Maxent Modeling for Predicting the Potential Distribution Of
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Ecological Engineering 92 (2016) 260–269 Contents lists available at ScienceDirect Ecological Engineering jo urnal homepage: www.elsevier.com/locate/ecoleng Maxent modeling for predicting the potential distribution of endangered medicinal plant (H. riparia Lour) in Yunnan, China a,b,∗ a,d a,b c Yu-jun Yi , Xi Cheng , Zhi-Feng Yang , Shang-Hong Zhang a Ministry of Education Key Laboratory of water and sediment Science, School of Environment, Beijing Normal University, Beijing 100875, China b State Key Laboratory of Water Environment Simulation and Pollution Control, School of Environment, Beijing Normal University, Beijing 100875, China c Renewable Energy School, North China Electric Power University, Beijing 102206, China d Environmental Protection Bureau of Yaohai District, Anhui, Hefei, 230012, China a r t i c l e i n f o a b s t r a c t Article history: Climate change influences ecosystem by altering the habitat of species in it. We report the quantitative Received 27 July 2015 predictions of climate change on riparian species. Homonoia riparia (H. riparia) Lour, a species native to Received in revised form 21 April 2016 Yunnan Province, China, is a medicinal plant with high ecological and economic value. Its population has Accepted 22 April 2016 declined significantly, and the species has become locally endangered in recent decades. Understanding the habitat requirements of this species, evaluating habitat quality, and predicting its potential habitat Keywords: are significant for protecting H. riparia Lour. One positional variable, three topographic variables and Climate change eight bioclimatic variables were used to model its distribution and potential habitat. The eight main Habitat suitability simulation Maxent bioclimatic variables influencing species distribution were selected from 19 bioclimatic variables based on correlation analysis and principal component analysis. An MAXENT model, because of the advantages Species distribution models (SDMS) Plant-climate interactions of using presence-only data and performing well with incomplete data, small sample sizes and gaps, was employed to simulate the habitat suitability distribution. The results show that seven variables, namely, annual mean temperature, altitude, precipitation seasonality, precipitation of coldest quarter, the distance to the nearest river, temperature seasonality, and precipitation during the driest month, are significant factors determining H. riparia Lour’s suitable habitat. Habitat suitability for three historical periods and two future climate warming scenarios were calculated. The habitat suitability of H. riparia Lour in Yunnan Province is predicted to improve with global warming. © 2016 Published by Elsevier B.V. 1. Introduction Lindenmayer 2007). Several reasons, such as climate change and land use change, may shrink, degrade or destroy the habitats of An organism’s habitat is the combination of the space it inhabits wild animals and plants (Grimm et al., 2008; Yang et al., 2015). Ilex and all eco-factors in that space, including the abiotic environ- khasiana Purk, a tree species of northeastern India, was critically ment and other organisms that are necessary for the existence of endangered by habitat loss; only approximately 3000 individu- individuals or groups. Habitat quantity and quality have a signifi- als of Ilex khasiana Purk currently survives (Adhikari et al., 2012). cant impact on a species’ distribution and species richness within The demands of an ever-increasing human population – the most environments. Habitat loss affects the spatial pattern of residual important being of land for agriculture, industry and urbanization – habitat and induces microclimatic change and habitat fragmen- has strong impacts on the habitat of Malabar nut (Justiciaadhatoda tation (Purves and Dushoff 2005). Thus, habitat loss has negative L.), a medicinal plant. The population of Malabar nut (Justiciaadha- effects on species richness that may be of long duration and high toda L.) is shrinking in India’s Dun Valley due to habitat loss (Yang intensity (Kruess and Tscharntke 1994; Anadón et al., 2014). Habi- et al., 2013). By 2010, approximately one-fifth of all of the world’s tat loss is the main reason for species endangerment, species plants species were at risk of extinction (Brummitt and Bachman extinction and biodiversity loss (Tilman et al., 2001; Fischer and 2010). H. riparia Lour is a rheophyte native to Yunnan Province. It is a medicinal plant with high ecological and economic values. Its abun- ∗ dance has decreased sharply in recent decades. A field investigation Corresponding author at: Ministry of Education Key Laboratory of water and sed- iment Science, School of Environment, Beijing Normal University, Beijing 100875, in 1984 (before the construction of Manwan reservoir) showed that China . H. riparia Lour was present in at least four habitats in the Manwan E-mail address: [email protected] (Y.-j. Yi). http://dx.doi.org/10.1016/j.ecoleng.2016.04.010 0925-8574/© 2016 Published by Elsevier B.V. Y.-j. Yi et al. / Ecological Engineering 92 (2016) 260–269 261 Fig. 1. (a) Map of China and the location of Yunnan Province; (b) and (c) H. riparia Lour; (d) distribution of the main rivers and the H. riparia Lour’s presence points in Yunnan Province. Fig. 2. The results of the AUC curves in developing H. riparia Lour’s habitat suitability model. (The red (training) line shows the “fit” of the model to the training data. The blue (testing) line indicates the fit of the model to the testing data and is the real test of the model’s predictive power.). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.) reservoir area, and its abundance was significantly more than 400; ing suitable survival conditions for H. riparia Lour are crucial to its only one habitat among these four remained in 1997 (after Manwan conservation. reservoir’s construction). Two habitats in the Manwan reservoir The first task was to understand how the environment struc- lake and one habitat below Manwan dam were flooded. The only tures the distribution of H. riparia Lour. To do so, we built a species remaining H. riparia Lour are scattered throughout the floodplain distribution model (SDM) as a function of climate, topography and between the upstream stretches and the estuary of Luozha river, location. Species distribution models (SDMS) mainly use distribu- but their condition in 1997 was worse than that in 1984 (Wang tion data of species (presence or absence) and environmental data et al., 2000). However, few studies of the habitat quality of H. riparia to algorithmically estimate species’ niches, and then project those Lour have been undertaken. Consequently, researching the habi- niches onto the landscape, reflecting a species’ habitat preferences tat preferences of H. riparia Lour, developing a habitat suitability in the form of a probability (Guisan and Thuiller 2005; Elith and model to calculate the spatial distribution of this species, and seek- Leathwick, 2009). The results can be explained as the probabil- ity of species presence, species richness, habitat suitability, and 262 Y.-j. Yi et al. / Ecological Engineering 92 (2016) 260–269 Fig. 3. The results of the jackknife test of variables’ contribution in modelling H. riparia Lour’s habitat distribution. (The regularized training gain describes how much better the Maxent distribution fits the presence data compared to a uniform distribution. The dark blue bars indicate that the gain from using each variable in isolation, the light blue bars indicate the gain lost by removing the single variable from the full model, and the red bar indicates the gain using all of the variables). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.) so on. SDMS has been used to predict the ranges of plant dis- ables and bioclimatic variables); (3) the habitat suitability of H. eases and insects, model the distributions of species, communities riparia Lour in three historical periods (1950–1959, 1975–1985 and or ecosystems, assess the impact of climate, land use and other 2000–2009) were simulated using the developed model; and 4) environmental changes on species distributions (Thomas et al., the potential habitats of H. riparia Lour under two climate warm- 2004; Yi et al., 2014a), evaluate the risk of species invasion and ing scenarios (RCP2.6 and RCP8.5, given by the IPCC) were pre- proliferation (Peterson 2003; Beerling et al., 2009), identify unsur- dicted. veyed areas with high suitability for precious endangered species (Raxworthy et al., 2003), contribute to the site selection of natu- ral preserves (Ferrier 2002), and identify target areas for species 2. Study area and species reserves and reintroductions (Adhikari et al., 2012). Typical SDMS include MAXENT (Phillips et al., 2004), BIOCLIM (Busby 1991), 2.1. Study area DOMAIN (Carpenter et al., 1993), GAM (Yee and Mitchell 1991), GLM (Lehmann et al., 2002), BIOMAPPER (Hirzel and Guisan, 2002), Yunnan Province is located in southwestern China, and so on. ◦ ◦ ◦ ◦ (21 8 N–29 15 N, 97 31 E–106 11 E). With a total area of approx- SDMS is based on presence and absence data, which may be imately 390,000 square kilometers (Fig. 1a and d). The north side obtained from field investigation, specimen records, and litera- is higher than the south side in Yunnan Province, and significant tures. In practice, it is very difficult to obtain absence data. Even temperature differences exist between the north and south. The when absence data can be obtained, it is unreliable. Presence data climate of Yunnan varies regionally and with altitude. The seasonal for rare and endangered species is also limited. Elith et al. (2006) temperature difference is small, and the diurnal temperature used 16 methods to model the distributions of 226 species from six difference is large. Rainfall is plentiful, with clearly delineated regions around the globe. The results indicated that the predictive wet and dry seasons, but precipitation is not uniform throughout ability of Maxent was always stable and reliable, and it outper- the province.