The relationship between climate change and the endangered rainforest shrub Triunia robusta (Proteaceae) endemic to southeast Queensland, Australia

Abstract

Threatened species in rainforests may be vulnerable to climate change, because of their potentially narrow thermal tolerances, small population sizes and restricted distributions. This study modelled climate induced changes on the habitat distribution of the endangered rainforest plant Triunia robusta, endemic to southeast Queensland, Australia. Species distribution models were developed for eastern Australia at 250 m grids and southeast Queensland at 25 m grids using ground-truthed presence records and environmental predictor data. The species’ habitat distribution under the current climate was modelled, and the future potential habitat distributions were projected for the epochs 2030, 2050 and 2070. The eastern Australia model identified several spatially disjunct, broad habitat areas of coastal eastern Australia consistent with the current distribution of rainforests, and projected a southward and upslope contraction driven mainly by average temperatures exceeding current range limits. The southeast Queensland models suggest a dramatic upslope contraction toward locations where the majority of known populations are found. Populations located in the Sunshine Coast hinterland, consistent with past rainforest refugia, are likely to persist long-term. Upgrading the level of protection for less formal nature reserves containing viable populations is a high priority to better protect refugial T. robusta populations with respect to climate change.

Phylogenetic diversity of two geographically overlapping lichens: isolation by distance, environment, or fragmentation?

“…Also, lineages adapted to more illuminated conditions and tolerating disturbances may be less susceptible to IBF than those restricted to well‐preserved, closed vegetation. Thus, autecology will variously affect IBF and IBD and lead to different population structures, even if other factors such as lineage age and geographic range are comparable (Cannon et al., 2009; Malhi et al., 2013; Morley, 2011; Shimizu‐Kimura et al., 2017).…”

Clade-age-dependent diversification under high species turnover shapes species richness disparities among tropical rainforest lineages of Bulbophyllum (Orchidaceae)

“…Together, these four TRF regions play an invaluable role in sustaining high levels of global biodiversity [8] while being increasingly threatened by various types of human disturbance and climate change [9]. Hence, explaining the evolutionary and ecological causes of TRF richness patterns between the highly diverse and disjunct regions of the Neotropics, Africa, Madagascar and the Asia-Pacific region is particularly important for the understanding of modern biodiversity and its conservation.…”

A multidisciplinary approach to inform assisted migration of the restricted rainforest tree, Fontainea rostrata

“…rostrata is a highly endemic species confined to small, fragmented habitat remnants within the modelled region, and despite extensive field surveys to bolster existing historical occurrence records, the species only occurs in relatively few locations, even within suitable habitat. Thus, as with other studies that utilise a relatively low number of occurrence records by necessity [63–67], MaxEnt was a suitable choice to identify trends and inform conservation management decisions for the species.…”

“…rostrata under current and future environmental conditions. MaxEnt is a software program for modelling species distributions using presence-only data on the principle of a maximum entropy algorithm [62] Whilst there are other methods available for modelling species distributions such as generalised linear models (GLMs), generalised additive models (GAMs), mechanical models and ensemble techniques, we used MaxEnt because it has been widely applied and used by government agencies and research institutions for modelling plant distributions under both current and future environments [63–67] and has been shown to perform well in comparison to other models where relatively few presence records are available [68]. To identify the most informative contributing subsets of variables, Spearman’s rank correlation [69] and MaxEnt jack-knife tests were conducted to identify significantly correlated pairs of variables ( r > 0.80), whereupon the variables that made the least contribution to model performance were omitted.…”