Invasive Plant Species Establishment and Range Dynamics in Sri Lanka under Climate Change

Abstract

Plant invasion has been widely recognized as an agent of global change that has the potential to have severe impacts under climate change. The challenges posed by invasive alien plant species (IAPS) on biodiversity and ecosystem stability is growing and not adequately studied, especially in developing countries. Defining climate suitability for multiple invasive plants establishment is important for early and strategic interventions to control and manage plant invasions. We modeled priority IAPS in Sri Lanka to identify the areas of greatest climatic suitability for their establishment and observed how these areas could be altered under projected climate change. We used Maximum Entropy method to model 14 nationally significant IAPS under representative concentration pathways 4.5 and 8.5 for 2050 and 2070. The combined climate suitability map produced by summing up climatic suitability of 14 IAPS was further classified into five classes in ArcMap as very high, high, moderate, low, and very low. South and west parts of Sri Lanka are projected to have potentially higher climatic suitability for a larger number of IAPS. We observed suitable area changes (gains and losses) in all five classes of which two were significant enough to make an overall negative impact i.e., (i) contraction of the very low class and (ii) expansion of the moderate class. Both these changes trigger the potential risk from IAPS in Sri Lanka in the future.The challenges posed by invasive alien plant species (IAPS) on biodiversity, ecological processes and ecosystem services, particularly in island countries, is increasing and well recognized [11]. This issue continues to be a challenge and increasingly important regardless of rigorous control and management efforts. Species invasion has been facilitated by the rapid growth of trade and transportation between countries, especially after the industrial revolution [12]. The natural barriers (i.e., mountains, rivers) that provided isolation required for unique species to evolve is no longer effective [7]. Understanding the current and potential distribution patterns is fundamental for managing IAPS [13][14][15]. Species distribution models (SDMs), which are based on ecological assumptions and theories, investigate how species shift their ecological niche spatially and temporally under climate change [16][17][18][19]. Hutchinson defined the niche as n-dimensional hyper-volume within which a species can survive and reproduce; in the absence of biotic interactions this volume is equal to the species' fundamental niche [16]. However, under given circumstances, a species will usually only occupy a certain part of the fundamental niche, which is called the realized niche [20]. Therefore, theoretically, SDMs estimate a species' potential distribution rather than the actual distribution. When the species niche is projected to a geographical space, it yields a predictive map of species' presence [21,22]. But there is a clear uncertainty associated with the changing climate which could be due to several reas...

Modelling Climate Suitability for Rainfed Maize Cultivation in Kenya Using a Maximum Entropy (MaxENT) Approach

“…A multicollinearity test was undertaken using R-program Package 'virtualspecies' (version 1.4-4) to screen highly correlated environmental predictors among the 19 bioclimatic variables that represent a combination of annual trends, extreme environmental conditions and seasonality. In recent SDM literature, this package has been used for variable selection [17]. The temperature-related variables include the annual mean temperature (BIO 1 ), diurnal temperature range (BIO 2 ), Isothermality (BIO 3 ), temperature seasonality (BIO 4 ), maximum temperature of warmest month (BIO 5 ), minimum temperature of the coldest month (BIO 6 ), annual temperature range (BIO 7 ), mean temperature of the wettest quarter (BIO 8 ), mean temperature of the driest quarter (BIO 9 ), mean temperature of the warmest quarter (BIO 10 ), and the mean temperature of the coldest quarter (BIO 11).…”

Potential Risks of Plant Invasions in Protected Areas of Sri Lanka under Climate Change with Special Reference to Threatened Vertebrates

“…Sri Lanka, being an island country located in the tropics, faces potential risks of IAPS invasion on native biodiversity, which can be challenging. In particular, south-west Sri Lanka is likely to face a high risk from multiple IAPS invasion under projected climate change [29]. Island species are generally highly susceptible to climate change-associated negative influences [4].…”

“…Even though species distribution models are frequently used and vital tools, there are some common limitations, and due to the level of uncertainty associated with modeling techniques, the general circulation models (GCMs) and representative concentration pathways (RCPs) used may result in serious implications for model performance [7,14,81]. We have discussed the general limitations of MaxEnt models and the approaches used to overcome them by Kariyawasam et al [29], with particular attention paid to the uncertainty associated with the model building, spatial scales, and selection of data. Thus, some limitations that are relevant to the present study are discussed here.…”

“…CR: Critically endangered; EN: Endangered; VU: Vulnerable. The number of endemic species is given within brackets (Source: Weerakoon [27] (39) This study is an extension of a previous work by Kariyawasam et al [29] that used the MaxEnt niche model to define potential overlapping areas of 14 IAPS of national significance in Sri Lanka (Table S1). MaxEnt is underpinned on the maximum entropy principle [30].…”

“…In this study, the combined maps developed under current and future climate scenarios were classified into five classes-very low (0 IAPS), low (1-2 IAPS), moderate (3-4 IAPS), high (5-6 IAPS), and very high (7-8 IAPS)-using manual classification. Further details that are relevant to model building and model results were provided by Kariyawasam et al [29].…”