Biology subjects

Lee, W. G.

Publications and source records attributed to Lee, W. G..

3 recordsLinked to original sources

bioRxiv2021

Predictions of biodiversity are improved by integrating trait-based competition with abiotic filtering

All organisms must simultaneously tolerate the environment and access limiting resources if they are to persist. Otherwise they go extinct. Approaches to understanding environmental tolerance and resource competition have generally been developed independently. Consequently, integrating the factors that determine abiotic tolerance with those that affect competitive interactions to model species abundances and community structure remains an unresolved challenge. This is likely the reason why current models of community assembly do not accurately predict species abundances and dynamics. Here, we introduce a new synthetic framework that models both abiotic tolerance and biotic competition by using functional traits, which are phenotypic attributes that influence organism fitness. First, our framework estimates species carrying capacities that vary along abiotic gradients based on whether the phenotype tolerates the local environment. Second, it estimates pairwise competitive interactions as a function of multidimensional trait differences between species and determines which trait combinations produce the most competitive phenotypes. We demonstrate that our combined approach more than doubles the explained variance of species covers in a wetland community compared to the model of abiotic tolerances alone. Trait-based integration of competitive interactions and abiotic filtering improves our ability to predict species abundances across space, bringing us closer to more accurate predictions of biodiversity structure in a changing world.

ecology↗

bioRxiv2019

Can we predict which species win when new habitat becomes available?

Land cover change is a key component of anthropogenic global environmental change, contributing to changes in environmental conditions of habitats. These changes can lead to the redistribution of species and shifts in the functional composition and properties of ecosystems. Deforestation is globally the most widespread anthropogenically driven land cover change leading to conversion from closed forest to open non-forest habitat. The consequences of these functional habitat changes on species distributions are only poorly understood. This study investigates the relative roles of geographic features, species climatic niche characteristics and species traits in determining the ability of open-habitat plant species to take advantage of recently opened habitats. We use current occurrence records of 18 herbaceous, predominantly open-habitat species of the genus Acaena (Rosaceae) to determine their prevalence in recently opened habitat. Geographic features of the spatial distribution of open habitat, species climatic niche characteristics, and species traits related to dispersal were tested their correlation with species prevalence in anthropogenically opened habitat. While primary open habitat (naturally open) was characterised by cold climates, secondary open habitat (naturally closed but anthropogenically opened) is characterised by warmer and wetter conditions. We found high levels of variation in the prevalence of secondary open habitat among the investigated species indicating differences between species in their ability to colonise newly opened habitat. For the species investigated, geographical and climatic niche factors showed generally stronger relationships with species prevalence in secondary open habitat than functional traits did. For small herbaceous species, geographical and environmental factors appear to be more important than species functional traits for facilitating expansion into secondary open habitats. Our results suggested that the land cover change might have triggered the shifts of factors controlling open-habitat plant distributions from the competition with forest trees to current environmental constraints.

ecology↗

bioRxiv2019

Assemblage of Focal Species Recognizers - AFSR: A technique for decreasing false positive rates of acoustic automatic identification in a multiple species context

Passive acoustic monitoring (PAM) coupled with automated species identification is a promising tool for species monitoring and conservation worldwide. However, high false positive rates are still an important limitation and a crucial factor for acceptance of these techniques in wildlife surveys. Here we present the Assemblage of Focal Species Recognizers - AFSR, a novel approach for decreasing false positives and increasing models precision in multispecies contexts. AFSR focusses on decreasing false positives by excluding unreliable sound file segments that are prone to misidentification. We used MatlabHTK, a hidden Markov models interface for bioacoustics analyses, for illustrating AFSR technique by comparing two approaches, 1) a multispecies recognizer where all species are identified simultaneously, and 2) an assemblage of focal species recognizers (AFSR), where several recognizers that each prioritise a single focal species are then summarised into a single output, according to a set of rules designed to exclude unreliable segments. Both approaches (the multispecies recognizer and AFSR) used the same sound files training dataset, but different processing workflow. We applied these recognisers to PAM recordings from a remote island colony with five seabird species and compared their outputs with manual species identifications. False positive rates and precision improved for all the five species when using AFSR, achieving remarkable 0% false positives and 100% precision for three of five seabird species, and < 6% false positive rates, and >90% precision for the other two species. AFSR output was also used to generate daily calling activity patterns for each species. Instead of attempting to withdraw useful information from every fragment in a sound recording, AFSR prioritises more trustworthy information from sections with better quality data. AFSR can be applied to automated species identification from multispecies PAM recordings worldwide.

ecology↗