No abstract

Local and Indigenous Peoples play critical roles in safeguarding global biological and cultural diversity. However, species distribution modelling has yet to incorporate perspectives that assess threats to the linked biological and cultural systems of local and Indigenous Peoples. Here, we provide the first example of integrating species distribution modelling with benefit‐relevant indicators. This novel approach assesses how human access to culturally important species may change over time. Focusing on two culturally significant species used by the Indigenous Māori people of New Zealand, we first identified predictor variables relevant to the habitat of each species. We used species distribution models (SDMs) to estimate the recent (1961–1990) potential distribution for each species based on occurrence records and predictor variables, then generated future climate suitability maps. Our models show that future suitability for one species shifts to the south, in line with changes in temperature and precipitation, while the second species range expands into higher latitudes, driven primarily by increased temperature. When we combined these models with knowledge of tribal boundaries and cultural practices, results indicated that these distributions might decrease access to culturally important plants. Future suitability for one species shifted substantially from where it is most valued for weaving, while the second species range expanded to include more of its primary medicinal users. Climate change‐mediated shifts in the ranges of these species are likely to affect intergenerational human–environment relationships, sense of place, cultural identity and knowledge on a regional scale, as well as cultural identity and social cohesion on a national scale. By interpreting SDMs within a socioecological framework, this research illustrates a new approach to assessment of vulnerabilities to climate change and identifies strategies for adaptation. A plain language summary is available for this article.

Ethnobiology as a discipline has evolved increasingly to embrace theory-inspired and hypothesis-driven approaches to study why and how local people choose plants and animals they interact with and use for their livelihood. However, testing complex hypotheses or a network of ethnobiological hypotheses is challenging, particularly for data sets with nonindependent observations due to species phylogenetic relatedness or socio-relational links between participants. Further, to account fully for the dynamics of local ecological knowledge, it is important to include the spatially explicit distribution of knowledge, changes in knowledge, and knowledge transmission and use. To promote the use of advanced statistical modelling approaches that address these limitations, we synthesize methodological advances for hypothesis-driven research in ethnobiology while highlighting the need for more figures than tables and more tables than text in ethnobiological literature. We present the ethnobiological motivations for conducting generalized linear mixed-effect modelling, structural equation modelling, phylogenetic generalized least squares, social network analysis, species distribution modelling, and predictive modelling. For each element of the proposed ethnobiologists quantitative toolbox, we present practical applications along with scripts for a widespread implementation. Because these statistical modelling approaches are rarely taught in most ethnobiological programs but are essential for careers in academia or industry, it is critical to promote workshops and short courses focused on these advanced methods. By embracing these quantitative modelling techniques without sacrificing qualitative approaches which provide essential context, ethnobiology will progress further towards an expansive interaction with other disciplines.

Human subsistence societies have thrived in environmental extremes while maintaining biodiversity through social learning of ecological knowledge, such as techniques to prepare food and medicine from local resources. However, there is limited understanding of which processes shape social learning patterns and configuration in ecological knowledge networks, or how these processes apply to resource management and biological conservation. In this study, we test the hypothesis that the prestige (rarity or exclusivity) of knowledge shapes social learning networks. In addition, we test whether people tend to select who to learn from based on prestige (knowledge or reputation), and homophily (e.g., people of the same age or gender). We used interviews to assess five types of medicinal plant knowledge and how 303 people share this knowledge across four villages in Solomon Islands. We developed exponential random graph models (ERGMs) to test whether hypothesized patterns of knowledge sharing based on prestige and homophily are more common in the observed network than in randomly simulated networks of the same size. We found that prestige predicts five hypothesized network configurations and all three hypothesized learning patterns, while homophily predicts one of three hypothesized network configurations and five of the seven hypothesized learning patterns. These results compare the strength of different prestige and homophily effects on social learning and show how cultural practices such as intermarriage can affect certain aspects of prestige and homophily. By advancing our understanding of how prestige and homophily affect ecological knowledge networks, we identify which social learning patterns have the largest effects on biocultural conservation of ecological knowledge.

Phenotypic diversity among individuals defines the potential for evolutionary selection in a species. Phytophthora infestans epidemics are generally thought to be favored by moderate to low temperatures, but temperatures in many locations worldwide are expected to rise as a result of global climate change. Thus, we investigated variation among individuals of P. infestans for relative growth at different temperatures. Isolates of P. infestans came from three collections: (i) individual genotypes recently dominant in the United States, (ii) recently collected individuals from Central Mexico, and (iii) progeny of a recent sexual recombination event in the northeastern United States. In general, these isolates had optimal mycelial growth rates at 15 or 20°C. However, two individuals from Central Mexico grew better at higher temperatures than did most others and two individuals grew relatively less at higher temperatures than did most others. The isolates were also assessed for mefenoxam sensitivity and mating type. Each collection contained individuals of diverse sensitivities to mefenoxam and individuals of the A1 and A2 mating type. We then searched for genomic regions associated with phenotypic diversity using genotyping-by-sequencing. We found one single nucleotide polymorphism (SNP) associated with variability in mycelial growth at 20°C, two associated with variability in mycelial growth at 25°C, two associated with sensitivity to mefenoxam, and one associated with mating type. Interestingly, the SNPs associated with mefenoxam sensitivity were found in a gene-sparse region, whereas the SNPs associated with growth at the two temperatures and mating type were found both at more gene-dense regions.