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.

Occurrence data of Memecylon 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 spocc, scrubr, and spatstat (Chamberlain, 2014; Baddeley, Rubak, & Turner, 2015; Chamberlain, Ram, & Hart, 2021) on R v3.3.1 (R Core Team, 2019). Based on our knowledge of Memecylon, 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 (r) for all bioclimatic variables within the species-specific buffer zones were estimated to avoid collinearity between them using R packages raster and rgdal (Keitt, Bivand, Pebesma, & Rowlingson, 2010; Hijmans et al., 2012); species-specific predictors were retained based on a threshold of |r| < 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 Memecylon species. We implemented a MaxEnt tuning process, which uses different combinations of model settings with the R package ENMeval (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 scales and geosphere (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 Memecylon occurrences using protected areas (UNEP-WCMC & IUCN, 2020) and land-cover maps of Sri Lanka (Rathnayake et al., 2020).

For some Memecylon 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.