2020
DOI: 10.3389/fmars.2020.00277
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Habitat Partitioning in Sympatric Delphinids Around the Falkland Islands: Predicting Distributions Based on a Limited Data Set

Abstract: Habitat Partitioning in Sympatric Delphinids show the usefulness of such refinements applied to a carefully chosen spatially limited dataset as a cost-effective approach to elucidating species distribution patterns. Our methodology and software implementations can be easily applied to transect survey data of other marine and terrestrial taxa.

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Cited by 9 publications
(13 citation statements)
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“…Similar findings were obtained when the habitat preferences of Commerson's dolphins were modelled at the regional scale (i.e., meso-scale), which indicates that the distance to the mouth of the river is one of the most influential variables for this species (Franchini et al, 2020;Garaffo et al, 2011;Righi et al, 2013). Additionally, previous studies that analyzed the Commerson's dolphin distribution along the Patagonian shelf (farther away from the coast)…”
supporting
confidence: 79%
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“…Similar findings were obtained when the habitat preferences of Commerson's dolphins were modelled at the regional scale (i.e., meso-scale), which indicates that the distance to the mouth of the river is one of the most influential variables for this species (Franchini et al, 2020;Garaffo et al, 2011;Righi et al, 2013). Additionally, previous studies that analyzed the Commerson's dolphin distribution along the Patagonian shelf (farther away from the coast)…”
supporting
confidence: 79%
“…These results broadly concur with previously published information on the habitat preference of the Commerson's dolphins, which have highlighted the prominent relationship between this species and geographic and bathymetric features (Goodall, 1994). This study also provides evidence that the same features that condition the distribution of Commerson's dolphins on a regional scale are those that rule its distribution on a small scale (Dellabianca et al, 2016;Franchini et al 2020;Garaffo et al 2011). From an analytical point of view, using the INLA framework to model habitat preferences also proves to be a powerful tool to analyze data originating from opportunistic or nonsystematic sampling such as haphazard (Corkeron et al, 2011).…”
mentioning
confidence: 76%
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“…Despite this mismatch in scale between proposed MMAs (which necessarily aim to balance ecological, economic, and social factors), and the scale at which wide-ranging species used the marine environment, the proposed MMAs encapsulate areas around breeding colonies where animals are known to congregate, and the foraging areas of a number of other species that were not included in our study, but are known to forage nearshore. For example, Rock Shags (Phalacrocorax magellanicus) breeding at the Falkland Islands travel an average distance from land of 0.4 km, and a maximum distance of 3.6 km, Falkland Steamer Ducks are likely to spend the majority of their annual cycle within the inshore MMA, and similarly, Peales dolphins (Lagenorhynchus australis) are thought to spend a high proportion of their time within nearshore coastal waters (Aug e et al 2018, Franchini et al 2020. The proposed inshore MMA also includes much of the FIG.…”
Section: Discussionmentioning
confidence: 99%
“…They inhabit the Southern Hemisphere with partially sympatric distributions overlapping in different areas of their geographic ranges but differing in their habitat preferences. The former two species are considered coastal associated with complex habitats where foraging strategies require greater maneuverability; and accordingly, they show plasticity in their prey preference (see Goodall, 1994;Goodall et al, 1997b,c;Coscarella et al, 2010;Riccialdelli et al, 2010;Garraffo et al, 2011;Dellabianca et al, 2016;Franchini et al, 2020). The latter two species are considered fast-swimming cooperative feeders that forage on pelagic prey exhibiting long-distance displacements that reflect prey availability in pelagic habitats (see Würsig and Würsig, 1980;Goodall et al, 1997a;Schiavini et al, 1999;Riccialdelli et al, 2010;Degrati et al, 2012).…”
Section: Introductionmentioning
confidence: 99%