to the cover and food provided by agricultural areas, thereby enhancing the movement and spread of the species. We found that the seasonal distribution of wild boar in agricultural lands is mostly influenced by the search for cover and food provided alternatively by forest and field crops. Interestingly, it seems that this search for cover operates under the constraint of a threshold distance. Our results indeed reveal an increased probability of presence not only in the vicinity of forests but also above a threshold distance of 865 m from the forest edge, suggesting that wild boar can overcome the dependence to forest cover. The forecast distribution of wild boar highlighted a potential increase of the current range into suitable habitat between 63 and 168 km 2 . To counteract the potential spread of the species into agricultural habitats and the consecutive damages, we insist on the need for the development of integrated management strategies, combining land use spatial configuration and wild boar spatial behaviour.

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“…Nonetheless, commission errors tend to decrease while omission errors subsequently increase as the threshold increases [4,14]. As such, threshold levels are typically selected to minimize the error term that is most detrimental to the model objectives (e.g., [16,20,31,32]). However, this still involves the somewhat arbitrary selection of a threshold level; a more consistent or more clearly defined selection approach would provide greater consistency in model output and is worthy of further investigation.…”

Section: Threshold Developmentmentioning

confidence: 99%

Use of Maximum Entropy Modeling in Wildlife Research

Baldwin

2009

Entropy

601

415

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Maximum entropy (Maxent) modeling has great potential for identifying distributions and habitat selection of wildlife given its reliance on only presence locations. Recent studies indicate Maxent is relatively insensitive to spatial errors associated with location data, requires few locations to construct useful models, and performs better than other presence-only modeling approaches. Further advances are needed to better define model thresholds, to test model significance, and to address model selection. Additionally, development of modeling approaches is needed when using repeated sampling of known individuals to assess habitat selection. These advancements would strengthen the utility of Maxent for wildlife research and management.

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Maximum entropy niche-based modelling of seasonal changes in little bustard (Tetrax tetrax) distribution

Cited by 86 publications

References 53 publications

Weekly predictions of North Atlantic right whale Eubalaena glacialis habitat reveal influence of prey abundance and seasonality of habitat preferences

Weekly predictions of North Atlantic right whale Eubalaena glacialis habitat reveal influence of prey abundance and seasonality of habitat preferences

Seasonal variations of wild boar Sus scrofa distribution in agricultural landscapes: a species distribution modelling approach

Use of Maximum Entropy Modeling in Wildlife Research

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