DOI: 10.5061/dryad.2v6wwpzr8
title: Fijian habitat and invertebrate species distribution modelling

description: Aim Spatially explicit protections of coastal habitats determined on the
 current distribution of species and ecosystems risk becoming obsolete in
 100 years if the movement of species ranges outpaces management action.
 Hence, a critical step of conservation is predicting the efficacy of
 management actions in future. We aimed to determine how foundational,
 habitat‐building species will respond to climate change in Fiji. Location
 The Republic of Fiji. Methods We develop species distribution models
 (SDMs) using MaxEnt, General Additive Models and Boosted Regression Trees
 and publicly available data from the Global Biodiversity Information
 Facility to predict changes in distribution of suitable habitat for
 mangrove forests, coral habitat, seagrass meadows and critical fisheries
 invertebrates under several IPCC climate change scenarios in 2070 or 2100.
 We then overlay predicted distribution models onto existing Fijian
 protected area network to assess whether today's conservation
 measures will afford protection to tomorrow's distributions. Results
 We develop species distribution models (SDMs) using MaxEnt, General
 Additive Models and Boosted Regression Trees and publicly available data
 from the Global Biodiversity Information Facility to predict changes in
 distribution of suitable habitat for mangrove forests, coral habitat,
 seagrass meadows and critical fisheries invertebrates under several IPCC
 climate change scenarios in 2070 or 2100. We then overlay predicted
 distribution models onto existing Fijian protected area network to assess
 whether today's conservation measures will afford protection to
 tomorrow's distributions. Main conclusions Species distribution
 models are a critical tool for conservation managers, as linking spatial
 distribution data with future climate change scenarios can aid in the
 creation and resiliency of protected area programmes. New protected area
 designations should consider the future distribution of species to
 maximize benefits to those taxa.
description: We sampled mangrove presence points from mangrove global
 distribution polygons created by Hamilton and Casey (2016) based on
 estimates from 2000.  Seagrass presence points were downloaded from
 McKenzie et al. (2021) who thoroughly reviewed the known distribution of
 seagrass meadows in the Pacific Islands. Coral presence points were
 sampled from coral reef global distribution polygons created by UNEP-WCMC
 (2021). Presence points for each taxa were reviewed to eliminate points
 without a minimum of three decimal places as well as any
 outliers. Publicly available distribution data on mud crabs
 (<em>Scylla</em> <em>serrata</em>), ark shells
 (<em>Anadara</em> <em>antiquata</em>), triton
 shells (<em>Charonia</em> <em>tritonis</em>), sea
 cucumbers (<em>Holothuria</em> <em>atra</em>,
 <em>Bohadschia</em> <em>argus</em>, and
 <em>Holothuria</em> <em>edulis</em>), and giant
 clams (including <em>Tridacna</em> <em>gigas</em>,
 <em>T. maxima</em>, and <em>T. squamosa</em>) were
 downloaded from the Global Biodiversity Information Facility. Presences
 reported on GBIF went through a cleaning process to remove points without
 latitude and longitude, coordinates with reported uncertainty larger than
 10 km<sup>2</sup>, duplicates, points without a minimum of
 three decimal places, and points without enough identifying information
 using the R package scrubr (Chamberlain 2020). Geographic distribution of
 points were reviewed per taxa to determine if spatial sampling bias
 occurred, and model study areas reflect the closest and most thorough
 sampling effort to Fiji (Table 1). Finally, to address pseudoreplication
 and bias, presence points were gridded over the entire study area and
 reduced to only one presence point per 5‐arcminute cell. Background points
 were identified from nonoccupied cells of this grid, masked for land
 area. Biophysical variables were selected for inclusion in analysis based
 on life history traits of each taxa type, using available literature and
 prior SDM studies on similar species to limit the amount of irrelevant
 predictors (Santini et al. 2020). Climatic variables were sourced from
 WorldClim, and oceanographic variables were sourced from Marspec and
 Bio-Oracle at a resolution of 5 arcminutes. We compared
 the performance of MaxEnt, BRT, and GAM using area under the receiver
 operating characteristic curves (AUC) , the true skill statistic (TSS),
 and the presence-only metric Boyce Index. We assumed that AUC = 1 was
 overfit, AUC values &lt; 0.8 were poorly fit, and TSS values &lt;
 0.6 were poorly fit (see Mainella 2016; Shabani et al. 2016). Boyce Index
 values vary from -1 to 1 and values &gt;0 indicate that presence
 points and model predictions are consistent and better than random, while
 values &lt;0 indivate poor performance. Validation, modelling and
 projections were completed in the SDMTune package for MaxEnt and BRT
 (Vignali et al. 2020), and in Biomod2 for GAM models (Thuiller et al.
 2021).