Applying species distribution modelling for the conservation of the threatened saproxylic Stag Beetle (Lucanus cervus)

Thomaes, Arno; Kervyn, Thierry; Maes, Dirk

doi:10.1016/j.biocon.2008.03.018

Cited by 79 publications

(78 citation statements)

References 65 publications

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“…BRTs generally avoid overfitting [35], can be applied to a variety of spatial analyses, such as the distribution of species and vegetation types [36][37][38][39][40][41][42], hydrology [43,44], soil and landform properties [45][46][47] and natural disturbance [48], as well as quantification of land cover and land use change through human activities [34,49,50]. Across a wide variety of contexts, model comparisons have shown BRTs to perform much better than traditional models and comparably well to other machine-learning models [36,37,43,45,48,51], with some variability in comparative performance with other machine-learning methods depending on context [33,38,44,47].…”

Section: Discussionmentioning

confidence: 99%

Patterns and Predictors of Recent Forest Conversion in New England

Thorn

Thompson

Plisinski

2016

Land

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New England forests provide numerous benefits to the region's residents, but are undergoing rapid development. We used boosted regression tree analysis (BRT) to assess geographic predictors of forest loss to development between 2001 and 2011. BRT combines classification and regression trees with machine learning to generate non-parametric statistical models that can capture non-linear relationships. Based on National Land Cover Database (NLCD) maps of land cover change, we assessed the importance of the biophysical and social variables selected for full region coverage and minimal collinearity in predicting forest loss to development, specifically: elevation, slope, distance to roads, density of highways, distance to built land, distance to cities, population density, change in population density, relative change in population density, population per housing unit, median income, state, land ownership categories and county classification as recreation or retirement counties. The resulting models explained 6. . From our models, we generated high-resolution probability surfaces, which can provide a key input for simulation models of forest and land cover change.

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Section: Discussionmentioning

confidence: 99%

Patterns and Predictors of Recent Forest Conversion in New England

Thorn

Thompson

Plisinski

2016

Land

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“…Knowledge of geographical distributions and habitat preferences are central to the conservation and population management of threatened species Thomaes et al, 2008). Ecological niche models have been widely applied in conservation biology to predict the potential distributions of species using correlative models that exploit a species-environment relationship (Anderson et al, 2009;Chefaoui et al, 2005;De Siqueira et al, 2009;Peterson, 2001;Raxworthy et al, 2007;Rushton et al, 2004;Titeux et al, 2007).…”

Section: Introductionmentioning

confidence: 99%

Predicting the potential distribution of an endangered cryptic subterranean mammal from few occurrence records

Jackson

Robertson

2011

Journal for Nature Conservation

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“…This saproxylic species is often considered as an umbrella species, representing the large saproxylic diversity inhabiting forests and half open landscapes (Luce 1996;Thomaes et al 2008). The stag beetle is included in the second annex of the European Habitats Directive and consequently, species specific protection and monitoring is mandatory for every member state.…”

Section: Introductionmentioning

confidence: 99%

“…Even then, the species remains mostly hidden underground, being active during a short period around sunset (Campanaro et al 2016;Rink and Sinsch 2007). Furthermore, the activity of adults strongly depends on temperature (Harvey et al 2011a;Rink and Sinsch 2011;Sprecher-Uebersax 2001;Thomaes et al 2008). Finally, relative humidity, rain, wind speed and other weather variables have been found to influence the stag beetles' activity (e.g.…”

Section: Introductionmentioning

confidence: 99%

Can we successfully monitor a population density decline of elusive invertebrates? A statistical power analysis on Lucanus cervus

Thomaes¹,

Verschelde²,

Mader³

et al. 2017

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Monitoring global biodiversity is essential for understanding and countering its current loss. However, monitoring of many species is hindered by their difficult detection due to crepuscular activity, hidden phases of the life cycle, short activity period and low population density. Few statistical power analyses of declining trends have been published for terrestrial invertebrates. Consequently, no knowledge exists of the success rate of monitoring elusive invertebrates. Here data from monitoring transects of the European stag beetle, Lucanus cervus, is used to investigate whether the population trend of this elusive species can be adequately monitored. Data from studies in UK, Switzerland and Germany were compiled to parameterize a simulation model explaining the stag beetle abundance as a function of temperature and seasonality. A Monte-Carlo simulation was used to evaluate the effort needed to detect a population abundance decline of 1%/year over a period of 12 years. To reveal such a decline, at least 240 1-hour transect walks on 40 to 100 transects need to be implemented in weekly intervals during warm evenings. It is concluded that monitoring of stag beetles is feasible and the effort is not greater than that which has been found for other invertebrates. Based on this example, it is assumed that many other elusive species with similar life history traits can be monitored with moderate efforts. As saproxylic invertebrates account for a large share of the forest biodiversity, although many are elusive, it is proposed that at least some flagship species are included in monitoring programmes.

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Applying species distribution modelling for the conservation of the threatened saproxylic Stag Beetle (Lucanus cervus)

Cited by 79 publications

References 65 publications

Patterns and Predictors of Recent Forest Conversion in New England

Patterns and Predictors of Recent Forest Conversion in New England

Predicting the potential distribution of an endangered cryptic subterranean mammal from few occurrence records

Can we successfully monitor a population density decline of elusive invertebrates? A statistical power analysis on Lucanus cervus

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