NORTH-WESTERN JOURNAL OF ZOOLOGY 14 (2): 165-170 ©NWJZ, Oradea, Romania, 2018 Article No.: e171508 http://biozoojournals.ro/nwjz/index.html
Potential effects of climate change on the distribution of the endangered Darwin’s frog
Johara BOURKE1,2,*, Klaus BUSSE1 and Wolfgang BÖHME1
1. Zoologisches Forschungsmuseum Alexander Koenig (ZFMK), Adenaueralle 160, D-53113 Bonn, Germany. 2. Tierärztliche Hochschule Hannover, Bünteweg 17, 30559 Hannover, Germany. *Corresponding author, J. Bourke (2nd address), E-mail: [email protected]
Received: 24. June 2016 / Accepted: 26. July 2017 / Available online: 29. July 2017 / Printed: December 2018
Abstract. Herein, we gathered 109 historic and current records of the endangered Darwin’s frog (Rhinoderma darwinii) to be able to model its potential current and future distribution by means of expert opinion and species distribution model (Maxent). Results showed that R. darwinii potential current distribution is much wider than the known one, mainly due to anthropogenically modified areas which were concentrated in the Chilean central valley. We identified a major threat to Darwin’s frog existence, namely isolation of the population between the Coastal and Andean mountain ranges. On the other hand, SDM projections onto future climate scenarios suggest that in 2080, the frog’s climatically suitable areas will expand and slightly shift southward. However, due to current ongoing threats, such as habitat destruction, we encourage further monitoring and surveying of Darwin’s frog populations and increased protection of their habitat, especially at the central valley.
Key words: Global warming, land use change, anthropogenic habitat destruction, distribution range shift, Rhinoderma.
Introduction province in Chile (Rabanal & Nuñez 2009) and in Neuquén and Río Negro provinces in Argentina (Úbeda et al. 2010) at Among all vertebrates, amphibians are currently the most altitudes ranging from the sea level to 1500 m a.s.l. (Crump threatened group (e.g., Stuart et al. 2004, Stuart et al. 2008, & Veloso 2005). Individuals can be found hidden under leaf Wake & Vredenburg 2008). A wide range of likely reasons litter, moss layers, fallen tree bark and swimming in rain have been suggested to explain this worldwide phenome- puddles (Codoceo 1956) at the most shadowed parts in the non, wherein habitat destruction / alteration, contaminants, forest (Codoceo 1956, Cei 1962) as well as at forest gaps with invasive species, and large scale disease transmissions are direct solar radiation (Codoceo 1956, Crump 2002). Next to the most frequently cited causes (e.g., Stebbins & Cohen natural environments, it also inhabits moderately anthropo- 1995, Sparling et al. 2000, Linder et al. 2003, Semlitsch et al. genically disturbed areas such as pastures or logged forest, 2003). Additionally, amphibians are one of the most suscep- as well as swampy areas next to slow flowing creeks (Crump tible groups to climate change within vertebrates (IUCN 2002, Crump & Veloso 2005) or other running waters (Cei 2008), due to their biphasic life histories making them highly 1962). dependent on environmental factors (Corn 2005). Global Based on a comprehensive literature screening, assess- climate change has been suggested as one of the major ments of museums specimens and recent field surveys, we agents causing amphibians declines, with both direct and herein build a spatially explicit quantitative species distribu- indirect effects at the individual, population and community tion model (SDM; cf. Guisan & Zimmermann 2000, Guisan & level (Blaustein et al. 2010). Furthermore, species distribution Thuiller 2005, Elith & Leathwick 2009) to assess the current modelling (SDM) suggests shifts in amphibian ranges when and future climatically suitable areas of R. darwinii. Such projected onto future climate change scenarios (e.g. Araújo models have frequently proven to be useful tools for evalu- et al. 2006). Whether a species can survive these shifts in ating habitat suitability over large areas and also for guiding their distribution depends on its vagility and the habitat further survey efforts (Edwards et al. 2005, Peterson et al. permeability around populations (e.g. Scoble and Lowe 2005). In a similar vein, our model may aid in identifying po- 2010). The interactions between all these factors are complex tential threats to R. darwinii and may help prevent the extinc- (Blaustein et al. 2010). However, any throughout assessment tion of this unique frog, which is probably the last remaining on how amphibians will be affected by climate change re- representative of the genus Rhinoderma. quires knowledge on the species’ biology, habitat require- ments and its’ geographic distribution. Herein, we focus on Darwin’s frog (Rhinoderma darwinii), Material and methods which is an endangered and unique anuran due to its paren- Species records tal care, where males brood tadpoles in their vocal sacs We gathered 109 Rhinoderma darwinii records from literature, zoo- (Jiménez de la Espada 1872) until metamorphosis is finished logical collections and from the Global Biodiversity Information Fa- (Bürger 1905). Due to severe population declines (Young et cility (GBIF; www.gbif.org) and HerpNet databases al. 2001) R. darwinii is globally classified as vulnerable by the (www.herpnet.org). Of these, 99 records were from Chile and 10 IUCN (Úbeda et al. 2010) and locally in Chile as endangered from Argentina (Supplementary material). Geo-referencing was (Nuñez et al. 1997, SAG 1998). It is endemic to the Valdivian conducted whenever necessary with Google Earth forest, a rainy temperate forest which occurs in the south of (www.googlearth.com). All data was checked in DIVA-GIS 7.4 (Hi- jmans et al. 2005a) to spot possible errors. Chile and the border with Argentina. The specific distribu- tion was unclear for many years because of frequent confu- Current climate data sion with its sister species R. rufum due to morphological Information on average climate conditions between 1960 and 1990 similarities. Today, a few populations of R. darwinii are for Chile and bordering areas in Argentina was obtained from the known from the area between Concepción and the Palena WorldClim database, version 1.4 (www.worldclim.org, Hijmans et 166 J. Bourke et al. al., 2005b). Based on this climate data with a spatial resolution of 30 cover (closed and open, evergreen and deciduous) corresponding to arc-sec (approximately 1 × 1 km) throughout the study area, biocli- GLC200 classes 11 and 12). The remaining land cover classes were matic variables (cf. Busby 1991, Beaumont et al. 2005) were check considered as unsuitable. with DIVA-GIS. Because temperature is a key factor influencing ec- tothermic species, related to energetic balances and digestive turn- Future climate over rates, we selected all variables related to temperature. Also, as To establish the effect of climate change on R. darwinii we considered humidity is a factor limiting the distributions for amphibians, we in- climate change projections based on the CCMA, CSIRO, and cluded all variables related to precipitation. For model building and HADCM3 models (Flato et al. 2000, Gordon et al. 2000) and the to minimize predictor correlation, we selected a subset of these vari- emission scenarios informed in the Special Report on Emission sce- ables with a pair-wise intercorrelation r < 0.75, which was assessed narios (SRES) by the Intergovermental Panel on Climate Change using Pearson’s correlation coefficients. In those cases where two (2001); IPCC 4 from CIAT (http://www.grida.no/climate/ variables were inter-correlated to a higher degree we selected the ipcc/emission/). The families of emission scenarios were founded putatively biologically more important. The final variable set com- on future production of the greenhouse gases and aerosol emissions. prised “minimum temperature of the coldest month” (bio6), “tem- We selected the SRES scenarios of A2a and B2a, which are available perature annual range” (bio7), “mean temperature of the warmest through www.worldclim.org with a resolution of 2.5 arc-min. Each quarter” (bio10), “precipitation of coldest quarter” (bio19), “precipi- of the selected scenarios described one possible demographic, politic, tation of driest quarter” (bio17) and “precipitation seasonality” economic, social and technological future expected for 2080. Scenario (bio15). B2a represents a more divided world and more ecologically friendly. Compared to B2a, scenario A2a emphasizes regionalized solutions to Species distribution modeling economic and social development, but it is less environmentally con- Using a maximum entropy approach, we modelled the climatically scious and population size is increasing faster (Rödder 2009). suitable areas of R. darwinii with Maxent 3.3.3e (www.cs.princeton. edu/ ~shapire/maxent) (Phillips et al. 2004, Phillips & Dudík, 2008, Elith et al. 2010) applying the default program settings and the logis- Results tic output format ranging from 0 (unsuitable environmental condi- tions) to 1 (optimal) (Phillips & Dudík 2008). During model training We obtained a good SDM with an average training AUC Maxent automatically samples random background data from the value of 0.849 (ranging from 0.786 to 0.987). The lowest ob- training region in order to assess the general structure of the envi- served Maxent value at the species records was 0.113 and the ronmental space, which ideally comprises those areas potentially suitable to the target species (e.g., Phillips 2008, Barve et al. 2011). In lowest 10% training omission threshold was 0.260. Analyzes order to meet this requirement, we restricted the training area to an of variable contributions in the SDM revealed that the “pre- area defined by a 150 km radius enclosing all species records follow- cipitation of coldest quarter” with 39.7% had the highest ex- ing Bourke et al. (2012). Maxent automatically allows for model test- planative power, followed by “precipitation of driest quar- ing using the Area under the Curve (AUC), referring to the ROC ter” (18.7%), “precipitation seasonality” (17.3%), “tempera- (Receiver Operation Characteristic) curve (Swets 1988). AUC scores ture annual range” (13.9%), “minimum temperature of the quantify the SDM’s ability to differentiate between random predic- driest quarter” (6.8%) and “mean temperature of the warm- tion (AUC = 0.5) and perfect identification of suitable grid cells (AUC = 1.0) (Hanley & McNeil 1982, Phillips et al. 2006). The robust- est quarter” (3.4%). ness of the models was assessed using 100 repetitions each trained The climatically suitable area suggested by our SDM is with 70% of the available species records and tested using the re- more similar to the former distribution areas (Fig. 1A). Our maining 30%. The average Maxent value per grid cell across all repe- SDM differed from former distributions due to missing re- titions was subsequently computed as ensemble prediction and the cords at the southern area (Fig. 1A; Formas 1979) and at the SDM was projected onto the future climate change scenarios. The northern area (Fig. 1B; Úbeda et al. 2010). The pres- relative contribution of each variable to the final model was auto- ence/absence map given by SDM built based on bioclimatic matically assessed in Maxent and possible uncertainties arising when data (Fig. 1C) included the Chilean central valley, but for a projecting the SDM onto bioclimatic conditions exceeding those pre- sent in the training region were assessed using multivariate envi- more likely distribution the unsuitable habitat where ex- ronmental similarity surfaces (Elith et al. 2010). cluded from the map (Fig. 1D). To give independency to species records and avoid an overesti- Rhinoderma darwinii’s climatically suitable areas in the fu- mation of prediction based on over sampled areas. To know R. dar- ture climate changes scenarios indicated that the range may winii potential current distribution we created a presence/absence become widespread, especially towards the south under the map where the species presence was considered when Maxent val- A2a and B2a scenarios (Fig. 2B-C). Only minor differences ues were over 10% training omission and over the minimal training were observed in model projections onto climate change presence. scenarios derived from CCCMA, CSRIRO and HADCM3 Land cover information GCMs. Although a SDM derived from bioclimatic conditions may accurately depicture a species’ broad scale distribution it may overestimate its distribution on a regional scale due to inclusion of areas with unsuit- Discussion able microhabitats. To minimize this likely overestimation, we classi- fied suitable land cover types that are available through the Global This paper provides the first map of the current potential Land Cover Information Facility (GLC2000, http://bioval.jrc.ec. distribution and future climatically suitable areas for R. dar- europa.eu/products/glc2000/glc2000.php). Based on micro-habitat preferences gathered from the literature (see introduction) and our winii, which were derived from bioclimatic data and species own field experience, we considered as potentially suitable habitats records compiled from specimens housed in zoological col- the following land cover types: tree cover (broadleaf and needle leaf, lections, publications and personal communications. mixed leaf type, evergreen and deciduous, open and closed, regu- As suggested by our SDM, environmentally suitable ar- larly flooded by fresh water) corresponding to the GLC2000 classes 1 eas for R. darwinii are situated between the Concepción pen- to 7; mosaic tree cover and other natural vegetation (class 9); shrub insula and continental Chiloé (38 and 45°S). This distribution Darwin’s frog distribution and its threats 167
Figure 1. Rhinoderma darwinii distribution maps and suitable land covers. (A) Historical distribution given by Formas (1979) in light brown and species records collected between 1880 and 1990; (B) current distribution given by Úbeda et al. (2010) in light blue and species records compiled between 1997 and 2015 ; (C) presence/absence map built based on climatically suitable areas , in orange SDM over (>) minimal training presence, and in yellow SDM over (>) 10% training omission; selected suitable land cover classes and anthropogenically modified areas (D) with selected suitable land covers in dark green and anthropogenically modified areas in brown (cf. material and methods and Table 1 for more details). Species records are shown as open squares from Formas (1979), as open triangles from Formas (1975) and as open circles from Cei (1962). Species museum’s records are shown as filled circles and recent observations as x. All species records included at SDM are shown as filled squares. More information can be found in supplementary material.
Figure 2. Climatically suitable areas of Rhinoderma darwinii as expected in 2080 assuming (A) current (B) A2a and (C) B2a bioclimatic condi- tions. The maps show the mean Maxent scores derived from 100 models, each projected onto CCCMA, CSIRO and HADCM3 scenarios of the IPCC story lines A2a and B2a.
pattern differs from former ones mainly due to records that cluded by them (from Codoceo 1956, Cei 1962, Kilian 1965, by mistake were not included at former maps and lack of Formas 1979). At the extreme southern part, we also found surveys at areas of difficult access. This is expected, as geo- differences because no species record is known for this area graphical distributions of most species are poorly under- and Úbeda et al. (2010) supposed it presence. Although it is stood and usually contain many gaps (the Wallacean short- possible that this species could be found in the extreme fall; see Brown and Lomolino, 1998). Our current SDM dif- south, surveys in this area are difficult due to limited acces- fers from the distribution pattern proposed by Úbeda et al. sibility. On the other hand, Formas (1979) suggested a distri- (2010) especially at the northern part, due to data not in- bution also different to ours at the southern part, because he 168 J. Bourke et al. ignored species records in the south area from former au- tion declines worldwide (Daszak et al. 2003) was found pre- thors (from Codoceo 1956, Cei 1962, Kilian 1965) and mu- sent in R. darwinii populations in Chile (Bourke et al. 2010, seum specimens (MZUC from years 1956-1958). Bourke et al. 2011, Soto-Azat et al. 2013). But the relation be- The SDM suggested that climatically suitable areas are tween Bd with future climatic conditions is controversial extended. Unfortunately, at the central valley (between the (Rohr & Raffel 2010). Some authors have concluded that cli- coastal and Andean mountain ranges), agriculture and live- mate change was driving amphibian declines, presumably stock are concentrated, making the habitat unsuitable for R. by increasing disease risk (Bosh et al. 2007, D’Amen & darwinii. Because of this, current records are found mainly Bombi 2009) because the correlation of increase of air tem- in the coastal and Andean mountain range and only few in perature and amphibian extinctions at several locations the central valley, and exclusively situated in the last re- across the globe (Daszak et al. 2000, Hay et al. 2002; Jones et maining patches of unmodified vegetation. In summary, the al. 2008, Lafferty 2008, Rohr et al. 2008). However, this posi- SDM appears to be largely consistent with the distribution tive correlation was temporally confounded, so that virtually patterns derived only based on R. darwinii records, but fur- any variable that increased in the 1980s and early 1990s ther survey efforts should be carried out in non-surveyed ar- would be correlated positively with these amphibian losses eas. The most important variables according to the SDM (Rohr et al. 2008). Furthermore, a decrease in the future dis- were related to precipitation, which fits the characteristics of tribution of Chytrids under global warming conditions was the known habitat of R. darwinii: the Valdivian temperate predicted (Rödder et al. 2010). But climate fluctuations on rainforest. Darwin’s frogs are found in forest leaf litter far the other hand would affect frogs' ability to fight the fungus, away from permanent water sources, so temporary rain probably because temperature fluctuations could have pools are of extreme importance. strong effects on the immune system (Rohr & Raffel 2010, In spite of the accuracy of the SDM obtained from bio- Raffel et al. 2013). Additionally, climate change can also alter climatic factors because of their strong correlation with spe- pathogen-host dynamics and influence how diseases are cies’ ecology, SDM models tend to overpredict the distribu- manifested and can interact with other stressors such as UV- tion limits of species, because they are not able to evaluate B radiation and contaminants (Blaustein et al. 2010). absences due to species’ evolutionary history, dispersal limi- For these reasons, we suggest further survey for R. dar- tations, and biotic interactions with other species (Graham & winii populations at understudied potential habitats to Hijmans 2006, Pineda & Lobo 2009). For this reason, to im- gather more information on species’ requirements and be prove results, we suggest including in future SDMs more able to acquire a better understanding on this species. Also, ecological parameters that could affect this species, as well central valley habitats should be protected to avoid isolation as to compare different settings and include other algorithms and increase the chances to conserve this unique and endan- besides Maxent to consider intermodal variation and synthe- gered frog as well as other endangered Chilean species. size their outcomes in a final consensus model. Projecting R. darwinii’s climatically suitable areas onto future climate changes scenarios indicated that climatically Acknowledgments. We thank all the people that shared their suitable areas may become more widespread, especially to- Darwin’s frog localities records, D. Rödder for methodology and wards the south. A potential change in species distribution suggestions, F. Ahmadzadeh for his help on methodology, A. due to future climatic conditions has been previously re- Hinojosa for shared literature, and H. Werning for co-funding the ported (e.g. Chen et al. 2011). In general, climate change is Rhinoderma project. This research was funded by Chester Zoo/North driving species distribution poleward (Parmesan et al. 2006). of England Zoological Society in collaboration with Leipzig Zoo, Amphibians are predicted to shift their distribution toward ZGAP and Reptilia. J. Bourke acknowledges a grant from DAAD- higher latitude and altitudes, expanding their distribution in CONICYT and Becas Chile. case of several European species (Blank et al. 2014, Araújo et al. 2016) or contracting it in case of South American treefrogs
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+ Supplementary material – available exclusive online (7 pages, 1 Table)