Impact of end‐of‐century climate change on priority non‐timber forest product species across tropical Africa

Abstract

Plant species providing Non-Timber Forest Products (NTFP) are economically important across Africa. How this heterogeneous and understudied resource will respond to ongoing climate change remains understudied. Here, we modelled the impact of endof-the-century climate change on the distribution of 40 NTFP plant species distributed across tropical Africa. Occurrence data were extracted from a taxonomically verified database and three different ecological niche modelling algorithms were used. Species distributions were modelled under two end-of-century (2085) climate change models (RCP4.5 and RCP8.5) and two dispersal scenarios (limited and expanded). We show that for the 40 NTFP plant species studied here, different responses are modelled with some species gaining in suitable habitats (47.5%-65% under RCP4.5), whereas others will lose in suitable habitats (35%-52.5% under RCP4.5). Nevertheless, we also show that our results vary between the different methods used, such as modelling algorithms, dispersal scenarios and general circulation models. Overall, our results suggest that the response of NTFP species to climate change depends on their distribution, ecology and dispersal ability.

Mopane (Colophospermum mopane): A potential winner under climate change in southern Africa

“…Several studies have demonstrated the vulnerability of sub‐Sahara African tree species to climate change (De Cauwer et al, 2014; Blach‐Overgaard et al, 2015; Stévart et al, 2019; Dimobe et al, 2020; Jinga et al, 2020; Amoussou et al, 2022; Balima et al, 2022; Lyam et al, 2022; Ngarega et al, 2021). Mitigation measures have been proposed for the species imperilled by climate change, including establishment in protected areas that will remain climatically stable (Esser & Weldemariam, 2023; Warren et al, 2018) and introductions in future suitable habitats (Pimm et al, 2018).…”

“…Several methods exist for selecting environmental variables to include in SDMs, each with advantages and disadvantages. Variables can simply be selected according to expert knowledge on how logically relevant they are to the species under study (Amoussou et al, 2022; Blach‐Overgaard et al, 2015). All variables can be used simultaneously and SDM algorithms are allowed to select the most important ones through variable permutation (Braunisch et al, 2013).…”

“…The vulnerability of African tree species to climate change has been revealed in several SDM studies (Amoussou et al, 2022; Blach‐Overgaard et al, 2015; De Cauwer et al, 2014; Esser & Weldemariam, 2023; Jinga et al, 2020; Stévart et al, 2019). The natural range of 20 African NTFP plant species contracted when forecasted to 2085 under representative concentration pathway (RCP) 4.5 and RCP8.5 (Amoussou et al, 2022). Range contraction has also been shown among several tree species, including wild teak ( Pterocarpus angolensis ) (De Cauwer et al, 2014), African palms (Blach‐Overgaard et al, 2015), the shea tree ( Vitellaria paradoxa ) (Dimobe et al, 2020), African wild loquat ( Uapaca kirkiana ) (Jinga et al, 2020), gum Arabic ( Senegalia senegal ) (Lyam et al, 2022) and species in genus Boswellia (Esser & Weldemariam, 2023).…”

“…The vulnerability of African tree species to climate change has been revealed in several SDM studies (Amoussou et al, 2022; Blach‐Overgaard et al, 2015; De Cauwer et al, 2014; Esser & Weldemariam, 2023; Jinga et al, 2020; Stévart et al, 2019). The natural range of 20 African NTFP plant species contracted when forecasted to 2085 under representative concentration pathway (RCP) 4.5 and RCP8.5 (Amoussou et al, 2022).…”

Variable intraspecific response to climate change in a medicinally important African tree species, Vachellia sieberiana (DC.) (paperbark thorn)

“…Several methods are available to determine environmental variables to use in SDMs, each with advantages and disadvantages. Variables can be selected according to expert ecological knowledge on how relevant they are to the focal species (Amoussou et al., 2022 ; Blach‐Overgaard et al., 2015 ). Variables can be selected by permutation tests implemented in SDM algorithms, where those with high variable importance scores are selected (Braunisch et al., 2013 ).…”