Biology subjects

Silman, M. R.

Publications and source records attributed to Silman, M. R..

7 recordsLinked to original sources

bioRxiv2025

Upslope diversification and gene flow among congeneric Andean trees

O_LIHybridization and introgression are increasingly recognized as important in plant evolution, yet their prevalence and consequences remain poorly understood in tropical Andean trees, where species coexist at low densities and turn over rapidly across elevation gradients. C_LIO_LIHere, we investigate whether species boundaries are maintained among congeneric Andean Prunus distributed across the Andes-Amazon elevational gradient by reconstructing phylogenetic relationships, divergence times, and patterns of historical and recent gene flow. C_LIO_LIWe find that Andean Prunus diversified during the Miocene, likely in response to novel habitat opportunities resulting from mountain uplift, combined with allopatric processes. Despite this evolutionary divergence, these species show extensive ancient introgression and recent admixture, indicating that species boundaries remain permeable across both ecological and phylogenetic distance. C_LIO_LITogether, these results suggest that Andean Prunus comprise distinct but evolutionarily connected lineages that retain morphological identity while potentially functioning as a long-lived syngameon, with implications for understanding the origin and maintenance of tropical tree diversity and future response to global change. C_LI

evolutionary biology↗

bioRxiv2025

Hydrological Connectivity Enhances Fish Biodiversity in Amazonian Mining Ponds: Insights from eDNA and Traditional Sampling

The expansion of artisanal and small-scale gold mining (ASGM) in the Madre de Dios region of the Peruvian Amazon has transformed primary forests into a novel wetland complex of thousands of abandoned mining ponds. Despite their ecological relevance, post-mining recovery of these systems remains understudied, particularly regarding fish biodiversity and recolonization. In this study, we evaluate fish community richness and composition in mining ponds of different dimensions, years post abandonment, and degree of pulse flood connectivity using traditional collection-based methods and environmental DNA (eDNA) with the 12S and COI markers. We compared these two methods of biodiversity inventory and contrasted results from ASGM waterbodies with those obtained from nearby pristine oxbow lakes. Overall, we registered more fish richness at all sites using eDNA vs traditional methods, especially with the 12S marker. We identified 14 and 13 unique genera using traditional methods and eDNA, respectively, with 40 genera detected by both approaches, evidencing their complementarity. Notably, we found that the degree of pulse flooding connectivity was the main predictor of species richness among the abandoned mining ponds (p-value < 0.05). We registered 11 to 22, 23 to 71, and 56 morphospecies in non-flooded mining ponds, pulse flooded mining ponds and nearby oxbow lakes, respectively. Furthermore, the fish community composition of mining ponds most influenced by pulse flooding were similar to that of pristine lakes. Our findings highlight the role of hydrological connectivity in ecological recovery within mining-impacted wetlands. Future restoration efforts should enhance aquatic connectivity to accelerate recovery in post-mining environments.

ecology↗

bioRxiv2025

Experimental assessment of large mammal population estimates from airborne thermal videography

Wildlife resource management requires reliable, fast, and affordable methods of surveying wildlife populations to develop and adaptively adjust policies. Many methods are used to survey populations of large mammals, and thermal video from drones can yield high rates of detection over large extents with relative speed and safety. In wild populations it can be difficult to estimate detection rates and the accuracy of resulting abundance estimates, especially because many factors of study design and natural systems can influence counts from thermal surveillance. We surveyed a captive white-tailed deer (Odocoileus virginianus) population of known size at the 174-ha Auburn Deer Facility in Auburn, AL, USA, and used a designed experiment to investigate the effects of observers, time-of-day, and day-to-day variation on the accuracy of abundance estimates from trial flights using drone-based airborne thermal videography. The experiment consisted of 20 full-census trials, occurring over 4 days, at times-of-day before sunset, after sunset, near midnight, before sunrise, and after sunrise, with each trial video counted by three trained observers. Counts across all trials and observers yielded a mean point estimate of 77 (95% CI 71-83) deer representing 81-97% of the known population range. Flights near sunset yielded estimates of the highest accuracy, within or close to the true range of the population. Variability in estimates was primarily influenced by daily climatic conditions and time-of-day, with only minor observer effects. Results highlight the importance of day-to-day variability in environmental conditions and diel processes, such as nocturnal cooling of the environment and crepuscular peaks in deer activity. The estimates from our experiment and resulting model illustrate that censuses conducted at optimal times-of-day and averaged across multiple days were able to achieve accurate estimates within the known range without correction, and that by averaging across flights, even including suboptimal conditions, thermal videography returned estimates within 4% of the known range with substantial overlap of the estimate. Conversely, single estimates chosen at random from any time-of-day varied widely around the known value, highlighting the need to plan flights with respect to diurnal activity patterns and landscape thermal properties, or, in the absence of information, average multiple surveys across times and days.

ecology↗

bioRxiv2023

Camera trap methods and drone thermal surveillance provide reliable, comparable density estimates of large, free-ranging ungulates

Camera traps and drone surveys both leverage advancing technologies to study dynamic wildlife populations with little disturbance. Both techniques entail strengths and weaknesses, and common camera trap methods can be confounded by unrealistic assumptions and prerequisite conditions. We compared three methods to estimate the population density of white-tailed deer (Odocoileus virgnianus) at a section of Pilot Mountain State Park, NC, USA: (1) camera trapping using mark-resight ratios or (2) N-mixture modelling, and (3) aerial thermal videography from a drone platform. All three methods yielded similar density estimates, suggesting that they converged on an accurate estimate. We also included environmental covariates in N-mixture modeling to explore spatial habitat use, and we fit models for each season to understand temporal changes in population density. Deer occurred in greater densities on warmer, south-facing slopes in the fall and winter, and on cooler north-facing slopes and in areas with flatter terrain in the summer. Seasonal density estimates over two years suggested an annual cycle of higher densities in fall and winter than in summer, suggesting that the region may function as a refuge during the hunting season. Simple SummaryWildlife researchers and managers can choose from several techniques to estimate the number of animals in a population. Camera traps and drones are increasingly common and cost-effective options that can estimate the number and density of wildlife. We compared three simple methods that are used to estimate the number and density of deer in Pilot Mountain State Park: (1) mark-resighting and (2) N-mixture modeling, both using camera trap data, and (3) extrapolating transect counts from thermal videos collected from an aerial drone. We found that all three methods provided similar estimates of the populations density, with complementary strengths and weaknesses: drone surveys collected data quickly and precisely, mark-resight ratios provided estimates of different demographic groups, and N-mixture modeling revealed changes in density across different habitat qualities and seasons.

ecology↗

bioRxiv2021

A low-cost, long-term underwater camera trap network coupled with deep residual learning image analysis

AO_SCPLOWBSTRACTC_SCPLOWO_LIUnderstanding long-term trends in marine ecosystems requires accurate and repeatable counts of fishes and other aquatic organisms on spatial and temporal scales that are difficult or impossible to achieve with diver-based surveys. Long-term, spatially distributed cameras, like those used in terrestrial camera trapping, have not been successfully applied in marine systems due to limitations of the aquatic environment. C_LIO_LIHere, we develop methodology for a system of low-cost, long-term camera traps (Dispersed Environment Aquatic Cameras), deployable over large spatial scales in remote marine environments. We use machine learning to classify the large volume of images collected by the cameras. We present a case study of these combined techniques use by addressing fish movement and feeding behavior related to grazing halos, a well-documented benthic pattern in shallow tropical reefscapes. C_LIO_LICameras proved able to function continuously underwater at deployed depths (up to 7 m, with later versions deployed to 40 m) with no maintenance or monitoring for over five months, and collected time-lapse images during daylight hours for a total of over 100,000 images. Our ResNet-50-based deep learning model achieved 92.5% overall accuracy in sorting images with and without fish, and diver surveys revealed that the camera images accurately represented local fish communities. C_LIO_LIThe cameras and machine learning classification represent the first successful method for broad-scale underwater camera trap deployment, and our case study demonstrates the cameras potential for addressing questions of marine animal behavior, distributions, and large-scale spatial patterns. C_LI

ecology↗

bioRxiv2020

The evolutionary assembly of forest communities along environmental gradients: recent diversification or sorting of pre-adapted clades?

O_LIBiogeographic events occurring in the deep past can contribute to the structure of modern ecological communities. However, little is known about how the emergence of environmental gradients shape the evolution of species that underlie community assembly. In this study, we address how the creation of novel environments lead to community assembly via two non-mutually exclusive processes: 1) the immigration and ecological sorting of pre-adapted clades (ISPC), and 2) recent adaptive diversification (RAD). We study these processes in the context of the elevational gradient created by the uplift of the Central Andes. C_LIO_LIWe develop a novel approach and method based on the decomposition of species turnover into within- and among-clade components, where clades correspond to lineages that originated before mountain uplift. Effects of ISPC and RAD can be inferred from how components of turnover change with elevation. We test our approach using data from over 500 Andean forest plots. C_LIO_LIWe found that species turnover between communities at different elevations is dominated by the replacement of clades that originated before the uplift of the Central Andes. C_LIO_LIOur results suggest that immigration and sorting of clades pre-adapted to montane habitats is the primary mechanism shaping communities across elevations. C_LI

ecology↗

bioRxiv2020

Evolutionary heritage shapes tree distributions along an Amazon-to-Andes elevation gradient

Understanding how evolutionary constraints shape the elevational distributions of tree lineages provides valuable insight into the future of tropical montane forests under global change. With narrow elevational ranges, high taxonomic turnover, frequent habitat specialisation, and exceptional levels of endemism, tropical montane forests and trees are predicted to be highly sensitive to environmental change. Using plot census data from a gradient traversing >3000 m in elevation on the Amazonian flank of the Peruvian Andes, we employ phylogenetic approaches to assess the influence of evolutionary heritage on distribution trends of trees at the genus level. We find that closely related lineages tend to occur at similar mean elevations, with sister genera pairs occurring a mean 254 m in elevation closer to each other than the mean elevational difference for all genera pairs. We also demonstrate phylogenetic clustering both above and below 1750 m a.s.l, corresponding roughly to the cloud-base ecotone. Belying these general trends, some lineages occur across many different elevations. However, these highly plastic lineages are not phylogenetically clustered. Overall, our findings suggest that tropical montane forests are home to unique tree lineage diversity, constrained by their evolutionary heritage and vulnerable to substantial losses under environmental changes, such as rising temperatures or an upward shift of the cloud base.

ecology↗