DOI: 10.5061/dryad.0rxwdbrxs
title: Non-reef habitats in a tropical seascape affect density and biomass of fishes on coral reefs

description: Non-reef habitats such as mangroves, seagrass, and macroalgal beds are
 important for foraging, spawning, and as nursery habitat for some coral
 reef fishes. The spatial configuration of non-reef habitats adjacent to
 coral reefs can therefore have a substantial influence on the distribution
 and composition of reef fish. We investigate how different habitats in a
 tropical seascape in the Philippines influence the presence, density, and
 biomass of coral reef fishes to understand the relative importance of
 different habitats across various spatial scales. A detailed seascape map
 generated from satellite imagery was combined with field surveys of fish
 and benthic habitat on coral reefs. We then compared the relative
 importance of local reef (within coral reef) and adjacent habitat
 (habitats in the surrounding seascape) variables for coral reef fishes.
 Overall, adjacent habitat variables were as important as local reef
 variables in explaining reef fish density and biomass, despite being fewer
 in number in final models. For adult and juvenile wrasses (Labridae), and
 juveniles of some parrotfish taxa (Chlorurus), adjacent habitat was more
 important in explaining fish density and biomass. Notably, wrasses were
 positively influenced by the amount of sand and macroalgae in the adjacent
 seascape. Adjacent habitat metrics with the highest relative importance
 were sand (positive), macroalgae (positive) and mangrove habitats
 (negative), and fish responses to these metrics were consistent across
 fish groups evaluated. The 500-m spatial scale was selected most often in
 models for seascape variables. Local coral reef variables with the
 greatest importance were percent cover of live coral (positive), sand
 (negative), and macroalgae (mixed). Incorporating spatial metrics that
 describe the surrounding seascape will capture more holistic patterns of
 fish-habitat relationships on reefs. This is important in regions where
 protection of reef fish habitat is an integral part of fisheries
 management but where protection of non-reef habitats is often overlooked.
description: <b>Study
 Location</b> This study was
 conducted around Siquijor Island in the Visayan region of the Philippines
 (Fig. 1a). Shallow water benthic habitats of Siquijor include macroalgal
 beds, mangroves, and seagrass beds of varying spatial extent adjacent to
 fringing coral reefs.  <b>Fish and
 Habitat Surveys</b> Surveys of
 reef fish and benthos were conducted in April – July 2016 at eight
 locations around Siquijor Island (Fig. 1a), with paired NTMR and control
 (open to fishing) sites, totalling to 16 sites. Along a 50-m by 5-m
 transect, large mobile reef fish (&gt;10 cm TL) were counted and sized
 to the nearest centimetre. On the return swim, smaller (≤ 10 cm TL) reef
 fish species were recorded within a 2-m width. Biomass of fishes was
 calculated using published length-weight relationships (Kulbicki et al
 2005). For benthic surveys, substratum was identified at 50-cm intervals
 along the 50-m transect and was classified based on substrate (rock, sand,
 rubble, coarse sand) and benthic cover (abiotic, crustose coralline algae,
 epilithic algal matrix, macroalgae, soft coral, hard coral, other) (Table
 1). Macroalgae and soft coral were identified to genus when possible. Hard
 coral was identified to genus and classified into growth form (fragile,
 robust). The ‘other’ category included sessile invertebrates such as
 sponges, tunicates, and gorgonians. Structural complexity was estimated
 visually on a 0-5 scale following methods used in Wilson et al.
 (2007).  <b>Habitat
 Mapping</b>  Images from
 the GeoEye and PlanetScope satellite sensors were acquired from the
 Digital Globe Foundation, and Planet, respectively. The GeoEye satellite
 provides a spatial resolution of 1.84-m and Planet provides a 3-m
 resolution, both across four spectral bands of blue, green, red, and
 near-infrared (NIR) (Fig. 1b). Both sensors were necessary to acquire
 complete coverage of the island. Pre-processing of imagery was conducted
 using the software ENVI (v. 5.3, Harris Geospatial Inc.). Band ratios were
 calculated to provide additional unique spectral signatures for benthic
 habitat classes (Phinn et al. 2012, Roelfsema et al. 2013). Band ratios
 were: blue to red (B/R), blue to green (B/G), and red to NIR (R/NIR).
 After pre-processing, classification of imagery into habitat types was
 conducted using the maximum likelihood classification tool in ArcGIS, v.
 10.4.1. Feature classes were a combination of biotic and geomorphological
 features: seagrass meadows, macroalgal beds, reef flat, reef crest, reef
 slope, lagoon, sand, mangrove forest, and beach (Fig. 1c). Georeferenced
 habitat data points (n=500) collected <i>in-situ</i> in
 2016-2018 informed the maximum likelihood classification, with 70% of
 points used for training, and the remaining 30% used for validation of the
 classified map. The map was then manually reviewed and edited for obvious
 errors, smoothed using the majority filter in ArcGIS, and converted to
 polygons for spatial analysis. Map validation identified 72% accuracy of
 habitat classification using the maximum likelihood method.
 <b>Spatial
 Analysis</b> Fish and
 benthic survey locations were overlaid onto the classified habitat map to
 calculate spatial statistics of the seascape surrounding each site (n =
 16). Adjacent habitats used for spatial analysis were seagrass,
 macroalgae, sand, reef flat, and mangroves. For each location, distance to
 the nearest habitat type was measured using edge-to-edge distance between
 survey sites and each habitat. Buffer zones surrounding each survey site
 were calculated at three different spatial scales (250, 500, 1000-m) (Fig.
 1c). Buffers were clipped by shore and deep-water features to only
 represent shallow water habitat. The proportion of each habitat within
 each buffer zone was calculated as the area of habitat divided by the
 total area of the clipped buffer. These data were then incorporated with
 the benthic survey data on coral reefs for further analysis (Table 1).
  Global Moran’s I was calculated for the 500-m habitat spatial scale to
 evaluate any potential spatial autocorrelation. Spatial data was not
 significantly spatially autocorrelated for the 500-m scale (Moran’s I =
 0.370, p = 0.24). <b>Statistical
 Analysis</b> Boosted
 regression trees (BRT; Elith et al. 2008) were used to evaluate how
 benthic habitats at different spatial scales affected coral reef fishes
 using the gradient BRT method from the gbm package. Fish groups were
 analysed in terms of density and biomass, or presence/absence, using
 Poisson, Gaussian, and Bernoulli distributions, respectively.
 Presence/absence was used for species groups with too few observations for
 density and biomass analysis (Lutjanidae and Serranidae). In total, 32 BRT
 models were run on fish groups with the greatest number of observations at
 the family level: Labridae (wrasses, excluding parrotfishes), Lutjanidae
 (snappers), Serranidae (groupers), Pomacentridae (damselfishes),
 Chaetodontidae (butterflyfishes), and Acanthuridae (surgeonfishes) (Table
 2). Parrotfishes (Labridae, subfamily Scarinae) were run at the level of
 genus for two different feeding-type groups, <i>Scarus
 </i>and<i> Chlorurus</i>, where Scarus are scrapers and
 Chlorurus excavators. <i>Hipposcarus </i>was included in the
 ‘Scarus’ group and<i> Cetoscarus </i>was<i>
 </i>included in the ‘Chlorurus’ group based on their feeding modes.
 Models for juvenile reef fish density were only possible for wrasses, and
 the parrotfish groups <i>Scarus</i> and
 <i>Chlorurus,</i> due to the lack of juveniles observed from
 other families. Fish groups were also separated by coral reef zones, i.e.
 reef crest and slope. To identify
 the scale at which reef fish responded to the seascape, a BRT was run for
 each adjacent habitat type at all three spatial scales (250, 500, 1000 m)
 for each response variable. The ‘best’ scale for each habitat type was
 selected as the radius with the highest relative importance, and only that
 scale was included for further analysis. Variables with correlation values
 greater than 0.8 (e.g. hard coral, fragile coral, robust coral) were run
 in a BRT, and only the variable with highest relative importance was
 selected for the remaining analysis. Full models were then run with these
 pre-selected variables with an interaction depth of 3 and bag fraction of
 0.75 using the gbm.step method in the gbm package, and were calibrated for
 best results by altering the learning rate to achieve the optimal number
 of iterations between 1000-10000 trees, based on a 10-fold
 cross-validation procedure. The gbm.simplify process was used to reduce
 the number of variables by an iterative backwards stepwise removal of the
 least influential variables using k-fold cross validation until the change
 in predictive deviance was minimized. The simplify process selected the
 nine most influential variables, and NTMR status was the tenth variable to
 evaluate any reserve effect. To account for stochasticity and incorporate
 uncertainty values for relative importance, models were bootstrapped
 (sampling with replacement) 100 times. Error in relative importance and
 deviance explained values were measured by 95% confidence intervals from
 the bootstrapping process. Cross validation deviance (CV deviance) was
 calculated by subtracting the CV deviance from the null deviance and
 dividing by the null deviance. Mean relative importance was used as an
 indicator for variable importance. Because models had 10 variables,
 relative importance values greater than 10% were considered influential as
 they were selected more frequently than expected by chance. The mean
 relative importance was summarised only for influential variables
 (&gt;10% relative importance) and compared between variables
 categories (local reef vs. adjacent habitat) (Table 1). Here, we define
 “local reef” as the small-scale benthic habitat characteristics of a coral
 reef, whereas “adjacent habitat” describes larger scale spatial metrics of
 multiple habitat types across a seascape. Wilcoxon ranked tests for
 non-parametric data were used to compare the mean relative importance
 between local reef and adjacent habitat categories across all models, at
 the level of reef zones (crest and slope), fish life stages (juvenile and
 adult), and for each fish group.
description: <strong>Transect_Summ_data_for_Dryad.xlsx:</strong><br> Data for analysis, with descriptions of variables.   <strong>Bootstrap_BRT_full_dryad.Rmd:</strong> .Rmd file detailing variable selection, gbm process, and boostrap example when running data for one respone variable.   <strong>BRT_BOOTSTRAP_final.Rmd: </strong> .Rmd file for bootstrap to run through multiple models at once. Loops through bootstrap and also loops through mulitple models.