Abstract
Climate change is significantly affecting ecosystem services and leading to strong impacts on the extent and distribution of glaciers and vegetation. In this context, species distribution models represent a suitable instrument for studying ecosystem development and response to climate warming. This study applies the maximum entropy model, MaxEnt, to evaluate trends and effects of climate change for three environmental indicators in the area of the Alpi Marittime Natural Park under the Municipality of Entracque (Italy). Specifically, this study focuses on the magnitude of the retreat of six glaciers and on the distribution of two different plant communities, Alnus viridis scrub and Fagus sylvatica forest associated with Acer pseudoplatanus and tall herbs (megaforbie), in relation to predicted increases in mean temperatures. MaxEnt software was used to model and observe changes over a thirty-year period, developing three scenarios: a present (2019), a past (1980) and a future (2050) using 24 “environmental layers”. This study showed the delicate climate balances of these six small glaciers that, in the next 30 years, are likely to undergo an important retreat (≈−33%) despite the high altitude and important snowfall that still characterize the area. At the same time, it is predicted that the two plant communities will invade those higher altitude territories that, not so long ago, were inhospitable, expanding their habitat by 50%. The MaxEnt application to glaciers has shown to be an effective tool that offers a new perspective in the climate change field as well as in biodiversity conservation planning.
Highlights
Global climate change has an impact on indicator species and ecosystem dynamics [1] and significantly affects the services provided by ecosystems in any geographical area [2,3,4,5]
The statistical analysis, i.e., the graph of the receiver operating characteristic ROC, indicates the accuracy of the model, whereas the predictive maps identify the probability that the three environmental indicators are present in the study area
To clearly highlight the areas suitable for the three environmental indicators, only the two classes of teristic ROC, indicates the accuracy of the model, whereas the predictive maps identify the probability that the three environmental indicators are present in the study area
Summary
Global climate change has an impact on indicator species and ecosystem dynamics [1] and significantly affects the services provided by ecosystems in any geographical area [2,3,4,5]. Global warming effects could be severe, and the temperature will continue to rise over the 21st century by minimum 0.3–1.7 ◦C to maximum 2.6–4.8 ◦C [6] with dramatic consequences for glacier reduction and vegetation distribution In this perspective of change, the ecological indicators traditionally used to assess the state of the ecosystem may become increasingly unreliable [7]. The multiple drivers of global change may shift baselines and emergent novel properties of ecosystems In this context, the study of the evolution of ecosystems is a suitable instrument that makes it possible to predict habitat development as well as to estimate geographic distribution [8]. To predict the effects of climate change, the most implemented methods are: climatic envelope models such as BIOCLIM [16], genetic algorithms such as GARP [17], ecological niche factor analysis (ENFA) [18], generalized additive models (GAM) [19], generalized linear models (GLM) [20] and maximum entropy models such as MaxEnt [21]
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