Lineage‐level distribution models lead to more realistic climate change predictions for a threatened crayfish

Abstract

Aim As climate change presents a major threat to biodiversity in the next decades, it is critical to assess its impact on species habitat suitability to inform biodiversity conservation. Species distribution models (SDMs) are a widely used tool to assess climate change impacts on species’ geographical distributions. As the name of these models suggests, the species level is the most commonly used taxonomic unit in SDMs. However, recently it has been demonstrated that SDMs considering taxonomic resolution below (or above) the species level can make more reliable predictions of biodiversity change when different populations exhibit local adaptation. Here, we tested this idea using the Japanese crayfish (Cambaroides japonicus), a threatened species encompassing two geographically structured and phylogenetically distinct genetic lineages. Location Northern Japan. Methods We first estimated niche differentiation between the two lineages of C. japonicus using n‐dimensional hypervolumes and then made climate change predictions of habitat suitability using SDMs constructed at two phylogenetic levels: species and intraspecific lineage. Results Our results showed only intermediate niche overlap, demonstrating measurable niche differences between the two lineages. The species‐level SDM made future predictions that predicted much broader and severe impacts of climate change. However, the lineage‐level SDMs led to reduced climate change impacts overall and also suggested that the eastern lineage may be more resilient to climate change than the western one. Main conclusions The two lineages of C. japonicus occupy different niche spaces. Compared with lineage‐level models, species‐level models can overestimate climate change impacts. These results not only have important implications for designing future conservation strategies for this threatened species, but also highlight the need for incorporating genetic information into SDMs to obtain realistic predictions of biodiversity change.

Neither historical climate nor contemporary range fully explain the extant patterns of molecular diversity in marine species

“…However, species‐level SDMs over‐predicted the range of the east coast lineages and under‐predicted the western lineages for each species. This finding mirrors other studies which suggest that species‐level SDMs can both over‐ and under‐predict the ranges of individual lineages; thus, a hierarchical approach should be used to evaluate future or past range shifts (D’Amen et al, 2013; Zhang et al, 2021). In addition, it is important to note that the SDMs used here did not account for past changes in oceanographic dynamics such as current and upwelling systems.…”

“…Evidence of east and west differentiation was further corroborated by recent genomic work on these species (Nielsen, Henriques, et al, 2020). SDMs including intraspecific population differentiation, by modelling separate lineages, can potentially portray more accurate habitat suitability outputs (Maia‐Carvalho et al, 2018; Zhang et al, 2021). Yet, it has also been argued that the smaller number of observations at the lineage level may lead to issues in model calibration and/or evaluation (Maguire et al, 2018).…”

Exploring ecological specialization in pipefish using genomic, morphometric and ecological evidence

“…Predictions of single algorithms were ensembled using a committee averaging strategy (Thuiller et al, 2020; Thuiller et al., 2019). The continuous habitat suitability results (ranging from 0 to 1) were transformed into binary suitable/unsuitable maps by using the 10th percentile presence probability threshold (Zhang et al., 2021).…”

Intraspecific genetic variation matters when predicting seagrass distribution under climate change

“…SDMs constructed with data for lineages below the species level can account for possible local adaptations and therefore can provide more reliable niche estimations and habitat suitability projections for species with high intraspecific variation. For instance, a species‐level SDM for the threatened Japanese crayfish Cambaroides japonicus (De Haan 1841) predicted that this species might lose a large proportion of its suitable habitat in the future, whereas lineage‐level SDMs for the same species predicted a weaker impact of climate change overall (Zhang et al, 2021). The importance of taxonomic units (i.e., above and below the species level) in distribution modelling has recently been recognized (Benito Garzón et al, 2019; Collart et al, 2021; Peterson et al, 2019; Smith et al, 2019), which has resulted in more SDM applications for terrestrial and freshwater species that consider intraspecific variation (Ikeda et al, 2017; Razgour et al, 2019; Zhang et al, 2021).…”