Biology subjects

Mahler, D. L.

Publications and source records attributed to Mahler, D. L..

5 recordsLinked to original sources

bioRxiv2025

Dude, Everyone Wants Pattern Analysis Tools (DEWPAT): Tools for measuring visual pattern diversity from digital images

O_LIExploring the diversity and function of complex colour patterns is a fundamental interest in ecology and evolutionary biology, but progress on many questions is limited by our ability to quantify diverse visual patterns. We address this problem by introducing Dude, Everyone Wants Colour Pattern Analysis Tools (DEWPAT), a Python package for characterizing multidimensional pattern complexity. C_LIO_LIDEWPAT is a flexible framework designed to extract a diversity of components of visual pattern complexity from standard RGB and multispectral images, including entropy (information content), average gradient magnitude (edge content), high frequency content (detail granularity), generalized variance (heterogeneity), and patch dissimilarity. DEWPAT offers optional image transformation functionality, including blurring to model receiver acuity and segmentation to reduce noise. C_LIO_LIFunctions in this package return both quantitative measurements and graphical representations of color and pattern diversity. We demonstrate DEWPATs key functions and applications with three empirical examples (longhorn beetles, anole lizards, and flowers). C_LIO_LIDEWPAT has the potential to quantitatively characterize previously intractable pattern phenotypes in ways that make their features available for biological analysis. C_LI

evolutionary biology↗

bioRxiv2021

Testing for heterogeneous rates of discrete character evolution on phylogenies

Many hypotheses in the field of phylogenetic comparative biology involve specific changes in the rate or process of trait evolution. We present a method designed to test whether the rate of evolution of a discrete character has changed in one or more clades, lineages, or time periods. This method differs from other related approaches (such as the covarion model) in that the regimes in which the rate or process is postulated to have changed are specified a priori by the user, rather than inferred from the data. Similarly, it differs from methods designed to model a correlation between two binary traits in that the regimes mapped onto the tree are fixed. We apply our method to investigate the rate of dewlap color and/or caudal vertebra number evolution in Caribbean and mainland clades of the diverse lizard genus Anolis. We find little evidence to support any difference between mainland and island evolution in either character. We also examine the statistical properties of the method more generally and show that it has acceptable type I error, parameter estimation, and power. Finally, we discuss the relationship of our method to existing models of heterogeneity in the rate of discrete character evolution on phylogenies.

evolutionary biology↗

bioRxiv2021

A simple hierarchical model for heterogeneity in the evolutionary correlation on a phylogenetic tree

Numerous questions in phylogenetic comparative biology revolve around the correlated evolution of two or more phenotypic traits on a phylogeny. In many cases, it may be sufficient to assume a constant value for the evolutionary correlation between characters across all the clades and branches of the tree. Under other circumstances, however, it is desirable or necessary to account for the possibility that the evolutionary correlation differs through time or in different sections of the phylogeny. Here, we present a method designed to fit a hierarchical series of models for heterogeneity in the evolutionary rates and correlation of two quantitative traits on a phylogenetic tree. We apply the method to two datasets: one for different attributes of the buccal morphology in sunfishes (Centrarchidae); and a second for overall body length and relative body depth in rock- and non-rock-dwelling South American iguanian lizards. We also examine the performance of the method for parameter estimation and model selection using a small set of numerical simulations.

evolutionary biology↗

bioRxiv2020

A unified model of species abundance, genetic diversity, and functional diversity revealsthe mechanisms structuring ecological communities

Biodiversity accumulates hierarchically by means of ecological and evolutionary processes and feedbacks. Reconciling the relative importance of these processes is hindered by current theory, which tends to focus on a single spatial, temporal or taxonomic scale. We introduce a mechanistic model of community assembly, rooted in classic island biogeography theory, which makes temporally explicit joint predictions across three biodiversity data axes: i) species richness and abundances; ii) population genetic diversities; and iii) trait variation in a phylogenetic context. We demonstrate that each data axis captures information at different timescales, and that integrating these axes enables discriminating among previously unidentifiable community assembly models. We combine our massive eco-evolutionary synthesis simulations (MESS) with supervised machine learning to fit the parameters of the model to real data and infer processes underlying how biodiversity accumulates, using communities of tropical trees, arthropods, and gastropods as case studies that span a range of spatial scales.

evolutionary biology↗

bioRxiv2017

Integrating Over Uncertainty In Spatial Scale Of Response Within Multispecies Occupancy Models Yields More Accurate Assessments Of Community Composition

O_LISpecies abundance and community composition are affected not only by the local environment, but also by broader landscape and regional context. Yet determining the spatial scale at which landscapes affect species remains a persistent challenge that hinders ecologists abilities to understand how environmental gradients influence species presence and shape entire communities, especially in the face of data deficient species and imperfect species detection.\nC_LIO_LIHere we present a Bayesian framework that allows uncertainty surrounding the true spatial scale of species responses (i.e., changes in presence/absence) to be integrated directly into a community hierarchical model.\nC_LIO_LIThis scale selecting multi-species occupancy model (ssMSOM) estimates the scale of response, and shows high accuracy and correct type I error rates across a broad range of simulation conditions. In contrast, ensembles of single species GLMs frequently fail to detect the correct spatial scale of response, and are often falsely confident in favoring the incorrect spatial scale, especially as species detection probabilities deviate from perfect.\nC_LIO_LIIntegrating spatial scale selection directly into hierarchical community models provides a means of formally testing hypotheses regarding spatial scales of response, and more accurately determining the environmental drivers that shape communities.\nC_LI

ecology↗