Taxonomic scale dependency of Bergmann’s patterns: a cross-scale comparison of hawkmoths and birds along a tropical elevational gradient

Bergmann’s rule predicts a larger body size for endothermic organisms in colder environments. The contrasting results from previous studies may be due to the differences in taxonomic (intraspecific, interspecific and community) and spatial (latitudinal vs elevational) scales. We compared Bergmann’s patterns for endotherms (Aves) and ectotherms (Lepidoptera: Sphingidae) along the same 2.6 km elevational transect in the eastern Himalayas. Using a large data spanning 3,302 hawkmoths (76 morpho-species) and 15,746 birds (245 species), we compared the patterns at the intraspecific (hawkmoths only), interspecific and community scales. Hawkmoths exhibited a positive Bergmann’s pattern at the intraspecific and abundance-weighted community scale. Contrary to this, birds exhibited a strong converse Bergmann’s pattern at interspecific and community scales, both with and without abundance. Overall, our results indicate that incorporation of information on intraspecific variation and/or species relative abundances influences the results to a large extent. The multiplicity of patterns at a single location provides the opportunity to disentangle the relative contribution of individual- and species-level processes by integrating data across multiple nested taxonomic scales for the same taxa. We suggest that future studies of Bergmann’s patterns should explicitly address taxonomic and spatial scale dependency, with species relative abundance and intraspecific trait variation as essential ingredients especially at short elevational scales.

Extending the CWM approach to intraspecific trait variation: how to deal with overly optimistic standard tests?

Community weighted means (CWMs) are widely used to study the relationship between community-level functional traits and environment variation. When relationships between CWM traits and environmental variables are directly assessed using linear regression or ANOVA and tested by standard parametric tests, results are prone to inflated Type I error rates, thus producing overly optimistic results. Previous research has found that this problem can be solved by permutation tests (i.e. the max test). A recent extension of this CWM approach, that allows the inclusion of intraspecific trait variation (ITV) by partitioning information in fixed, site-specific and intraspecific CWMs, has proven popular. However, this raises the question whether the same kind of Type I error rate inflation also exists for site-specific CWM or intraspecific CWM-environment relationships. Using simulated community datasets and a real-world dataset from a subtropical montane cloud forest in Taiwan, we show that site-specific CWM-environment relationships also suffer from Type I error rate inflation, and that the severity of this inflation is negatively related to the relative ITV magnitude. In contrast, for intraspecific CWM-environment relationships, standard parametric tests have the correct Type I error rate, while being somewhat conservative, with reduced statistical power. We introduce an ITV-extended version of the max test for the ITV-extended CWM approach, which can solve the inflation problem for site-specific CWM-environment relationships, and which, without considering ITV, becomes equivalent to the “original” max test used for the CWM approach. On both simulated and real-world data, we show that this new ITV-extended max test works well across the full possible magnitude of ITV. We also provide guidelines and R codes of max test solutions for each CWM type and situation. Finally, we suggest recommendations on how to handle the results of previously published studies using the CWM approach without controlling for Type I error rate inflation.

Individual-based models

Individual-based models (IBMs, also known as agent-based models) are mechanistic models in which demographic population trends emerge from processes at the individual level. IBMs are used instead of more aggregated approaches whenever one or more of the following aspects are deemed too relevant to be ignored: intraspecific trait variation, local interactions, adaptive behaviour, and response to spatially and temporally heterogeneous environments, which often results in nonlinear feedbacks. IBMs offer a high degree of flexibility and therefore vary widely in structure and resolution, depending on the research question, system under investigation, and available data. Data used to parameterise an IBM can be divided into two categories: species and environmental data. Unlike other model types, qualitative empirical knowledge can be taken into account via probabilistic rules. IBM flexibility is often associated with higher number of parameters and hence higher uncertainty; therefore sensitivity analysis and validation are extremely important tools for analysing these models. The chapter presents three examples: a vole–mustelid model used to understand the mechanisms underlying population cycles in rodents; a wild boar–virus model to study persistence of wildlife diseases in heterogeneous landscapes; and a wild tobacco-moth caterpillar model to study emergence of delayed chemical plant defence against insect herbivores. These examples demonstrate the ability of IBMs to decipher mechanisms driving observed phenomena at the population level and their role in planning applied conservation measures. IBMs typically require more data and effort than other model types, but rewards in terms of structural realism, understanding, and decision support are high.