Biology subjects

Ponciano, J. M.

Publications and source records attributed to Ponciano, J. M..

11 recordsLinked to original sources

bioRxiv2025

MONITORING POPULATION EXTINCTION RISK WITH COMMUNITY SCIENCE DATA

AbstractThe robust estimation of local extinction risk is central to inform management and conservation efforts. Still, estimating this key demographic parameter requires standardized monitoring data that are lacking for most species and systems. The analysis of community science data is emerging as a promising alternative. These expansive datasets leverage observations from multiple volunteers that provide higher temporal and spatial resolution. Nevertheless, the proper analysis of community science data is challenging because it requires accounting for additional complexities in the intrinsic ecological and observational processes. To address this issue, we describe and test a quantitative approach that fits continuous state-space models iteratively to eBird data with the ultimate goal of estimating local persistence probability through time. We evaluated model accuracy by comparing estimates and trends from eBird with those from the endangered Everglades snail kite long-term, standardized monitoring project. We also performed two separate sensitivity analyses (temporal and sampling thinning) to assess how robust the persistence estimates are to a reduction in the number of eBird observations available. Our results showed that the temporal trend trajectory of local population persistence estimated from eBird closely matched that from standardized monitoring. Moreover, the trend remained similar even when reducing the amount of eBird data available to 5% of the original data set - a reduction from 258 to 13 weeks or from 7,714 to 385 lists of observations across 5 years of monitoring. Synthesis and applicationsOur modeling framework provides a robust, computationally efficient, and easy-to-apply tool for monitoring local persistence probability that can support global conservation efforts. This will complement the monitoring of species population viability in places where standardized monitoring is still lacking, but community science observations are common.

ecology↗

bioRxiv2025

Estimating extreme displacements under a Lomax generalized linear model framework: the role of sample size and threshold choice

Understanding ecological variation is fundamental to predicting the dynamics of natural systems, and it becomes increasingly more complex when rare or extreme events are involved. Extreme value statistics are a useful but underutilized tool in ecological analysis, and in this paper we showcase a simulation based study that uses extreme value distributions to estimate rare events. Focused on long-distance dispersal events, we simulate individual variation in seed dispersal events within heterogeneous populations by using finite mixture models. We employ three different approaches to model fitting extreme value distributions, and we particularly incorporate the special case of a Lomax distribution. Using a modified generalized extreme value distribution and comparing to a peak over threshold model fitting approach, we assess which models are able to capture the true underlying parameters. Additionally, we used a well-known linear bootstrap bias-corrrection and evaluate its utility to correct the bias in the estimation. Our results show that threshold models are the best at capturing the true underlying probability of long-distance dispersal models, and that the bias correction is somewhat useful, depending on the sample size. Estimating rare events in ecology is an important and statistically challenging process and in this study we provide a simple probabilistic approach to doing so.

ecology↗

bioRxiv2024

Direct inference of the distribution of fitness effects of spontaneous mutations from recombinant inbred C. elegans mutation accumulation lines

The distribution of fitness effects (DFE) of new mutations plays a central role in evolutionary biology. Estimates of the DFE from experimental Mutation Accumulation (MA) lines are compromised by the complete linkage disequilibrium (LD) between mutations in different lines. To reduce LD, we constructed two sets of recombinant inbred lines from a cross of two C. elegans MA lines. One set of lines ("RIAILs") was intercrossed for ten generations prior to ten generations of selfing; the second set of lines ("RILs") omitted the intercrossing. Residual LD in the RIAILs is much less than in the RILs, which affects the inferred DFE when the sets of lines are analyzed separately. The best-fit model estimated from all lines (RIAILs + RILs) infers a large fraction of mutations with positive effects ([~]40%); models that constrain mutations to have negative effects fit much worse. The conclusion is the same using only the RILs. For the RIAILs, however, models that constrain mutations to have negative effects fit nearly as well as models that allow positive effects. When mutations in high LD are pooled into haplotypes, the inferred DFE becomes increasingly negative-skewed and leptokurtic. We conclude that the conventional wisdom - most mutations have effects near zero, a handful of mutations have effects that are substantially negative and mutations with positive effects are very rare - is likely correct, and that unless it can be shown otherwise, estimates of the DFE that infer a substantial fraction of mutations with positive effects are likely confounded by LD.

evolutionary biology↗

bioRxiv2024

Inferring stability and persistence in the vaginal microbiome: A stochastic model of ecological dynamics

The vaginal microbiome is dynamic, yet the ecological and stochastic forces shaping its stability and persistence remain poorly understood. We developed a multi-species stochastic population model to analyze time-series data from 135 individuals sampled daily over 70 days, integrating ecological theory with microbial dynamics. Our framework explicitly incorporates stochasticity, ecological feedback, and sampling error to quantify community stability. We show that intra- and inter-species interactions and environmental fluctuations critically shape microbial population trajectories. This approach enabled the estimation of species competition coefficients and the identification of distinct stability regimes across individuals. We also introduce a Risk Prediction Monitoring (RPM) tool to track persistence probabilities of key taxa, particularly Lactobacillus spp., mirroring extinction risk models in conservation biology. Our findings challenge static "healthy" vs. "dysbiotic" categorizations and offer a quantitative framework for assessing microbiome resilience. This has direct implications for microbiome-targeted therapies aimed at promoting ecological stability in vaginal bacterial communities.

ecology↗

bioRxiv2024

nQuack: An R package for predicting ploidal level from sequence data using site-based heterozygosity

PremiseTraditional methods of ploidal level estimation are tedious; leveraging sequence data for cytotype estimation is an ideal alternative. Multiple statistical approaches to leverage DNA sequence data for ploidy prediction based on site-based heterozygosity have been developed. However, these approaches may require high-coverage sequence data, use improper probability distributions, or have additional statistical shortcomings that limit inference abilities. We introduce nQuack, an open-source R package, that addresses the main shortcomings of current methods. Methods and ResultsnQuack performs model selection for improved ploidy predictions. Here, we implement expected maximization algorithms with normal, beta, and beta-binomial distributions. Using extensive computer simulations that account for variability in sequencing depth, as well as real data sets, we demonstrate the utility and limitations of nQuack. ConclusionInferring ploidal level based on site-based heterozygosity alone is discouraged due to the low accuracy of pattern-based inference.

bioinformatics↗

bioRxiv2023

Increasing environmental fluctuations can dampen variability of endogenously cycling populations

Understanding how populations respond to increasingly variable conditions is a major objective for natural resource managers attempting to forecast extinction risk. The lesson from current modeling is clear: Increasing environmental variability increases population abundance variability. We show that this paradigm fails to describe a broad class of empirically observed dynamics, namely endogenously-driven population cycles. In contrast to the dominant paradigm, these populations can exhibit reduced long-run population variance under increasing environmental variability. We hypothesize that this paradox arises from interactions between environmental stochasticity and nonlinear density dependence. Such interactions violate the oft-assumed additivity of stochastic and deterministic drivers of population fluctuations present in many models that forecast population size. We show evidence for the interaction in two canonical cyclical populations: flour beetles and Canadian Lynx. To help identify the interaction, we develop new theory to quantify the strength of these interactions by partitioning the effects of nonlinear dynamics and stochastic variation on dynamical systems. In both empirical examples, the partitioning shows that the interaction between deterministic and stochastic dynamics reduces the overall variance in population size. Our results highlight that previous predictions about extinction under environmental variability may prove inadequate to understand the effects of climate change in many populations.

ecology↗

bioRxiv2023

Dynamics of mixed-ploidy populations under demographic and environmental stochasticities

The theoretical population dynamics of autopolyploids - organisms with more than two genome copies of a single ancestral species - and their diploid progenitors have been extensively studied. The acquisition of multiple genome copies, being in essence a stochastic process, strongly suggests a probabilistic approach to examine the long-term dynamics of a population with multiple cytotypes. Yet, our current understanding of empirical evidence on the dynamics of autopolyploid populations has not incorporated stochastic population dynamics. To investigate the factors contributing to the probability and stability of coexisting cytotypes, we designed a new population dynamics model with demographic and environmental stochasticities to simulate the formation, establishment, and persistence of diploids, triploids, and autotetraploids over time when gene flow is allowed among cytotypes. Contrary to previous research, increased selfing rates and pronounced reproductive isolation stabilized the long-run coexistence of multiple cyto-types. In stressful environments, these dynamics become much more complex, and our stochastic modeling approach helped reveal the resulting intricacies that give tetraploids competitive advantage over their diploid progenitors. Our work is fundamental to a better understanding of the dynamics of coexistence of multiple cytotypes and is a necessary step for further work modeling the dynamics between an autopolyploid and its diploid progenitor.

evolutionary biology↗

bioRxiv2018

Asymmetric effects of environmental filtering on the assembly of tropical bird communities along a moisture gradient

The species-sorting hypothesis (SSH) states that environmental factors influence local community assembly of metacommunities by selecting for species that are well adapted to the specific conditions of each site. Along environmental gradients, the strength of selection against individuals that are marginally adapted to the local conditions increases towards the extremes of the environment where the climate becomes harsh. In rainfall gradients, the strength of selection by the environment has been proposed to decrease with rainfall. Under this scenario SSH would predict that immigration of individuals from the metacommunity should be restricted into the dry end of the gradient creating a positive relationship between immigration and rainfall. However, if the selection is strong in both ends of the gradient, the restriction should be expected to be in both directions such that the ends behave as independent metacommunities even in the absence of geographical barriers. In this study we used models based on neutral theory to evaluate if SSH can explain the distribution of bird species along a steep rainfall gradient in Colombia. We found a strong positive relationship between immigration rates and precipitation suggesting that the dry forests impose stronger challenges for marginally adapted bird populations. However, a two-metacommunity model separating dry and wet forests was a better fit to the observed data, suggesting that both extremes impose strong selection against immigrants. The switch from the dry forest to the wet forest metacommunities occurred abruptly over a short geographic distance in the absence of any apparent geographic barrier; this apparent threshold occurs where the forest becomes mostly evergreen. The relative number of rare species in dry forest was lower than in wet forests suggesting that the selection against marginally adapted populations is stronger in the dry forests. Overall, our analyses are consistent with SSH at the regional scales, but the rarity analysis suggests that the mechanisms at the local scales are substantially different. Based on these results, we hypothesize that abiotic (climatic) factors limit immigration into dry forest communities and whereas biotic factors such as competition and predation may limit immigration into bird communities in the wet forest.

ecology↗

bioRxiv2018

Climatic effects on turnover of lowland forest bird communities across a precipitation gradient

One of the main goals of community ecology is to understand the influence of the abiotic environment on the abundance and distribution of species. It has been hypothesized that dry forests are harsher environments than wet forests, which leads to the prediction that environmental filtering should be a more important determinant of patterns of species abundance and composition than in wet forest, where biotic interactions or random assembly should be more important. We attempt to understand the influence of rainfall on the abundance and distribution of bird species along a steep precipitation gradient in an inter-Andean valley in Colombia. We gathered data on species distributions, abundance, morphological traits and phylogenetic relationships to determine the influence of rainfall on the taxonomic, functional and phylogenetic turnover of species along the Magdalena Valley. We demonstrate that there is a strong turnover of community composition at the limit of the dry forest. The taxonomic turnover is steeper than the phylogenetic turnover, suggesting that replacement of closely related species accounts for a disproportionate number of changes along the gradient. We found evidence for environmental filtering in dry forest as species tend to be more tolerant of higher temperature ranges, stronger rainfall seasonality and lower minimum rainfall. On the other hand, wet forest species tend to compete actively for nest space but not for the resources associated with the axes we measured. Our results suggest that rainfall is a strong determinant of community composition when comparing localities above and below the 2400 mm rainfall isocline.

ecology↗

bioRxiv2017

Ecological Change Points: The strength of density dependence and the loss of history

Change points in the dynamics of animal abundances have extensively been recorded in historical time series records. Little attention has been paid to the theoretical dynamic consequences of such change-points. Here we propose a change-point model of stochastic population dynamics. This investigation embodies a shift of attention from the problem of detecting when a change will occur, to another non-trivial puzzle: using ecological theory to understand and predict the post-breakpoint behavior of the population dynamics. The proposed model and the explicit expressions derived here predict and quantify how density dependence modulates the influence of the pre-breakpoint parameters into the post-breakpoint dynamics. Time series transitioning from one stationary distribution to another contain information about where the process was before the change-point, where is it heading and how long it will take to transition, and here this information is explicitly stated. Importantly, our results provide a direct connection of the strength of density dependence with theoretical properties of dynamic systems, such as the concept of resilience. Finally, we illustrate how to harness such information through maximum likelihood estimation for state-space models, and test the model robustness to widely different forms of compensatory dynamics. The model can be used to estimate important quantities in the theory and practice of population recovery.

ecology↗

bioRxiv2017

A parametric interpretation of Bayesian Nonparametric Inference from Gene Genealogies: linking ecological, population genetics and evolutionary processes

Using a nonparametric Bayesian approach Palacios and Minin [1] dramatically improved the accuracy, precision of Bayesian inference of population size trajectories from gene genealogies. These authors proposed an extension of a Gaussian Process (GP) nonparametric inferential method for the intensity function of non-homogeneous Poisson processes. They found that not only the statistical properties of the estimators were improved with their method, but also, that key aspects of the demographic histories were recovered. The authors work represents the first Bayesian nonparametric solution to this inferential problem because they specify a convenient prior belief without a particular functional form on the population trajectory. Their approach works so well and provides such a profound understanding of the biological process, that the question arises as to how truly \"biology-free\" their approach really is. Using well-known concepts of stochastic population dynamics, here I demonstrate that in fact, Palacios and Minins GP model can be cast as a parametric population growth model with density dependence and environmental stochasticity. Making this link between population genetics and stochastic population dynamics modeling provides novel insights into eliciting biologically meaningful priors for the trajectory of the effective population size. The results presented here also bring novel understanding of GP as models for the evolution of a trait. Thus, the ecological principles foundation of Palacios and Minin [1]s prior adds to the conceptual and scientific value of these authorsinferential approach. I conclude this note by listing a series of insights brought about by this connection with Ecology.

genetics↗