DOI: 10.5061/dryad.sxksn034f
title: Niche dynamics of Memecylon in Sri Lanka: distribution patterns, climate change effects, and conservation priorities

description: Aim: Recent climate projections have shown that the distribution of
 organisms in island biotas is highly affected by climate change. Here, we
 present the results of the analysis of niche dynamics of a plant group,
 Memecylon on Sri Lanka, an island, using species occurrences and climate
 data. We aim to determine which climate variables explain current
 distribution, model how climate change impacts the availability of
 suitable habitat for Memecylon, and determine conservation priority areas
 for Sri Lankan Memecylon. Location: Sri Lanka Methods: We used
 georeferenced occurrence data of Sri Lankan Memecylon to develop
 ecological niche models and assess both current and future potential
 distributions under six climate change scenarios in 2041-2060 and
 2061-2080. We also overlaid land-cover, and protected area maps and
 performed a gap analysis to understand the impacts of land-cover changes
 on Memecylon distributions and propose new areas for conservation.
 Results: Differences among suitable habitats of Memecylon were found to be
 related to patterns of endemism. Under varying future climate scenarios,
 endemic groups were predicted to experience habitat shifts, gains, or
 losses. The narrow endemic Memecylon restricted to the montane zone were
 predicted to be the most impacted by climate change. Projections also
 indicated that changes in species’ habitats can be expected as early as
 2041-2060. Gap analysis showed that while narrow endemic categories are
 considerably protected as demonstrated by their overlap with protected
 areas, more conservation efforts in Sri Lankan forests containing wide
 endemic and non-endemic Memecylon are needed. Main conclusions: This
 research helped clarify general patterns of responses of Sri Lankan
 Memecylon to global climate change. Data from this study are useful for
 designing measures aimed at filling the gaps in forest conservation on
 this island.
description: Occurrence data of <i>Memecylon</i> from Sri Lanka
 were collected from herbaria, Global Biodiversity Information Facility
 (GBIF), published literature, and fieldwork. Identical data points, points
 without environmental data, and proximate data points were removed using R
 packages <i>spocc, scrubr</i>, and <i>spatstat</i>
 (Chamberlain, 2014; Baddeley, Rubak, & Turner, 2015; Chamberlain,
 Ram, & Hart, 2021) on R v3.3.1 (R Core Team, 2019). Based on our
 knowledge of <i>Memecylon</i>, all occurrence data were
 visually examined in QGIS v3.3.3k to look for potential errors.
 Bioclimatic variables (19 variables) for the current climate at 30
 arc-second resolution, were downloaded from the WorldClim 2 Global Climate
 Data website v.2.1 (Fick & Hijmans, 2017). We established a buffer
 zone of 100 km around the occurrence data of each species separately using
 QGIS to generate calibration areas for the models. Pairwise Pearson’s
 correlation coefficients (<i>r</i>) for all bioclimatic
 variables within the species-specific buffer zones were estimated to avoid
 collinearity between them using R packages <i>raster</i> and
 <i>rgdal</i> (Keitt, Bivand, Pebesma, & Rowlingson,
 2010; Hijmans et al., 2012); species-specific predictors were retained
 based on a threshold of |<i>r</i>| &lt;
 0.65.  To model future climate scenarios, we used bioclimatic variables
 (2050 and 2070) from General Circulation Models (GCMs): Beijing Climate
 Center Climate System Model (BCC-CSM1-1) and Model for Interdisciplinary
 Research on Climate (MIROC5) and for each, we used 2.6, 4.5, and 8.5
 Representative Concentration Pathways (RCPs) (30 arc-second resolution);
 these data were obtained from WorldClim v.1.4 (Hijmans et al., 2005). We
 used MaxEnt v3.4.1 (Phillips, Anderson, Dudík, Schapire, & Blair,
 2017) to construct Ecological Niche Models (ENMs) for each Sri Lankan
 <i>Memecylon</i> species. We implemented a MaxEnt tuning
 process, which uses different combinations of model settings with the R
 package <i>ENMeval</i> (https://github.com/jamiemkass/ENMeval; Muscarella et al., 2014). The final model was created with the best-selected parameter set using 20 replicates, logistic output format, bootstrap resampling, and 5000 maximum iterations. Ecological niche models were used to calculate the niche breadth (Connor et al., 2018). The models were converted into binary presence-absence maps with three threshold approaches: the minimal training presence threshold, the threshold that equalizes sensitivity and specificity, and the threshold that maximizes the sum of sensitivity and specificity of the binary maps using R packages <i>scales</i> and <i>geosphere</i> (Baumgartner & Wilson, 2017; Wickham & Seidel, 2020). The Multivariate Environmental Similarity Surface (MESS) (Elith, Kearney, & Phillips, 2010) for each species was performed to compare the current and future climates at each locality. A gap analysis (Scott et al., 1993) was conducted on QGIS to evaluate conservation priorities based on existing protected areas and <i>Memecylon</i> occurrences using protected areas (UNEP-WCMC & IUCN, 2020) and land-cover maps of Sri Lanka (Rathnayake et al., 2020).
description: For some
 <i>Memecylon</i> species used in this study, sample sizes are
 low and there can be potential sampling biases associated with
 inadequately capturing the environmental conditions in which the species
 occurs.