A comparison of approaches for modelling the occurrence of marine animals

MacLeod, Colin D.; Mandleberg, Laura; Schweder, Caroline; Bannon, Sarah M.; Pierce, Graham J.

doi:10.1007/978-1-4020-9141-4_3

Cited by 14 publications

(11 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Variables showing covariance > 0.8 were not included in the GAMs. The amount of survey effort in each grid cell was used as a weighting factor in the model, in order to both determine whether all habitat types had been adequately sampled and to further control for the risk of 'false' absences (MacLeod et al 2008). Output of the final GAM was evaluated following the GAMvelope approach (Torres et al 2008).…”

Section: Habitat Modelingmentioning

confidence: 99%

“…While presence/absence habitat models should be preferred to presence-only models (MacLeod et al 2008, Praca et al 2009), their interpretation can be strongly affected by false-absence bias (Gu & Swihart 2004). We particularly addressed the source of false-absence bias that leads to erroneous classification of habitat cells as non-habitat cells.…”

Section: Utm: Universal Transverse Mercatormentioning

confidence: 99%

See 1 more Smart Citation

Habitat preferences of two deep-diving cetacean species in the northern Ligurian Sea

Tepsich¹,

Rosso²,

Halpin³

et al. 2014

Mar. Ecol. Prog. Ser.

View full text Add to dashboard Cite

We used generalized additive models (GAMs) as exploratory habitat models for describing the distribution of 2 deep-diving species, Cuvier's beaked whale Ziphius cavirostris Cuvier, 1823 and sperm whale Physeter catodon Linnaeus, 1758, in the Pelagos Sanctuary (northwestern Mediterranean). We analyzed data collected from research surveys and whale-watching activities during summer months from 2004 to 2007. The dataset encompassed 147 Cuvier's beaked whale sightings and 52 sperm whale sightings. We defined and applied a post hoc workflow to the data, to minimize false absence bias arising from the unique ecology of the species and the lack of a dedicated sampling design. We calculated a novel topographic predictor, distance from the canyon axis, as a covariate for use in the habitat model. Given the complex topography of the area, the analysis was performed on a high-resolution spatial grid (1 km). Our methods allowed effective use of the non-dedicated sampling dataset for building habitat models of elusive and cryptic species (Cuvier's beaked whale final model sensitivity = 0.88 and specificity = 0.84; sperm whale final model sensitivity = 0.65 and specificity = 0.77). The GAM results confirmed the preference for submarine canyons for both species and also highlighted the importance of the deeper portion of the Ligurian basin, especially for Cuvier's beaked whale. Habitat overlap nevertheless is resolved by a well-defined spatial partitioning of the area, with sperm whale occupying the western part and Cuvier's beaked whale the central and eastern parts.

show abstract

Section: Habitat Modelingmentioning

confidence: 99%

Section: Utm: Universal Transverse Mercatormentioning

confidence: 99%

Habitat preferences of two deep-diving cetacean species in the northern Ligurian Sea

Tepsich¹,

Rosso²,

Halpin³

et al. 2014

Mar. Ecol. Prog. Ser.

View full text Add to dashboard Cite

show abstract

“…Platform-of-opportunity data, collected aboard industrial platforms (oil and gas, renewable energies, seismic exploration), commercial fishing vessels (López et al 2004), passenger ferries (Williams et al 2006, Kiszka et al 2007, MacLeod et al 2008 and by wildlife tour operators (Hauser et al 2006, Ingram et al 2007) are increasingly used in monitoring and for mitigation schemes of marine mammals (Evans & Hammond 2004). To date, such data remain underutilized because their analysis presents several challenges.…”

Section: Introductionmentioning

confidence: 99%

Harbour porpoise habitat preferences: robust spatio-temporal inferences from opportunistic data

Isojunno¹,

Matthiopoulos²,

Evans³

2012

Mar. Ecol. Prog. Ser.

View full text Add to dashboard Cite

“…We used 19 "bioclimatic" variables from the Worldclim dataset [36] and topographic variables viz., elevation, slope, aspect, flow direction, flow accumulation and compound topographic index (an index for water retaining capacity) from the Hydro-1K dataset [37]. The circular variable aspect is transformed into two components viz., cosine and sine of the angle in order to represent the east and north respectively [38]. All the raster coverages were resampled to a resolution of 30 arc seconds (approximately one square kilometer pixels).…”

Section: Methodsmentioning

confidence: 99%

Predicting the Potential Distribution and Conservation Needs of Travancore Flying Squirrel, Petinomys fuscocapillus, in Peninsular India and Sri Lanka, using GARP

Kumara

Suganthasakthivel

2011

Tropical Conservation Science

View full text Add to dashboard Cite

Petinomys fuscocapillus, Jerdon 1847,Travancore flying squirrel, is known to occur in the forests of India and Sri Lanka. In spite of extensive surveys very few individuals were recorded in the last century. For a better understanding of the possible range of distribution, the present study mapped the potential geographic distribution of the P. fuscocapillus from peninsular India and Sri Lanka. We utilized occurrence records of 32 confirmed sightings of P. fuscocapillus to model the species´potential geographic distribution by applying an ecological niche modelling (ENM) framework using Genetic Algorithm for Rule set Prediction (GARP). Results indicate that the modeled potential distribution of P. fuscocapillus in India is highly restricted to the narrow strip on western slope of the Western Ghats, and in Sri Lanka the predicted distribution is predominant in the lowlands of wet and intermediate zones. Further, about 88% of the modeled potential distribution range lies outside the protected area network and occupies lowland evergreen, semi-evergreen and its degraded forested stages. The narrow environmental niche of both known and modeled potential distribution and highly susceptible forests with less protection raise the need for conservation efforts and future studies.

show abstract

A comparison of approaches for modelling the occurrence of marine animals

Cited by 14 publications

References 30 publications

Habitat preferences of two deep-diving cetacean species in the northern Ligurian Sea

Habitat preferences of two deep-diving cetacean species in the northern Ligurian Sea

Harbour porpoise habitat preferences: robust spatio-temporal inferences from opportunistic data

Predicting the Potential Distribution and Conservation Needs of Travancore Flying Squirrel, Petinomys fuscocapillus, in Peninsular India and Sri Lanka, using GARP

Contact Info

Product

Resources

About