Corybas Dowlingii DL Jones, 2004
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© Econumerics Pty Ltd 2020. Reproduction and distribution prohibited without permission Technical Report (Excerpt) Modelling current and future habitat suitability of the rare Red Helmet Orchid (Corybas dowlingii D.L. Jones, 2004) © Econumerics Pty Ltd 2020 1 | P a g e © Econumerics Pty Ltd 2020. Reproduction and distribution prohibited without permission Technical Report (Excerpt) 1. Introduction Orchids belong to the second world’s largest family of flowering plants, which comprises 736 currently recognized genera, and nearly 28,000 species (Chase, Cameron et al. 2015). Among them is the Red Helmet Orchid, a rare terrestrial colonially growing tuberous herb, also known as Red Lantern. This perennial deciduous plant is officially classified as Corybas dowlingii D.L. Jones (Streptophyta; Magnoliopsida; Asparagales; Orchidaceae) and was first described in 2004 (Jones 2004) (Fig. 1). Fig. 1. Dendrogram of the family Orchidaceae based on classification (not on phylogeny), highlighting the taxonomic position of the Red Helmet Orchid (Corybas dowlingii), within the tribe Diurideae, subtribe Acianthinae (mod. from (Chase, Cameron et al. 2015)). The Red Helmet Orchid has a single dark green leaf, cordate to orbicular, with a tapered end (15-35 mm long; 15-35 mm wide), and a solitary dark purple/red flower, with white patchy 2 | P a g e © Econumerics Pty Ltd 2020. Reproduction and distribution prohibited without permission Technical Report (Excerpt) areas on the labellum (Jones 2004, NSW Government - Office of Environment and Heritage 2020) (Fig 2). Fig. 2. The Red Helmet Orchid (Corybas dowlingii) (Photo credit: Lachlan Copeland; www.environment.nsw.gov.au). The Scientific Committee of the NSW Government lists C. dowlingii as an endangered species (NSW Government - NSW Threatened Species Scientific Committee 2020), under the NSW Threatened Species Conservation Act (Government of New South Wales 1995). Among the main factors affecting this recommendation is the orchid’s highly restricted known distribution. Orchids populate a wide variety of habitats worldwide. The taxon includes highly specialized species, adapted to relatively extreme values of light, soil pH, temperatures, moisture, altitude etc. (Djordjevic, Tsiftsis et al. 2020). Despite this overall plasticity at higher taxonomic level, a number of species display remarkably narrow geographic distributions, and, consequently, are highly vulnerable to ecological threats, and anthropogenic pressure. C. dowlingii, is endemic to Australia and believed to be confined only to New South Wales, where it has so far been found at two localities in Port Stephens, plus two additional sites near Bulahdelah, and Freemans Waterhole, south of Newcastle (Okada 2006). The Red Lanterns are commonly found in shady and protected areas of the understory of tall open forests, like gullies and southerly slopes, characterized by well-drained gravelly soils, and low elevation (Jones 2006). Documented occurrences (about 18,400 plants) cover <1,000 Km2 of land in total; however, with the exception of Port Stephens (≈50 ha), all reported populations occupy areas of less than a few hectares each. No occurrence has been found within existing conservation reserves. Rather, clearing, illegal dumping, habitat degradation and fragmentation, and anthropogenic disturbance are major factors, contributing to a steady decline in numbers (Okada 2006). 3 | P a g e © Econumerics Pty Ltd 2020. Reproduction and distribution prohibited without permission Technical Report (Excerpt) The present report uses species distribution modelling (SDM) with MaxEnt (Phillips, Anderson et al. 2006) to model the C. dowlingii’s fundamental niche, across most NSW coastal area and eastern Victoria. Habitat suitability prediction was based on environmental predictors like soil properties, and gridded conformal datasets of spatially-interpolated monthly climate data, averaged between 1970-2000, at high spatial resolution (≈1 Km2) (Fick and Hijmans 2017). 2. Materials and Methods 2.1 GIS Processing Processing in GIS was conducted in ArcGIS Pro 2.6.2. The raster to ASCII tool was used to convert raster datasets to an ASCII text file for further processing. Prior to the conversion, the soil pH grid was reclassified as follows: pH 0 to 5.5 (i.e., acidic)=value 1; pH 5.5 to 8 (neutral)=value 2; pH 8 to 9 (alkaline)=value 3; pH 9 to 14 (highly alkaline)=value 4. After pre-processing, all final grids had the same call size (0.008 angular units), and geographic coordinate system (WGS 1984). 2.2 Occurrence Data and Environmental Variables C. dowlingii observations (n=165), recorded between 2003 and 2018 from n=11 sites, were downloaded from the Biodiversity and Climate Change Virtual Lab website (BCCVL - Biodiversity and Climate Change Virtual Lab 2020) (Fig 3). 4 | P a g e © Econumerics Pty Ltd 2020. Reproduction and distribution prohibited without permission Technical Report (Excerpt) Fig. 3. Location of occurrence sites used, in the present report, to model the species distribution of the rare Red Helmet Orchid (Corybas dowlingii). 5 | P a g e © Econumerics Pty Ltd 2020. Reproduction and distribution prohibited without permission Technical Report (Excerpt) Global climate and weather data were obtained from WorldClim (WorldClim 2020). For the present analysis we used the complete bioclimatic dataset (n=19), including biologically meaningful monthly temperature- and rainfall- values (Fick and Hijmans 2017), plus the categorical factors included soil cover, and pH (Table 1). For landcover, the Australia Dynamic Land Cover categorical dataset 1.0 (DLCDv1_Class_Reduced) was used. This shows a single snapshot of Australian land cover between 2000 and 2008, at 9 arcsec (Australian Government - Geoscience Australia 2020). pH soil data from the 5-15cm layer with 95th percentile confidence limit (pHc_005_015_95_N_P_AU_NAT_C_20140801) was sourced from TERN AusCover (TERN AusCover 2020), and used after reclassification as categorical data. Table 1. Environmental variables, meaning and data type, used in the present analysis. Variable Meaning Type BIO1 Annual Mean Temperature Continuous Mean Diurnal Range (Mean of monthly (max temp - min BIO2 Continuous temp)) BIO3 Isothermality (BIO2/BIO7) (×100) Continuous BIO4 Temperature Seasonality (standard deviation ×100) Continuous BIO5 Max Temperature of Warmest Month Continuous BIO6 Min Temperature of Coldest Month Continuous BIO7 Temperature Annual Range (BIO5-BIO6) Continuous BIO8 Mean Temperature of Wettest Quarter Continuous BIO9 Mean Temperature of Driest Quarter Continuous BIO10 Mean Temperature of Warmest Quarter Continuous BIO11 Mean Temperature of Coldest Quarter Continuous BIO12 Annual Precipitation Continuous BIO13 Precipitation of Wettest Month Continuous BIO14 Precipitation of Driest Month Continuous BIO15 Precipitation Seasonality (Coefficient of Variation) Continuous BIO16 Precipitation of Wettest Quarter Continuous BIO17 Precipitation of Driest Quarter Continuous BIO18 Precipitation of Warmest Quarter Continuous BIO19 Precipitation of Coldest Quarter Continuous 6 | P a g e © Econumerics Pty Ltd 2020. Reproduction and distribution prohibited without permission Technical Report (Excerpt) BIOcateg20 Land cover Categorical BIOcateg21 Soil pH Categorical 2.3 Model Evaluation Spatially-independent evaluations and estimate of optimal model complexity were performed in R version 4.0.2 (Taking Off Again), using the ENMeval package, and the random k-fold (bins) method of cross-validation, with n=10,000 background points (Muscarella, Galante et al. 2014). Other R libraries used were: rJava, Raster, dismo, and MASS. MaxEnt settings (based on ENMeval evaluation results) included: Features=LQHPT; Regularization multiplier (rm)=0.5; Output format=Cloglog; Random seed=yes; Max No. background points=10,000; Replicates (i.e., model folds)=10; Replicated run type=Cross validate; Maximum iterations=500; Convergence threshold=0.00001. 2.4 Future Climate Change Scenarios CMIP6 downscaled future climate projections (downscaled and calibrated with WorldClim v2.1 as baseline) (Eyring, Bony et al. 2016) were used for the global climate model (GCM) CNRM-ESM2-1 (Séférian, Nabat et al. 2019), and the Shared Socioeconomic Pathway (SSP) 3-7.0, which models global warming if climate policies are not fully enacted (Riahi, van Vuuren et al. 2017). The present report used monthly values averaged over 2021-2040, at 2.5 minutes resolution. No categorical data were included for this modelling projection. 3. Results 3.1 Current Climatic Conditions Figures 4 and 5 illustrate land cover and soil pH across the study area. Documented occurrences were found only in dense forests characterized by acidic soils (pH <5.5). 7 | P a g e © Econumerics Pty Ltd 2020. Reproduction and distribution prohibited without permission Technical Report (Excerpt) Fig. 4. Orchid occurrences and land cover classes used for modelling. 8 | P a g e © Econumerics Pty Ltd 2020. Reproduction and distribution prohibited without permission Technical Report (Excerpt) Fig. 5. Orchid occurrences and soil pH classes used for modelling. 9 | P a g e © Econumerics Pty Ltd 2020. Reproduction and distribution prohibited without permission Technical Report (Excerpt) Figures 6 and 7 show the orchid’s mean niche suitability, and standard deviation of the nine output grids, under current climatic conditions. In Fig. 6 the values have been thresholded using the 10th percentile training presence test omission (i.e., 0.8889). The habitat is restricted to a narrow coastal strip for a