Environmental factors that influence the distribution of coral reef fishes: modeling occurrence data for broad-scale conservation and management

Beger, Maria; Possingham, Hugh P.

doi:10.3354/meps07481

Cited by 54 publications

(43 citation statements)

References 52 publications

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“…In our case, we found that using aerial photos led to overprediction of gypsum springmound sites, and it was only through remotely sensed LANDSAT data that we were able to better predict gypsophyte locality. Remote sensing has previously been shown to be critical in conservation planning for species with distinct habitat requirements (Sellars and Jolls 2007;Beger and Possingham 2008). Remote sensing data also provide an excellent starting point for identifying areas in need of inventory.…”

Section: Useful Predictors In Small-scale Species Distribution Modelingmentioning

confidence: 99%

Species Distribution Modeling for Conservation of Rare, Edaphic Endemic Plants in White River Valley, Nevada

Queiroz

Baughman

et al. 2012

Natural Areas Journal

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Section: Useful Predictors In Small-scale Species Distribution Modelingmentioning

confidence: 99%

Species Distribution Modeling for Conservation of Rare, Edaphic Endemic Plants in White River Valley, Nevada

Queiroz

Baughman

et al. 2012

Natural Areas Journal

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“…Similar hydrodynamic influences on reef fishes have been demonstrated in other volcanic archipelagos (e.g. Friedlan-der et al 2003, Beger & Possingham 2008, Krajewski & Floeter 2011, Richards et al 2012. Pais et al (2007) reported significantly higher abundance of carnivorous labrids and serranids in sheltered habitats in the Mediterranean, whereas De Raedemaecker et al (2010) did not find a significant difference for these guilds.…”

Section: Exposure-related Effectsmentioning

confidence: 57%

“…Friedlander et al 2003, Beger & Possingham 2008, De Raedemaecker et al 2010, Krajewski & Floeter 2011 ever, it also shows that the species' trophic ecology shapes such relationships differently. In agreement with other studies, carnivores and planktivores respond positively to bathymetry (Fried lander & Parrish 1998, Bertoncini et al 2010, Krajewski & Floeter 2011, whereas herbivores and omnivores are also more abundant in shallow and intermediate depths (Friedlander & Parrish 1998, Nemeth & Appeldoorn 2009).…”

Section: Environmental Predictors That Influence the Distribution Ofmentioning

confidence: 99%

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Predictive habitat modelling of reef fishes with contrasting trophic ecologies

Schmiing¹,

Afonso²,

Tempera³

et al. 2013

Mar. Ecol. Prog. Ser.

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The success of marine spatial management and, in particular, the zonation of marine protected areas (MPAs), largely depends on the good understanding of species' distribution and habitat preferences. Yet, detailed knowledge of fish abundance is often reduced to a few sampled locations and a reliable prediction of this information across broader geographical areas is of major relevance. Generalised additive models (GAMs) were used to describe species− environment relationships and identify environmental parameters that determine the abundance or presence−absence of 11 reef fishes with contrasting life histories in shallow habitats of the Azores islands, Northeast Atlantic. Predictive models were mapped and visualised in a geographic information system (GIS) and areas with potential single or multi-species habitat hotspots were identified. Schooling, pelagic species typically required presence−absence models, whereas abundance models performed well for benthic species. Depth and distance to sediment significantly described the distribution for nearly all species, whereas the influence of exposure to swell or currents and slope of the seafloor depended on their trophic ecology. Potential presence of single species was widespread across the study area but much reduced for multiple species. There were no habitats shared by high abundances of all species in a given trophic group, and areas shared by minimal abundances were smaller than expected. Potential habitat hotspots should be considered as priority sites for conservation, but were only partially included in the existing MPA network. These findings highlight the potential of this methodology to support scientifically sound conservation planning, including but not restricted to fragmented and constrained habitats, such as those of oceanic archipelagos.

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“…Because collecting biological data is labour intensive, difficult and expensive for the large areas relevant to management, these data are often limited and patchy. Where biological data are limited or lacking, predicting patterns of biodiversity using relationships between biodiversity and physical variables can facilitate conservation decision-making (Lehmann et al 2002, Beger & Possingham 2008, Foster & Dunstan 2010.…”

Section: Resale or Republication Not Permitted Without Written Consenmentioning

confidence: 99%

Quantifying wave exposure in shallow temperate reef systems: applicability of fetch models for predicting algal biodiversity

Hill¹,

Pepper²,

Puotinen³

et al. 2010

Mar. Ecol. Prog. Ser.

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Management and conservation of ecosystems relies on biodiversity data; however, broad-scale biological data are often limited. Predictive modelling using environmental variables has recently proven a valuable tool in addressing this gap. Wave exposure is a particularly important environmental variable that structures shallow reef systems, but it is rarely quantified across the large areas often used for predictive studies. Therefore, we investigated approaches that quantify exposure and can be readily applied across a large area. We generated 6 quantitative indices that emphasise different aspects of exposure using a numerical wave model and cartographic fetch models. The utility of these indices for predictive modelling in shallow temperate reef systems was assessed by how well they explained community and genera-level algal patterns in Tasmania, Australia, which is a region that experiences a wide range of wave exposure conditions. Exposure indices were significant predictors of algal patterns, explaining up to 18% of community level patterns and up to 37% of the variance associated with the occurrence and cover of algal genera. Fetch-based indices in particular appear to be a viable option for quantifying exposure on shallow reefs. These indices can be generated within a Geographic Information System (GIS) program for specific sites of interest, along coastlines or on a grid, and are potentially accessible to ecologists. Quantification of exposure across broad regions using fetch indices will allow ecologists to makes advances in predictive modelling studies, but also facilitate studies that test the generality of hypotheses and mechanisms driving patterns previously observed using qualitative measures.KEY WORDS: Wave exposure index · Cartographic fetch modelling · Macro-algae · Biodiversity · Predictive models Resale or republication not permitted without written consent of the publisherMar Ecol Prog Ser 417: [83][84][85][86][87][88][89][90][91][92][93][94][95] 2010 tion of shallow reef communities requires knowledge of biodiversity patterns across large regions. Because collecting biological data is labour intensive, difficult and expensive for the large areas relevant to management, these data are often limited and patchy. Where biological data are limited or lacking, predicting patterns of biodiversity using relationships between biodiversity and physical variables can facilitate conservation decision-making (Lehmann et al. 2002, Beger & Possingham 2008, Foster & Dunstan 2010.A range of physical data is now available across large regions to aid in the development of predictive models for the marine environment. These include data on sea surface temperature or ocean colour from satellites (e.g. MODIS and SeaWiFS) or Argo floats, ocean chemistry measurements (e.g. CSIRO Atlas of Regional Seas, CARS; Condie & Dunn 2006), bathymetry (e.g. Whiteway 2009) and sediment grain-size data (e.g. Marine Sediment database, MARS; Geoscience Australia). While these physical data have proven useful for ecosys...

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Environmental factors that influence the distribution of coral reef fishes: modeling occurrence data for broad-scale conservation and management

Cited by 54 publications

References 52 publications

Species Distribution Modeling for Conservation of Rare, Edaphic Endemic Plants in White River Valley, Nevada

Species Distribution Modeling for Conservation of Rare, Edaphic Endemic Plants in White River Valley, Nevada

Predictive habitat modelling of reef fishes with contrasting trophic ecologies

Quantifying wave exposure in shallow temperate reef systems: applicability of fetch models for predicting algal biodiversity

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