William K. Morris

Rate of tree carbon accumulation increases continuously with tree size

Forests are major components of the global carbon cycle, providing substantial feedback to atmospheric greenhouse gas concentrations. Our ability to understand and predict changes in the forest carbon cycle—particularly net primary productivity and carbon storage—increasingly relies on models that represent biological processes across several scales of biological organization, from tree leaves to forest stands. Yet, despite advances in our understanding of productivity at the scales of leaves and stands, no consensus exists about the nature of productivity at the scale of the individual tree, in part because we lack a broad empirical assessment of whether rates of absolute tree mass growth (and thus carbon accumulation) decrease, remain constant, or increase as trees increase in size and age. Here we present a global analysis of 403 tropical and temperate tree species, showing that for most species mass growth rate increases continuously with tree size. Thus, large, old trees do not act simply as senescent carbon reservoirs but actively fix large amounts of carbon compared to smaller trees; at the extreme, a single big tree can add the same amount of carbon to the forest within a year as is contained in an entire mid-sized tree. The apparent paradoxes of individual tree growth increasing with tree size despite declining leaf-level and stand-level productivity can be explained, respectively, by increases in a tree’s total leaf area that outpace declines in productivity per unit of leaf area and, among other factors, age-related reductions in population density. Our results resolve conflicting assumptions about the nature of tree growth, inform efforts to undertand and model forest carbon dynamics, and have additional implications for theories of resource allocation and plant senescence.

Understanding co‐occurrence by modelling species simultaneously with a Joint Species Distribution Model (JSDM)

Summary A primary goal of ecology is to understand the fundamental processes underlying the geographic distributions of species. Two major strands of ecology – habitat modelling and community ecology – approach this problem differently. Habitat modellers often use species distribution models (SDMs) to quantify the relationship between species’ and their environments without considering potential biotic interactions. Community ecologists, on the other hand, tend to focus on biotic interactions and, in observational studies, use co-occurrence patterns to identify ecological processes. Here, we describe a joint species distribution model (JSDM) that integrates these distinct observational approaches by incorporating species co-occurrence data into a SDM. JSDMs estimate distributions of multiple species simultaneously and allow decomposition of species co-occurrence patterns into components describing shared environmental responses and residual patterns of co-occurrence. We provide a general description of the model, a tutorial and code for fitting the model in R. We demonstrate this modelling approach using two case studies: frogs and eucalypt trees in Victoria, Australia. Overall, shared environmental correlations were stronger than residual correlations for both frogs and eucalypts, but there were cases of strong residual correlation. Frog species generally had positive residual correlations, possibly due to the fact these species occurred in similar habitats that were not fully described by the environmental variables included in the JSDM. Eucalypt species that interbreed had similar environmental responses but had negative residual co-occurrence. One explanation is that interbreeding species may not form stable assemblages despite having similar environmental affinities. Environmental and residual correlations estimated from JSDMs can help indicate whether co-occurrence is driven by shared environmental responses or other ecological or evolutionary process (e.g. biotic interactions), or if important predictor variables are missing. JSDMs take into account the fact that distributions of species might be related to each other and thus overcome a major limitation of modelling species distributions independently.

Dental fluorosis and skeletal fluoride content as biomarkers of excess fluoride exposure in marsupials.

Particulate and gaseous fluoride emissions contaminate vegetation near fluoride-emitting industries, potentially impacting herbivorous wildlife in neighboring areas. Dental fluorosis has been associated with consumption of fluoride-contaminated foliage by juvenile livestock and wildlife in Europe and North America. For the first time, we explored the epidemiology and comparative pathology of dental fluorosis in Australian marsupials residing near an aluminium smelter. Six species (Macropus giganteus, Macropus rufogriseus, Wallabia bicolor, Phascolarctos cinereus, Trichosurus vulpecula, Pseudocheirus peregrinus) demonstrated significantly higher bone fluoride levels in the high (n=161 individuals), compared to the low (n=67 individuals), fluoride areas (p<0.001). Necropsy examinations of all six species from the high-fluoride area near the smelter revealed dental lesions considered characteristic of dental fluorosis in eutherian mammals. Within the high-fluoride area, 67% of individuals across the six species showed dental enamel lesions, compared to 3% in the low-fluoride areas. Molars that erupted before weaning were significantly less likely to display pathological lesions than those developing later, and molars in the posterior portion of the dental arcade were more severely fluorotic than anterior molars in all six species. The severity of dental lesions was positively associated with increasing bone fluoride levels in all species, revealing a potential biomarker of excess fluoride exposure.

Identifying species at coextinction risk when detection is imperfect: Model evaluation and case study

Losing a species from a community can cause further extinctions, a process also known as coextinction. The risk of being extirpated with an interaction partner is commonly inferred from a species’ host-breadth, derived from observing interactions between species. But observational data suffers from imperfect detection, making coextinction estimates highly unreliable. To address this issue and to account for data uncertainty, we fit a hierarchical N-mixture model to individual-level interaction data from a mutualistic network. We predict (1) with how many interaction partners each species interacts (to indicate their coextinction risk) and (2) how completely the community was sampled. We fit the model to simulated interactions to investigate how variation in sampling effort, interaction probability, and animal abundances influence model accuracy and apply it to an empirical dataset of flowering plants and their insect visitors. The model performed well in predicting the number of interaction partners for scenarios with high abundances, but indicated high parameter uncertainty for networks with many rare species. Yet, model predictions were generally closer to the true value than the observations. Our mutualistic plant-insect community most closely resembled the scenario of high interaction rates with low abundances. Median estimates of interaction partners were frequently much higher than the empirical data indicate, but uncertainty was high. Our analysis suggested that we only detected 14-59% of the flower-visiting insect species, indicating that our study design, which is common for pollinator studies, was inadequate to detect many species. Imperfect detection strongly affects the inferences from observed interaction networks and hence, host specificity, specialisation estimates and network metrics from observational data may be highly misleading for assessing a species’ coextinction risks. Our study shows how models can help to estimate coextinction risk, but also indicates the need for better data (i.e., intensified sampling and individual-level observations) to reduce uncertainty.

Combining functional traits, the environment, and multiple surveys to understand semi-arid tree distributions

QUESTIONS: Relationships between species, their functional traits and environmental gradients can now be more fully understood with trait‐based multi‐species distribution models (trait‐SDMs). However, general patterns are yet to emerge from founding studies using these models, which are mostly case studies at a single scale. Here, we address the generality of trait–environment relations by asking whether these relationships hold for different sampling schemes, environmental variables and species sets. METHODS: We focus on the drought and fire‐resistant “mallee” eucalypts of a semi‐arid region of southeast Australia, which are likely to face new climates and disturbance regimes under global change. We use hierarchical regression modelling to test how trait–environment relationships change for two data sets representing an extensively collected, multipurpose data set and an intensively collected data set stratified along environmental gradients. RESULTS: Three functional traits (specific leaf area, maximum height and seed mass) explained a substantial portion of the occurrence of species along soil, water and climatic gradients, with the relationship between seed mass and soil type robust across all tests. Other trait–environment relationships changed depending on study design and species set, with soil and substrate variables more important relative to climate (precipitation) for the intensively sampled survey. Remotely sensed variables were good surrogates for some field‐based measures (soil type), but not others (land form: dune or swale). In particular, airborne soil radiometric data show promise as a spatially continuous substitute for soil texture. CONCLUSIONS: Trait‐SDMs are a powerful tool for quantifying ecological interactions, but generalizations will only be possible when sample design, scale and environmental variables are carefully considered. We show that important ecological relationships can be diluted or missed entirely in broad scale trait–environment studies that rely on remotely sensed climate variables alone. Relationships that are robust to differences in study design, growth form and ecosystem (e.g., heavier seeds on sandy soil) are the most likely to reveal general ecological processes.