Rarity and beta diversity assessment as tools for guiding conservation strategies in marine tropical subtidal communities

Carlos-Júnior, Lélis Antonio; Spencer, Matthew; Neves, Danilo M.; Moulton, Timothy P.; Pires, Débora O.; Castro, Clovis B.; Ventura, Carlos Renato Rezende; Ferreira, Carlos E. L.; Serejo, Cristiana S.; Oigman-Pszczol, Simone Siag; Casares, Fernanda Araújo; Mantelatto, Marcelo Checoli; Creed, Joel Christopher

doi:10.1111/ddi.12896

Cited by 20 publications

(16 citation statements)

References 55 publications

(66 reference statements)

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“…Differentiation along the depth gradient is usually found in HBBC on tropical rocky reefs (e.g., Matheus et al, 2019; Mendonça‐Neto, Ferreira, Chaves, & Pereira, 2008; Oigman‐Pszczol, Figueiredo, & Creed, 2004) with a tendency for phototrophic organisms to decrease and heterotrophs to increase in abundance with depth (e.g.,Magalhães et al, 2015; Matheus et al, 2019). Depth was also one of the most important factors that explained overall marine species variation (Carlos‐Júnior et al, 2019), fish assemblage variation (Teixeira‐Neves, Neves, & Araújo, 2015), and that most structured Cnidaria and Echinodermata assemblages (Castro, Echeverría, Pires, & Fonseca, 1999) in the region.…”

Section: Discussionmentioning

confidence: 99%

“…Map coordinates were fed into a global positioning system and used to arrive at the sample sites in the field. Numbers and names of the sites used here are the same as used in Creed et al (2007) and in Carlos‐Júnior et al (2019), although five sites (21, 24, 36, 42, and 43) were not included here as they were not sampled quantitatively due to logistical limitations.…”

Section: Methodsmentioning

confidence: 99%

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Depth and grazing intensity are the main drivers of subtidal hardground benthic community structure on tropical south Atlantic reefs

Mantelatto

Oliveira

Menegola³

et al. 2020

Marine Ecology

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Marine hardground bottom (subtidal) benthic (sessile) communities (HBBC) are subjected to various biotic, abiotic, and anthropogenic factors that can vary over space and time and interact with one another. To evaluate the main drivers of the structure of HBBC in a large tropical bay in the southwest Atlantic (Ilha Grande Bay, Brazil), a total of 98 HBBC were sampled using visually estimated quadrats. Ten variables (depth, grazing intensity, irradiance, temperature, salinity, dissolved oxygen, nutrients (nitrite, nitrate, and phosphate), and chlorophyll a concentrations) were also measured. A total of 74 main space‐occupying taxon/functional groups were recorded which fell into six major HBBC groups. These were organized by depth/irradiance and grazing intensities which explained part of the HBBC variation: (a) Communities dominated by zoantharians occurred in the shallow, highest irradiance sites; (b) foliose and erect algae were inversely related to depth and grazing intensity; (c) crustose coralline algae dominated at greater depths and/or under higher grazing intensity; (d) zooxanthellate corals were associated with shallower and heterotrophic invertebrates with deeper stations; (e) photosynthetically efficient algae were associated with deeper stations, with more encrusting forms under higher grazing intensities; and (f) multi‐species turf‐forming algae were ubiquitous (did not present association with any of the measured variables). As communities’ components and main structuring factors were similar to those found in coral reefs, these communities represent compatible receptor habitats for poleward escape, migration, and range extension under the present global warming scenario.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Depth and grazing intensity are the main drivers of subtidal hardground benthic community structure on tropical south Atlantic reefs

Mantelatto

Oliveira

Menegola³

et al. 2020

Marine Ecology

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“…We compared the two approaches to spatial variable selection using simulated community data based on three real community composition datasets with a range of properties: 1. Presence/Absence of 110 marine benthic macroalgae species from a Rapid Assessment Program for biodiversity of 42 sample sites spanning roughly 2,000 km 2 at Ilha Grande Bay, Rio de Janeiro, Brazil (southwest Atlantic) (Carlos-Júnior et al, 2019), permit number IBAMA/RJ:031/04); 2. Presence/Absence of 588 plant species from grassland covering 500 km 2 of Scotland's coast.…”

Section: Baseline Datasetsmentioning

confidence: 99%

“…Therefore, for dataset 1 we chose the minimum spanning tree (B) with Euclidian linear distances as weights (A). Our decision was based on the shape of the bay and the fact that the main water movements make the sampling sites geographically compartmentalised in subregions where sites are likely to be minimally connected (Carlos-Júnior et al, 2019). Similarly, spatial organisation in dataset 2 could be sensibly described in terms of Delaunay Figure 1 Schematic diagram of the main steps used in this study to simulate community presence/absence data with pre-defined spatial structure.…”

Section: Baseline Datasetsmentioning

confidence: 99%

Generalized Linear Models outperform commonly used canonical analysis in estimating spatial structure of presence/absence data

Carlos-Júnior

Creed

Marrs

et al. 2020

PeerJ

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Background Ecological communities tend to be spatially structured due to environmental gradients and/or spatially contagious processes such as growth, dispersion and species interactions. Data transformation followed by usage of algorithms such as Redundancy Analysis (RDA) is a fairly common approach in studies searching for spatial structure in ecological communities, despite recent suggestions advocating the use of Generalized Linear Models (GLMs). Here, we compared the performance of GLMs and RDA in describing spatial structure in ecological community composition data. We simulated realistic presence/absence data typical of many β-diversity studies. For model selection we used standard methods commonly used in most studies involving RDA and GLMs. Methods We simulated communities with known spatial structure, based on three real spatial community presence/absence datasets (one terrestrial, one marine and one freshwater). We used spatial eigenvectors as explanatory variables. We varied the number of non-zero coefficients of the spatial variables, and the spatial scales with which these coefficients were associated and then compared the performance of GLMs and RDA frameworks to correctly retrieve the spatial patterns contained in the simulated communities. We used two different methods for model selection, Forward Selection (FW) for RDA and the Akaike Information Criterion (AIC) for GLMs. The performance of each method was assessed by scoring overall accuracy as the proportion of variables whose inclusion/exclusion status was correct, and by distinguishing which kind of error was observed for each method. We also assessed whether errors in variable selection could affect the interpretation of spatial structure. Results Overall GLM with AIC-based model selection (GLM/AIC) performed better than RDA/FW in selecting spatial explanatory variables, although under some simulations the methods performed similarly. In general, RDA/FW performed unpredictably, often retaining too many explanatory variables and selecting variables associated with incorrect spatial scales. The spatial scale of the pattern had a negligible effect on GLM/AIC performance but consistently affected RDA’s error rates under almost all scenarios. Conclusion We encourage the use of GLM/AIC for studies searching for spatial drivers of species presence/absence patterns, since this framework outperformed RDA/FW in situations most likely to be found in natural communities. It is likely that such recommendations might extend to other types of explanatory variables.

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“…Therefore, for dataset A we chose the minimum spanning tree (B) with Euclidian linear distances as weights (A). Our decision was based on the shape of the bay and the fact that the main water movements make the sampling sites geographically compartmentalised in subregions where sites are likely to be minimally connected (Carlos-Júnior et al, 2019). Similarly, spatial organisation in dataset B could be sensibly described in terms of Delaunay triangulation (B) with Euclidian weights (A).…”

Section: Baseline Datasetsmentioning

confidence: 99%

Peer Review #1 of "Generalized Linear Models outperform commonly used canonical analysis in estimating spatial structure of presence/absence data (v0.1)"

Dray¹

2020

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Background. Ecological communities tend to be spatially structured due to environmental gradients and/or spatially contagious processes such as growth, dispersion and species interactions. Data transformation followed by usage of algorithms such as Redundancy Analysis (RDA) is a fairly common approach in studies searching for spatial structure in ecological communities, despite recent suggestions advocating the use of Generalized Linear Models (GLMs). Here, we compared the performance of GLMs and RDA in describing spatial structure in ecological community composition data. We simulated realistic presence/absence data typical of many β-diversity studies. For model selection we used standard methods commonly used in most studies involving RDA and GLMs. Methods. We simulated communities with known spatial structure, based on three real spatial community presence/absence datasets (one terrestrial, one marine and one freshwater). We used spatial eigenvectors as explanatory variables. We varied the number of non-zero coefficients of the spatial variables, and the spatial scales with which these coefficients were associated and then compared the performance of GLMs and RDA frameworks to correctly retrieve the spatial patterns contained in the simulated communities. We used two different methods for model selection, Forward Selection (FW) for RDA and the Akaike Information Criterion (AIC) for GLMs. The performance of each method was assessed by scoring overall accuracy as the proportion of variables whose inclusion/exclusion status was correct, and by distinguishing which kind of error was observed for each method. We also assessed whether errors in variable selection could affect the interpretation of spatial structure. Results. Overall GLM with AIC-based model selection (GLM/AIC) performed better than RDA/FW in selecting spatial explanatory variables, although under some simulations the methods performed similarly. In general, RDA/FW performed unpredictably, often retaining too many explanatory variables and selecting variables associated with incorrect spatial scales. The spatial scale of the pattern had a negligible effect on GLM/AIC performance but consistently affected RDA's error rates under almost all scenarios. Conclusion. We encourage the use of GLM/AIC for studies searching for spatial drivers of species presence/absence patterns, since this framework outperformed RDA in situations most likely to be found

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Rarity and beta diversity assessment as tools for guiding conservation strategies in marine tropical subtidal communities

Cited by 20 publications

References 55 publications

Depth and grazing intensity are the main drivers of subtidal hardground benthic community structure on tropical south Atlantic reefs

Depth and grazing intensity are the main drivers of subtidal hardground benthic community structure on tropical south Atlantic reefs

Generalized Linear Models outperform commonly used canonical analysis in estimating spatial structure of presence/absence data

Peer Review #1 of "Generalized Linear Models outperform commonly used canonical analysis in estimating spatial structure of presence/absence data (v0.1)"

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