The potential of trait data to increase the availability of bioindicators: A case study using plant conservatism values.

Biological indicators are commonly used to evaluate ecosystem condition. However, their use is often constrained by the availability of information with which to assign species-specific indicator values, which reflect species' responses to the environmental conditions being evaluated by the indicator. As these responses are driven by underlying traits, and trait data for numerous species are available in publicly accessible databases, one possible approach to approximating missing bioindicator values is through traits. We used the Floristic Quality Assessment (FQA) framework and its component indicator of disturbance sensitivity, species-specific ecological conservatism scores (C-scores), as a study system to test the potential of this approach. We tested the consistency of relationships between trait values and expert-assigned C-scores and the trait-based predictability of C-scores across five regions. Furthermore, as a proof-of-concept exercise, we used a multi-trait model to try to reconstruct C-scores, and compared the model predictions to expert-assigned scores. Out of 20 traits tested, there was evidence of regional consistency for germination rate, growth rate, propagation type, dispersal unit, and leaf nitrogen. However, the individual traits showed low predictability (R2 = 0.1-0.2) for C-scores, and a multi-trait model produced substantial classification errors; in many cases, >50% of species were misclassified. The mismatches may largely be explained by the inability to generalize regionally varying C-scores from geographically neutral/naive trait data stored in databases, and the synthetic nature of C-scores. Based on these results, we recommend possible next steps for expanding the availability of species-based bioindication frameworks such as the FQA. These steps include increasing the availability of geographic and environmental data in trait databases, incorporating data about intraspecific trait variability into these databases, conducting hypothesis-driven investigations into trait-indicator relationships, and having regional experts review our results to determine if there are patterns in the species that were correctly or incorrectly classified.

A framework for linking competitor ecological differences to coexistence.

Not all ecological differences among competing species affect their ability to locally coexist. Rather, the differences that promote stable coexistence can be those which cause each species to experience stronger intraspecific than interspecific competition. Recent approaches have established how to detect the demographic signature of these competitive effects, but alone they cannot elucidate the ecological differences among species that yield these patterns. Here, we present a unifying experimental and observational framework that identifies potential ecological differences among species shaping their responses to intra- and interspecific competition. We first describe a conceptual model establishing why the strength of intra- and interspecific competitive interactions should vary along environmental gradients related to species ecological differences. We then show how to apply the framework using Enallagma damselflies, a diverse group of predatory aquatic insects. To determine how species responded to intra- and interspecific competition along environmental gradients, we experimentally manipulated the relative abundances of three species and replicated this across five lakes which varied in environmental conditions affecting larval damselfly per capita growth and mortality rates-key vital rates regulating their populations. Results suggest Enallagma are ecologically differentiated in ways that in some communities can result in intraspecific competition exceeding interspecific competition. However, in many cases the opposite was true, or the effects of intra- and interspecific competition were equivalent via growth and mortality responses. Moreover, these effects tended to be weak and asymmetrical among competitors, which suggests that differential responses of larval growth and mortality to intra- and interspecific competition may not contribute strongly to the maintenance of Enallagma diversity. Different environmental factors appear to shape these demographic responses to competition, providing insight into the ecological mechanisms regulating damselfly assemblages. This framework can be broadly applied to identify the ecological differences among species that may promote coexistence, advancing knowledge of the mechanisms underlying coexistence and overcoming some limitations of purely phenomenological approaches.

Large-scale probabilistic identification of boreal peatlands using Google Earth Engine, open-access satellite data, and machine learning.

Freely-available satellite data streams and the ability to process these data on cloud-computing platforms such as Google Earth Engine have made frequent, large-scale landcover mapping at high resolution a real possibility. In this paper we apply these technologies, along with machine learning, to the mapping of peatlands-a landcover class that is critical for preserving biodiversity, helping to address climate change impacts, and providing ecosystem services, e.g., carbon storage-in the Boreal Forest Natural Region of Alberta, Canada. We outline a data-driven, scientific framework that: compiles large amounts of Earth observation data sets (radar, optical, and LiDAR); examines the extracted variables for suitability in peatland modelling; optimizes model parameterization; and finally, predicts peatland occurrence across a large boreal area (397, 958 km2) of Alberta at 10 m spatial resolution (equalling 3.9 billion pixels across Alberta). The resulting peatland occurrence model shows an accuracy of 87% and a kappa statistic of 0.57 when compared to our validation data set. Differentiating peatlands from mineral wetlands achieved an accuracy of 69% and kappa statistic of 0.37. This data-driven approach is applicable at large geopolitical scales (e.g., provincial, national) for wetland and landcover inventories that support long-term, responsible resource management.

Monitoring butterfly abundance: beyond Pollard walks.

Most butterfly monitoring protocols rely on counts along transects (Pollard walks) to generate species abundance indices and track population trends. It is still too often ignored that a population count results from two processes: the biological process (true abundance) and the statistical process (our ability to properly quantify abundance). Because individual detectability tends to vary in space (e.g., among sites) and time (e.g., among years), it remains unclear whether index counts truly reflect population sizes and trends. This study compares capture-mark-recapture (absolute abundance) and count-index (relative abundance) monitoring methods in three species (Maculinea nausithous and Iolana iolas: Lycaenidae; Minois dryas: Satyridae) in contrasted habitat types. We demonstrate that intraspecific variability in individual detectability under standard monitoring conditions is probably the rule rather than the exception, which questions the reliability of count-based indices to estimate and compare specific population abundance. Our results suggest that the accuracy of count-based methods depends heavily on the ecology and behavior of the target species, as well as on the type of habitat in which surveys take place. Monitoring programs designed to assess the abundance and trends in butterfly populations should incorporate a measure of detectability. We discuss the relative advantages and inconveniences of current monitoring methods and analytical approaches with respect to the characteristics of the species under scrutiny and resources availability.

Sociality, density-dependence and microclimates determine the persistence of populations suffering from a novel fungal disease, white-nose syndrome.

Disease has caused striking declines in wildlife and threatens numerous species with extinction. Theory suggests that the ecology and density-dependence of transmission dynamics can determine the probability of disease-caused extinction, but few empirical studies have simultaneously examined multiple factors influencing disease impact. We show, in hibernating bats infected with Geomyces destructans, that impacts of disease on solitary species were lower in smaller populations, whereas in socially gregarious species declines were equally severe in populations spanning four orders of magnitude. However, as these gregarious species declined, we observed decreases in social group size that reduced the likelihood of extinction. In addition, disease impacts in these species increased with humidity and temperature such that the coldest and driest roosts provided initial refuge from disease. These results expand our theoretical framework and provide an empirical basis for determining which host species are likely to be driven extinct while management action is still possible.