Corybas Dowlingii DL Jones, 2004

Technical Report (Excerpt)

Modelling current and future habitat suitability of the rare Red Helmet Orchid (Corybas dowlingii D.L. Jones, 2004)

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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

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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).

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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).

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Fig. 3. Location of occurrence sites used, in the present report, to model the species distribution of the rare Red Helmet Orchid (Corybas dowlingii).

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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

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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).

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Fig. 4. Orchid occurrences and land cover classes used for modelling.

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Fig. 5. Orchid occurrences and soil pH classes used for modelling.

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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 total area of approximately 1,674 Km2. Un-thresholded maps show a steep drop in suitability occurs at increasing distance from the coast (data not shown). Multiple, potentially suitable sites exist south of Newcastle, between Port Macquarie and Coffs Harbour, and within the densely vegetated gullies, between Coffs Harbour, and the Nymboida National Park (about 130 Km, travelling NW). Conversely, habitat suitability quickly declined south of Sydney, and along the south-facing Victorian coastline.

Fig. 6. Average habitat suitability model (thresholded) of the Red Helmet Orchid (Corybas dowlingii), under current climatic conditions.

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Standard deviation was relatively low (<0.3), confirming only small differences among the nine output grids.

Fig. 7. Standard deviation map of the MaxEnt model outputs (n=9).

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Table 2 and Fig. 8 show the relative contributions of the different environmental variables. Variation of temperature seasonality and mean/minimum temperatures of the coldest periods seem major predictors of habitat suitability (contribution ≥17.7%); however, as this may change significantly with strong correlations, a jackknife analysis was performed.

Table 2. Environmental variable contributions (averages over replicates). Percent Permutation Variable Meaning contribution importance Temperature Seasonality (standard bio4 22.5 5.8 deviation ×100) bio11 19.3 38 Mean Temperature of Coldest Quarter bio6 17.7 10.3 Min Temperature of Coldest Month Precipitation Seasonality (Coefficient bio15 8.2 9.9 of Variation) bio14 7.7 14.4 Precipitation of Driest Month bio9 3.8 2.7 Mean Temperature of Driest Quarter bio8 3.2 4.7 Mean Temperature of Wettest Quarter bio17 3.1 3.3 Precipitation of Driest Quarter bio19 3.1 1.2 Precipitation of Coldest Quarter Mean Diurnal Range (Mean of monthly bio2 2.8 0.7 (max temp - min temp)) biocateg20 1.1 1.3 Land cover bio10 1 1.2 Mean Temperature of Warmest Quarter bio12 1 0.6 Annual Precipitation biocateg21 1 0.2 Soil pH bio13 0.7 0.8 Precipitation of Wettest Month bio18 0.7 0.2 Precipitation of Warmest Quarter bio3 0.7 1.6 Isothermality (BIO2/BIO7) (×100) bio5 0.7 0.9 Max Temperature of Warmest Month Temperature Annual Range (BIO5- bio7 0.7 0.1 BIO6) bio16 0.5 1.9 Precipitation of Wettest Quarter bio1 0.2 0.1 Annual Mean Temperature

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Variable contribution & importance 40 38 35 30 25 22.5 20 19.3 17.7 15 14.4 10 10.3 9.9 8.2 7.7 5 5.8 4.7 2.73.8 3.2 3.13.3 3.1 2.8

0 1.2 0.7 1.11.3 1.21 0.61 0.21 0.70.8 0.20.7 0.71.6 0.70.9 0.10.7 0.51.9 0.10.2 Contribution/Importance (%)Contribution/Importance

Variable

Percent contribution Permutation importance

Fig. 8. Environmental variable contributions and permutation importance.

The jackknife of test gain revealed how, in isolation, bio6 (Min Temperature of Coldest Month), bio11 (Mean Temperature of Coldest Quarter), and bio1 (Annual Mean Temperature), are relatively important single variables (Fig 9; cf. dark blue bars). Comparing the red (full model) and light blue bars, instead, tells that the predictive performance improves slightly, when bio4 is omitted. Importantly, a nearly 70% decrease was found between the regularized training gain and the test gain (data not shown).

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Fig. 9. Jackknife test of importance for test data.

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Habitat suitability predictions as a function of bio6, bio11, and bio1 variations, reveal a strong and steep increase of suitability, after defined thresholds, for these three variables (Fig. 10). The plots may imply a positive correlation between presence and increased temperatures.

Fig. 10. MaxEnt prediction response as each top three environmental variables is varied (keeping all others constant).

However, Fig. 11 suggests a potential sample bias (Kramer-Schadt, Niedballa et al. 2013), by displaying a relationship, between the predicted probability of occurrence (from test data) and the proportion of occurrences selected, which is different from an ideal straight 1:1 line (cf. green line). For an optimal (unbiased) model, errors of omission (false positives) should increase linearly, like the predicted omissions.

Fig. 11. Omission rate and sample bias.

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The receiver operating characteristic (ROC) curve (Fig. 11) shows model validity, with an average test AUC for the replicate runs of 0.788 (±0.115). Standard deviation implies moderate variability across replicates. Mean AUC, however, increases rather steeply suggesting fair predictive abilities for the model, in the absence of data.

Fig. 11. Model (predictive) validity.

3.2 Future Climate Change Scenario

In the present report, habitat suitability modelling under future climatic conditions was conducted to understand the impact of climatic variables (2021-2040 monthly averages) and policies, on the orchid’s future distribution. The scenario uses a different grid cell size and reduced list of n=19 bioclimatic predictors (i.e., no categorical data), so it is not aimed at a direct comparison with the current modelled distribution (Fig. 6). The analysis (Fig. 12) suggests that, under moderately optimistic GCMs and SSPs, suitable conditions for the Red Helmet Orchid, within the present report area, may cover more than 4,220 Km2 of coastal regions between Lismore and Sydney.

Interestingly, various parameters from the future climate change scenario model showed increased robustness, compared to the current climate model. These parameters included

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Technical Report (Excerpt) average test omission rates, ROC curve, regularized training- and test- gains (data not shown). As previously found, the jackknife of test gain confirmed how, in isolation, bio6, and bio11 were the most important single variables also for the future projections.

4. Discussion

MaxEnt shows the fundamental (not the realized) niche of an organism across a study area, by estimating the relationship between observed presence at sites, and environmental variables at those sites (Elith, Phillips et al. 2011). The fundamental niche does not account for interactions between species (e.g., mutualism, commensalism, amensalism, parasitism, competition etc.), or the ability to disperse to different habitat patches. Formally, presence-only data modelling (cf. MaxEnt) cannot estimate prevalence and is affected by sampling bias (e.g., rare, vagile,

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Technical Report (Excerpt) inaccessible species etc.). Unlike presence/absence modelling, time and search scale information is also lacking (Elith, Phillips et al. 2011).

Although a bias file was used in the present report (Kramer-Schadt, Niedballa et al. 2013), the paucity of spatially clustered observations used as input data, has likely affected the predictions’ robustness (Fig. 11). To avoid multicollinearity issues, future analysis should also consider assessing the correlations between the independent variables.

Soil pH and land use seemed to affect niche suitability only marginally, and this is probably not surprising considering the homogeneity of values, for these two factors, around the occurrences (Fig. 4 and 5). Interestingly, a positive correlation between plant presence and increased temperatures may exist, so future climate warming may still be compatible with the species’ survival, providing its habitat is appropriately protected.

5. Conclusion

The present MaxEnt model suggests that suitable habitats for the endangered Red Helmet Orchid (C. dowlingii) are restricted to small coastal regions, not much larger than the areas where this rare plant has already been found (Jones 2004, Okada 2006). As this species is relatively new and underrepresented in the peer-reviewed literature, our results warrant further investigations and field campaigns to better document its distribution. This information is essential to strengthen the reliability of the future modelling efforts (Elith, Phillips et al. 2011).

Although the parameters used in this study also suggests that future (moderately optimistic) climatic conditions may not impact dramatically the distribution of the species (which could even benefit from warmer climates), it is important to recognize the caveats and assumptions of the present analysis, as well as the incompleteness of the list of explanatory factors used. Under a precautionary approach, the present analysis recommends a revision of the conservation status of the areas harbouring documented occurrences (NSW Government - NSW Threatened Species Scientific Committee 2020), especially considering the ongoing habitat degradation and reduction trends already denounced.

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