DOI: 10.5061/dryad.xwdbrv1bd
title: The role of island physiography and oceanographic factors in shaping species richness and turnover of nesting seabird assemblages on islands across the southeastern Pacific

description: For seabirds, food supplies and nest sites are largely driven by
 oceanographic gradients and island habitats, respectively. Research into
 seabirds’ ecological roles in insular ecosystems is crucial to
 understanding processes that structure seabird nesting assemblages. We
 examined the influence of island physiography and oceanographic factors on
 the spatial variation in α and β-diversity of nesting seabird assemblages.
 Location Southeastern Pacific Ocean. Taxon Birds Methods We compiled data
 from 53 seabirds breeding on 41 coastal and oceanic islands using
 different sources: our field records, online databases, environmental
 reports, and literature. We used generalized linear models (GLM) to
 describe the effect of island physiography (area, elevation, and
 isolation) and oceanographic factors (surface temperature, salinity, and
 primary productivity) on seabird species richness (α-diversity). We
 applied multivariate GLM to test the effects of physiographic and
 oceanographic predictors on species composition (β-diversity). We used
 Jaccard dissimilarities on species occurrences per island to calculate
 β-diversity partitioned into turnover and nestedness. Polynomial models
 allowed us to model these metrics against geographical and environmental
 gradients and so analyze patterns in seabird β-diversity across spatial
 scales. Results  Species richness was highest in Galápagos,
 Pitcairn, and Rapa Nui. Changes in seabird α-diversity across islands were
 determined by island area and distance to South America but not by
 oceanographic variables. Physiographic and oceanographic factors were
 significant in determining β-diversity. Changes in β-diversity were mostly
 due to species replacement (β-turnover) across three major island Systems
 (Galápagos Archipelago, Chilean coastal islands, and oceanic islands of
 the southeastern Pacific). The contribution of β-nestedness was restricted
 to small scales (within archipelagos). Main conclusions Physiographic and
 oceanographic factors explain species diversity of seabird assemblages on
 islands of the southeastern Pacific. Oceanographic variables did not
 affect species richness but significantly influenced species composition.
 Change in species composition reflects gradients across three marine
 biogeographical realms: Temperate South, Eastern Indo-Pacific, and
 Tropical Eastern Pacific. The low degree of species nestedness may reflect
 multiple evolutionary origins.
description: <b>Study area</b> Our study area
 extended longitudinally from 130°44'W to 70°31'W and
 latitudinally from 1°40'N to 38°22'S. The dataset included 42
 islands of six archipelagos: Pitcairn (4 islands), Rapa Nui (4),
 Desventuradas (3), Juan Fernández (3), Galápagos (13), and Chilean coastal
 islands (15). Thus, the islands of our dataset have varying degrees of
 isolation from other islands and the mainland and are distributed within
 different biogeographic provinces (Spalding et al., 2007; Figure 1). Most
 of the oceanic islands are located on the Nazca Tectonic Plate, except for
 the Pitcairn Archipelago which is located on the Pacific Plate. These
 islands are all part of chains of volcanic islands and
 seamounts. <b>Environmental
 variables</b> We compiled information on island
 characteristics from the literature and online databases. After removing
 highly collinear variables (i.e., Pearson correlation coefficient &gt;
 0.8), we considered seven environmental variables as predictors in the
 analyses: island area (km<sup>2</sup>), island elevation (m),
 distance to the mainland (km), human density (individuals per
 km<sup>2</sup>), sea surface salinity (PSS), sea surface
 temperature (ºC), and primary productivity
 (g.m<sup>-3</sup>.day<sup>-1</sup>) (Appendix S1).
 Island area, elevation, and distance from the mainland were obtained from
 the literature or estimated using tools available in ‘R’ (R Core Team,
 2016) and ‘Google Earth’ (www.google.com/earth). We derived data on human
 density from public databases provided by national institutes of
 statistics and censuses (see metadata in Appendix S1). All oceanographic
 variables were extracted from the raster maps available in the
 ‘Bio-ORACLE’ online database (Assis et al., 2018; Tyberghein et al.,
 2012). The raster resolutions of these maps were approximately 9.2 km at
 the equator. We used a 50 km radius buffer around the center of each
 island to calculate the average values of each oceanographic variable
 using the R package ‘raster’ (Hijmans, 2017).
 <b>Seabird assemblages</b> We
 compiled data on nesting occurrences of 53 seabird species (Appendix S2)
 in coastal and oceanic islands of the southeastern Pacific from the
 literature, online databases, and unpublished reports. Most of the
 consulted literature was published in the past 30 years, although we also
 included older publications to reference confirmed reports on some islands
 (see metadata in Appendix S2). For the majority of the Chilean islands, we
 used information on seabirds from our field records from multiple
 expeditions conducted over 19 years (1999-2018) on Chilean coastal
 islands, and five years (2013-2018) on Chilean oceanic islands (i.e.,
 Desventuradas, Rapa Nui, and Juan Fernández). All of the seabird
 information was organized in a binary matrix of species occurrence (1 =
 presence, 0 = absence) for each island (Appendix S2).
 <b>Data analysis</b>
 <i>Seabird α-diversity - Hypotheses 1 and
 2</i> We measured the α-diversity of seabirds of
 each island by quantifying species richness, measured as the sum total of
 nesting seabird species on each island. Before the analyses, we used the
 Shapiro-Wilk test to check the normality of all environmental variables
 (i.e., island characteristics and oceanographic variables). Environmental
 variables that differed from the normal distribution were adjusted by
 applying either square root or log<sub>10</sub>(<i>x
 </i>+ 1) transformations. We analyzed the relationships between
 seabird species richness and environmental variables by fitting
 generalized linear models (GLM) based on <i>Poisson</i>
 distribution and <i>log</i>-link function. Model selection and
 averaging were performed using the R package ‘MuMIn’ (Barton, 2018). The
 function <i>dredge</i> was used to perform a model selection
 routine based on the lowest second-order Akaike Information Criterion
 (AICc) and applied to 128 different model subsets. We applied the function
 <i>model.avg</i> to perform full model averaging, considering
 the best model subsets (AICw &gt; 0.05). We plotted the partial
 residuals associated with each environmental variable to depict their
 effect on species richness. The final model considered the environmental
 variables island area (log<sub>10</sub> transformed), island
 elevation (square root transformed), distance from the mainland (square
 root transformed), human density (square root transformed), salinity,
 superficial primary productivity, and sea surface temperature. Finally, we
 used residual-distance correlograms and the Moran’s I test routines in the
 R packages ‘ape’ (Paradis, Claude, & Strimmer, 2004) and ‘ncf’
 (Bjornstad & Cai, 2018) to assess the spatial independence of the
 GLM results. <i>Seabird β-diversity - Hypotheses
 3 and 4</i> We applied the routines in the R
 package ‘betapart’ (Baselga & Orme, 2012) to calculate the
 partitioned β-diversity, which separates the turnover and
 nestedness-resultant components. The β-turnover component reflects species
 replacement, while β-nestedness reflects differences in species richness.
 These components are derived from the dissimilarity index chosen to
 describe β-diversity, which is frequently referred to as β-total. We used
 Jaccard dissimilarities on species occurrences per island to calculate
 β-diversity. The relationship between β-diversity and geographical and
 environmental distances was analyzed using linear models based on a
 third-degree polynomial fit. Geographical distances were calculated using
 the <i>distm</i> routine in the R package ‘geosphere’
 (Hijmans, 2017). Environmental distances were represented as Euclidean
 distances of a scaled and centered environmental data matrix. The
 correlation between geographical and environmental distances was analyzed
 using the Mantel test in the R package ‘ade4’ (Dray & Dufour,
 2007). We fitted multivariate GLM models to analyze the
 effect of environmental variables on seabird species composition (i.e.,
 spatial turnover). The multivariate GLM was based on a
 <i>binomial</i> distribution and included the same predictor
 variables used in the global model for univariate analyses. The analysis
 was performed using the function <i>manyglm </i>of the R
 package ‘mvabund’ (Wang, Naumann, Wright, & Warton, 2012). The
 significance of the model terms was assessed using analysis of deviance
 considering <i>α</i> = 0.01. Since ordinations are a good way
 to represent multivariate data graphically, we performed a canonical
 analysis of principal coordinates (CAP, or distance-based redundancy
 analysis; Legendre & Andersson, 1999; Anderson & Willis,
 2003) to depict changes in seabird assemblage composition across islands
 in relation to environmental variables. CAP is a model-based ordination
 technique that can represent changes in species composition given specific
 hypotheses (i.e., environmental variables). The analysis was based on a
 resemblance matrix of Jaccard distances of species occurrences and all of
 the environmental predictors. Although CAP does not necessarily represent
 the fitted values of multivariate GLMs, we observed that the function
 <i>capscale</i> in the R package ‘vegan’ (Oksanen et al.,
 2009) produced CAP ordinations that were consistent with our multivariate
 GLM results (data not shown). The ordination was plotted using the R
 package ‘ggplot2’ (Wickham, 2009).