DOI: 10.5061/dryad.1zcrjdfvn
title: Island size predicts mammal diversity in insular environments, except for land-bridge islands

description: Insular environments are among the most endangered ecosystems as they face
 a myriad of anthropogenic stressors. Forest mammals perform a wide range
 of ecological services, with their persistence being vital for ecosystem
 functionality in both natural and artificial islands. Studies revealed
 that shrinkage in island size usually leads to the decay of mammal species
 richness and abundance in patchy landscapes. However, mammal species-area
 (SARs) and abundance-area (AARs) relationships can differ among insular
 environments: oceanic, fluvial, artificial, and land-bridge islands (i.e.,
 natural islands connected to the mainland). Large dams create vast
 insularized landscapes after river impoundment, leading to pervasive
 habitat loss and potentially causing even worse biodiversity losses than
 other insular systems. We conducted an extensive literature search and
 used meta-analysis techniques to quantify the magnitude of SAR and AAR for
 forest mammals across different archipelago landscapes worldwide. After a
 screening process, we ended up with 26 studies comprising 55 different
 effect sizes representing the magnitude of SARs and AARs. Our global
 analysis unveiled a positive relationship between effect sizes and island
 area, with mammal species richness and abundance increasing in fluvial,
 oceanic, and artificial islands accordingly with island area, but not in
 land-bridge islands. These results demonstrate that, except for
 land-bridge islands, SAR and AAR are still fair models to predict mammal
 diversity. These results could improve the prediction of SAR and AAR in
 insular environments under habitat loss scenarios and propose sound
 conservation strategies since the rate at which insular communities have
 been lost is presently unknown.
description: <strong>2. MATERIAL AND
 METHODS</strong>
 <strong>2.1 Search and
 studies selection</strong> This study was carried out following the
 recommendations of the PRISMA initiative (Preferred Reporting Items for
 Systematic Reviews and Meta-Analysis; Moher et al., 2009). The literature
 search was concluded on April 2021, using the ISI Web of Science™ database
 (WoS) and Scopus. The following keywords were used: ("marine
 island*" OR "oceanic island*" OR "reservoir
 island*" OR "artificial island*" OR "forest
 island*" OR "land-bridge island*" OR "fluvial
 island*" OR "river* island*" OR "wetland*" OR
 "floodplain forest*") AND ("mammal*" OR
 "vertebrate*"). This set of keywords was applied to the
 "Topic" search with no filters. A total of 2190 and 1860 studies
 were found in WoS and Scopus, respectively. However, 2136 studies were
 excluded from this review because they were out of the scope (they did not
 evaluate mammals on islands). We focused on non-flying mammals, given that
 bats have a higher ability to disperse than arboreal and terrestrial
 forest mammals. After a careful assessment using our inclusion
 and exclusion criteria (see the topic' Data extraction' for more
 details), only 6% (number of studies, k = 129) of the papers presented
 adequacy to our eligibility criteria. We also included one study that we
 were aware of (Dalecky et al., 2002), which was not included in our data
 search. These studies must present the SAR, the AAR, or provided island
 area together with the number of species or abundance per surveyed island,
 which were subsequently used to calculate the effect sizes. At least three
 sampling sites per study were required to be included in our database, and
 all sampling techniques were considered. Finally, as this study was
 interested in native faunal assemblages, exotic species were excluded.
 Seventy-nine studies were excluded because they
 had no quantitative information relevant for syntheses, such as sample
 size, average, standard deviation, or correlation coefficients (Figure 1,
 Supporting information). Studies with a sample size lower than three were
 also excluded. After our assessment, we included 26 studies that tested
 the effects of island areas on mammal species richness or abundance
 belonging to 22 studied insular landscapes in our
 meta-analysis.  
 FIGURE 1. Flow diagram summarizing the procedure used to
 select studies for the systematic review.
 <strong>2.2 Data
 extraction</strong> For each study that achieved the eligibility
 criteria, we recorded the sample sizes and correlation coefficients
 (Pearson (<em>r</em>) or Spearman coefficients
 (<em>ρ</em>)) or the coefficient of determination
 (<em>r</em>²) for simple regression between island area and
 either species richness or abundance. Neither correlation measure,
 <em>r</em>², mean, or standard deviation, was provided in the
 two studies. For these two studies, we thus extracted data from figures to
 calculate effect sizes using the "metaDigitise" package (Pick et
 al., 2018). We considered oceanic islands, all islands that do not sit on
 continental shelves surrounded by sea. Fluvial islands encompassed those
 islands surrounded by a river, transitory islands, and islands
 disconnected from the mainland (Kalliola, 1993). Finally, land-bridge islands were considered
 those temporally connected
 to the mainland (Newmark,
 1987), while artificial islands were those anthropogenically created by dams.
 We also identified the
 distribution of the studies per biogeographical region (Afrotropical,
 Australasia, Indomalaya, Nearctic, Neotropic, and Palearctic).
 <strong>2.3 Data
 analysis</strong> We transformed Spearman correlations
 (<em>ρ</em>) to the Pearson product-moment correlation
 coefficient (<em>r</em>) following Lajeunesse (2013):
 <em>r </em>= 2*sin(<em>π ρ</em>/6), if <em>n
 </em>&lt; 90 or r = ρ, if <em>n </em>≥ 90 where
 <em>n </em>represents the sample size. All the studies
 included (26) presented a coefficient of determination (r²) of a simple
 regression between island area and mammal richness and abundance. In these
 cases, we estimated r as the square root of <em>r</em>². Next,
 we transformed all r values to Fisher's z (and their respective
 variances) following Borenstein et al. (2009).
 We estimated the weighted
 effect size using a random-effect model, in which we assume that all
 studies do not share a common true effect size but a true mean effect size
 with a given true variance between-study (<em>T</em>²)
 (Borenstein et al., 2009). We estimated <em>T</em>² by
 restricted maximum likelihood. We weighted each effect size by the inverse
 of the total variance. We quantified the heterogeneity in studies'
 outcomes (effect sizes summarizing SAR and AAR) with the
 <em>T</em>² and <em>I</em>² statistics.
 <em>T</em>² was decomposed into <em>T</em>²
 between and <em>T</em>² study-level. The
 <em>T</em>² statistic is the between-study variance, a
 heterogeneity measure in the same effect size scale. The
 <em>I</em>² statistic measures how much of the heterogeneity
 is true variability (i.e., is not due to experimental error) and is
 measured on a relative scale (Borenstein et al., 2009; 2017). We also reported Cochrane's Q,
 which is the weighted sum of squared differences between individual study
 effects and the pooled effect across studies (Koricheva et al.,
 2013). We assessed the effect of island environments
 (levels: artificial islands, land-bridge islands, oceanic islands, or fluvial islands) on
 Fisher's <em>z</em> to quantify how much heterogeneity
 (variability in studies outcomes) could be explained by these archipelagic
 characteristics. The effects of these factors (or moderators in the
 meta-analysis jargon) were evaluated by subgroup analyses (Borenstein et
 al., 2009; 2017), which were performed separately for each moderator. We
 only tested levels of each moderator that had at least four studies to
 perform this analysis using the "metafor" package (Viechtbauer,
 2010). In most cases, these studies were done at different locations to
 avoid a small sample size for parameter estimates. The effect of each
 moderator was assessed by partitioning Cochrane's <em>Q
 </em>in a heterogeneity explained by the moderator variable (QM) and
 a residual heterogeneity (QR) (analogous to an analysis of variance)
 (Borenstein et al., 2009). QM follows a χ² distribution and, if
 significant, means that there was a difference between the mean effect
 size of at least one of the levels of the moderator considered (in other
 words, the moderator explains part of the heterogeneity). We also tested
 the sub-group models without intercepts to test if the average effect size
 of each level of the moderator differs from zero. We estimated several effect size measures for a
 single study in some cases. In these cases, effect sizes cannot be assumed as independent
 information since they were estimated with the same sampling units (a
 multiplicity artifact; Hedges et al., 2010; López-López et al., 2018).
 Thus, to control for effect size multiplicity, the cumulative effect size
 and subgroup analyses consisted of multi-level meta-analyses (Nakagawa
 & Santos, 2012). In this analysis, we control for multiplicity by
 adding a random-effects term encoding the study corresponding to the group
 of dependent effects sizes (T²study-level) (Nakagawa & Santos,
 2012). The effects of spatial extent were evaluated by a meta-regression
 based on log-transformed data to improve linearity (Borenstein et al.,
 2009). In order to assess for publication bias, we used
 a funnel plot to identify asymmetry in the publications on SARs and AARs.
 The funnel plot is a scatterplot of effect sizes against a measure of
 their precision and tends to be symmetric around the mean effect size in
 the absence of bias (Borenstein et al., 2009). We used the trim-and-fill
 method to (i) estimate the potential number of missing studies and (ii)
 correct the cumulated effect size accounting for the potential omission of
 studies due to publication bias (Jennions et al., 2013). Finally, we also
 used Orwin's fail-safe number (OFSN; Orwin 1983) to assess
 publication bias. The OFSN estimates how many studies we would need to
 include to reduce the unweighted average effect size to the desired
 threshold we deemed as non-relevant (Borenstein et al., 2009; 2017). We
 choose non-relevant reductions of 25% in the mean effect size following
 Ferreira Neto et al. 2022. For publication bias analyzes, we computed mean
 effect sizes and variances by the study to avoid bias due to multiplicity.
 All analyzes were performed using the "metafor" package
 (Viechtbauer, 2010) in the "R" software (R Development Core
 Team, 2020).