Extinction risks, range sizes and range shift

Climate change and land use land cover change (LULCC) are threatening global

biodiversity (Watson 2014, Jantz et al. 2015, Mantyka-Pringle et al. 2015). Global warming,

deforestation and forest degradation are expected to directly impact and modify future plant

habitats (Corlett 2011). As a result, species ranges may contract, expand or experience

upslope or downslope displacement, and in some places, species extinctions may occur

(McCain and Colwell 2011, Zelazowski et al. 2011, Hong-Wa and Arroyo 2012, Kuhn et al.

2016). For other regions, changes in species ranges will cause lowland attrition and the

emergence of upper-zone specialists (Colwell et al. 2008, Laurance et al. 2011a). Plants in

the tropics may be the most vulnerable to these emerging environmental pressures mostly

because many tropical species are thought to already occupy their extreme ranges (Brown

2014, Marta et al. 2016, Males 2018). Moreover, projections for forests in Amazonia suggest

that future climate analogs may be eliminated and lead to increase distances between current

and future habitats by approximately 300 – 475 km by 2050 (Feeley and Rehm 2012),

thereby causing range-shift gaps between current and future suitable habitats (but see

Petitpierre et al. (2012). For plants, it is predicted that if average global temperatures were

to rise above 3o_{C, then more than one-half of current suitable habitats would be lost (Warren}

et al. 2018). LULCC and climate change may act synergistically, with deforestation

increasing the risk of reduction to future suitable habitats by approximately 30% or 55%

under the low or high carbon emissionscenarios, respectively (Feeley et al. 2012, Brown et

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Several studies have investigated plant responses to future climate and LULCC by

comparing predictions of range-shift from niche-based and process-based models7 _(Thuiller

2004, Morin and Thuiller 2009), focusing on differences in realised thermal niches in

lowland and montane species (Feeley and Silman 2010b) or using spatial metrics for

vulnerability assessments (Choe et al. 2017). Recently, emphasis has been placed on building

more robust, spatially-explicit or hybrid species distribution models (such as hierarchical

Bayesian models; see chapter 6) for predictions of range-shift gaps under different

environmental scenarios (Zurell et al. 2016). This approach is particularly relevant because

it conveniently allows for the incorporation of less commonly used abiotic variables as

predictors, whilst accounting for spatial bias in the model. Some examples include the: (i)

inclusion of soil data in a Bayesian logistic regression model to predict the distribution of

Amazonian plant species (Figueiredo et al. 2018) and (ii) incorporating species-specific

physiological data while explaining uncertainties in plant species predictions in the Amazon

basin (Feng et al. 2018). For Madagascar, applications of recent analytical techniques lag

behind other biodiversity hotspots (e.g., the Amazons), despite evidence that

disproportionate changes to plant diversity patterns is expected for the region due to climate

and LULCC (Brown et al. 2015), range contraction and expansion of some endemic species

(Hong-Wa and Arroyo 2012) and extinctions to more than half the species in one entire

genera (i.e., Coleeae) (Good et al. 2006). Regardless of geographical location, few studies

assess future risks, habitat vulnerabilities and consider the influence of dispersal capabilities

when predicting range-shifts (Elith et al. 2010, Choe et al. 2017). This is especially important

for sub-Saharan tropical forests where environmental changes will inevitably cause shifts in

species ranges (Kreyling et al. 2010, Pienaar et al. 2015, Ryan et al. 2016).

7_{Niche-based models rely on the establishment of statistical or theoretical relationships between environmental predictors and observed} species distribution; while process-based models predict the response of an individual or population to environmental conditions by explicitly incorporating biological processes calibrated with observations on individual species

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Until recently, the application of species-specific and process-based distribution

models at regional scale has been rare, despite the overwhelming evidence of their

importance for biodiversity conservation (Zurell et al. 2016). Application of either model to

regional assessment of species distribution is important for conservation schemes, especially

because studies conducted at global-scales tend to obscure regional and/or local patterns of

habitat loss and extinction risks (Boakes et al. 2018). However, environmental scientists still

rely to a large extent on abiotic factors as explanatory variables when predicting spatial

patterns across large geographical areas. Other explanatory variables that are indicative of

biotic interactions (such as competition) at fine scales are often neglected (Staniczenko et al.

2017). However, Schliep et al. (2018) show that dynamic local interactions among species

in a community can affect occurrence and/or abundance of any give species when predicting

range-shift. Suggesting that including inter and intra-species biotic interactions within the

modelling framework could potentially produce realistic predictions of species distribution

and likely reduce the uncertainties associated with predictions. Although there remains the

challenge of quantifying and collecting data on biotic interactions at regional and global

scales. Despite paucity of complete data there is need to use measures that are indicative of

environmental change (such as corridor connectivity) while quantifying range-shift gaps.

In this chapter, I model the potential range-shift of 84 endangered and critically

endangered plants under future emission scenarios (low and high), predicted estimates of

future deforestation and degradation, as well as with and without corridor connectivity. The

models were implemented using species distributions constructed using a hierarchical

Bayesian framework (See Chapter 6). The aim was to model how future climate and land

use land cover change may influence shifts in future distributions of plant species in

Madagascar. There were three primary objectives for this chapter. First, I compared the

effect of corridor connectivity on future plant range sizes under multiple climate and land-

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species ranges provided climate change does not exceed or mitigate the potential for plants

to disperse (Feeley and Silman 2010a, Dullinger et al. 2012). Second, I determined whether

variation in environmental conditions could lead to upslope displacement of endangered and

critically endangered plant species in lowland, humid and dry forests. I expect more upward

displacements under future high emission scenarios, as well as lowland attrition to be

dominant in all future emission scenarios (Raxworthy et al. 2008, Laurance et al. 2011a).

Third, I investigated whether feedbacks between future climate and land cover change would

impact range sizes and lead to range shift gaps. Lastly, I mapped range shift hotspots to

identify areas where substantial numbers of species range contract under different scenarios.

My expectation is that landscape connectivity will drive species range expansions, while

climate-only scenarios will lead to range contractions (Feeley 2012, Whitfield et al. 2016).

7.2 Methods

Species distribution maps showing probability of occurrence were produced

following the method described in Chapter 6, section 6.2.3. Species-specific probability of

occurrence maps (showing continuous distribution) were transformed into presence/absence

maps using a threshold that maximises the sum of sensitivity (true positive rate) and

specificity (true negative rate) (Liu et al. 2005). Among the plethora of threshold

transformation metrics available, the maximum sum of sensitivity and specificity was

selected because it remains consistent under differing ratios of presence and background

points (Liu et al. 2016).

Species’ range-shift were assessed under multiple future climate and land use land

cover scenarios (Table 6.1). Therefore, range shift analysis was implemented under four

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