Ecological Inference Research Articles

EcolRxC: Ecological inference estimation of R × C tables using latent structure approaches

Abstract Ecological inference is a statistical technique used to infer individual behavior from aggregate data. A particularly relevant instance of ecological inference involves the estimation of the inner cells of a set of R × C related contingency tables when only their aggregate margins are known. This problem spans multiple disciplines, including quantitative history, epidemiology, political science, marketing, and sociology. This paper proposes new models for solving the problem using the latent structure theory, and presents the ecolRxC package, an R implementation of this methodology. This article exemplifies, explains, and statistically documents the new extensions and, using real inner cell election data, shows how the new models in ecolRxC lead to significantly more accurate solutions than ecol and VTR, two Stata routines suggested within this framework. ecolRxC also holds its own against ei.MD.bayes and nslphom, the two algorithms currently identified in the literature as the most accurate to solve this problem. ecolRxC records accuracies as good as those reported for ei.MD.bayes and nslphom. Besides, from a theoretical perspective, ecolRxC stands up for modeling a causal theory of political behavior to build its algorithm. This distinguishes it from other procedures proposed from different frameworks (such as ei.MD.bayes and nslphom) which model expected behaviors, instead of modeling how voters make choices based on their underlying preferences as ecolRxC does.

Machine learning algorithms for the evaluation of risk by tick-borne pathogens in Europe

Background Tick-borne pathogens pose a major threat to human health worldwide. Understanding the epidemiology of tick-borne diseases to reduce their impact on human health requires models covering large geographic areas and considering both the abiotic traits that affect tick presence, as well as the vertebrates used as hosts, vegetation, and land use. Herein, we integrated the public information available for Europe regarding the variables that may affect habitat suitability for ticks and hosts and tested five machine learning algorithms (MLA) for predicting the distribution of four prominent tick species across Europe. Materials and methods A grid of cells 20 km in diameter was prepared to cover the entire territory, containing data on vegetation, points of water, habitat fragmentation, forest density, grass extension, or imperviousness, with information on temperature and water deficit. The distribution of the hosts (162 species) was modelled and included in the dataset. We used five MLA, namely, Random Forest, Neural Networks, Naive Bayes, Gradient Boosting, and AdaBoost, trained with reliable coordinates for Ixodes ricinus, Dermacentor reticulatus, Dermacentor marginatus, and Hyalomma marginatum in Europe. Results Both Random Forest and Gradient Boosting best predicted ticks and host environmental niches. Our results demonstrate that MLA can identify trait-matching combinations of environmental niches. The inclusion of land cover and land use variables has a superior capacity for predicting areas suitable for ticks, compared to classic methods based on the use of climate data alone. Conclusions Flexible MLA-driven models may offer several advantages over traditional models. We anticipate that these results may be extrapolated to other regions and combinations of tick-vertebrates. These results highlight the potential of MLA for inference in ecology and provide a background for the evolution of a completely automatized tool to calculate the seasonality of ticks for early warning systems aimed at preventing tick-borne diseases.

Resolving biology’s dark matter: species richness, spatiotemporal distribution, and community composition of a dark taxon

BackgroundZoology’s dark matter comprises hyperdiverse, poorly known taxa that are numerically dominant but largely unstudied, even in temperate regions where charismatic taxa are well understood. Dark taxa are everywhere, but high diversity, abundance, and small size have historically stymied their study. We demonstrate how entomological dark matter can be elucidated using high-throughput DNA barcoding (“megabarcoding”). We reveal the high abundance and diversity of scuttle flies (Diptera: Phoridae) in Sweden using 31,800 specimens from 37 sites across four seasonal periods. We investigate the number of scuttle fly species in Sweden and the environmental factors driving community changes across time and space.ResultsSwedish scuttle fly diversity is much higher than previously known, with 549 putative species detected, compared to 374 previously recorded species. Hierarchical Modelling of Species Communities reveals that scuttle fly communities are highly structured by latitude and strongly driven by climatic factors. Large dissimilarities between sites and seasons are driven by turnover rather than nestedness. Climate change is predicted to significantly affect the 47% of species that show significant responses to mean annual temperature. Results were robust regardless of whether haplotype diversity or species-proxies were used as response variables. Additionally, species-level models of common taxa adequately predict overall species richness.ConclusionsUnderstanding the bulk of the diversity around us is imperative during an era of biodiversity change. We show that dark insect taxa can be efficiently characterised and surveyed with megabarcoding. Undersampling of rare taxa and choice of operational taxonomic units do not alter the main ecological inferences, making it an opportune time to tackle zoology’s dark matter.

Cautionary tales on the use of proxies to estimate body size and form of extinct animals.

Body size is of fundamental importance to our understanding of extinct organisms. Physiology, ecology and life history are all strongly influenced by body size and shape, which ultimately determine how a species interacts with its environment. Reconstruction of body size and form in extinct animals provides insight into the dynamics underlying community composition and faunal turnover in past ecosystems and broad macroevolutionary trends. Many extinct animals are known only from incomplete remains, necessitating the use of anatomical proxies to reconstruct body size and form. Numerous limitations affecting the appropriateness of these proxies are often overlooked, leading to controversy and downstream inaccuracies in studies for which reconstructions represent key input data. In this perspective, we discuss four prominent case studies (Dunkleosteus, Helicoprion, Megalodon and Perucetus) in which proxy taxa have been used to estimate body size and shape from fragmentary remains. We synthesise the results of these and other studies to discuss nuances affecting the validity of taxon selection when reconstructing extinct organisms, as well as mitigation measures that can ensure the selection of the most appropriate proxy. We argue that these precautionary measures are necessary to maximise the robustness of reconstructions in extinct taxa for better evolutionary and ecological inferences.

Reassessment of French breeding bird population sizes using citizen science and accounting for species detectability.

Higher efficiency in large-scale and long-term biodiversity monitoring can be obtained through the use of Essential Biodiversity Variables, among which species population sizes provide key data for conservation programs. Relevant estimations and assessment of actual population sizes are critical for species conservation, especially in the current context of global biodiversity erosion. However, knowledge on population size varies greatly, depending on species conservation status and ranges. While the most threatened or restricted-range species generally benefit from exhaustive counts and surveys, monitoring common and widespread species population size tends to be neglected or is simply more challenging to achieve. In such a context, citizen science (CS) is a powerful tool for the long-term monitoring of common species through the engagement of various volunteers, permitting data acquisition on the long term and over large spatial scales. Despite this substantially increased sampling effort, detectability issues imply that even common species may remain unnoticed at suitable sites. The use of structured CS schemes, including repeated visits, enables to model the detection process, permitting reliable inferences of population size estimates. Here, we relied on a large French structured CS scheme (EPOC-ODF) comprising 27,156 complete checklists over 3,873 sites collected during the 2021-2023 breeding seasons to estimate the population size of 63 common bird species using hierarchical distance sampling (HDS). These population size estimates were compared to the previous expert-based French breeding bird atlas estimations, which did not account for detectability issues. We found that population size estimates from the former French breeding bird atlas were lower than those estimated using HDS for 65% of species. Such a prevalence of lower estimations is likely due to more conservative estimates inferred from semi-quantitative expert-based assessments used for the previous atlas. We also found that species with long-range songs such as the Common Cuckoo (Cuculus canorus), Eurasian Hoopoe (Upupa epops) or the Eurasian Blackbird (Turdus merula) had, in contrast, higher estimated population sizes in the previous atlas than in our HDS models. Our study highlights the need to rely on sound statistical methodology to ensure reliable ecological inferences with adequate uncertainty estimation and advocates for a higher reliance on structured CS in support of long-term biodiversity monitoring.

A Multi-Method Data Science Pipeline for Analyzing Police Service

Despite the fact that most police departments in the U.S. serve jurisdictions with fewer than 10,000 residents, policing practices in small towns are understudied. This is due in part to data limitations and technological barriers that exist in the small-town context. In this article we focus on one small town police department in New England with a history of misconduct, and develop a comprehensive data science pipeline that addresses the stages from design and collection to reporting. We present the reader with specific tools in the open-source Python ecosystem for replicating this pipeline. Once these data are processed, we perform two statistical analyses in an attempt to better understand the provisions of service by the small-town police department of focus. First, we perform ecological inference to estimate the rate at which residents are placing calls for service. Second, we model wait times using a negative binomal regression model to account for overdispersion in the data. We discuss data and model limitations arising through the pipeline creation and analysis process.

A protocol for assessing bias and robustness of social network metrics using GPS based radio-telemetry data

BackgroundSocial network analysis of animal societies allows scientists to test hypotheses about social evolution, behaviour, and dynamic processes. However, the accuracy of estimated metrics depends on data characteristics like sample proportion, sample size, and frequency. A protocol is needed to assess for bias and robustness of social network metrics estimated for the animal populations especially when a limited number of individuals are monitored.MethodsWe used GPS telemetry datasets of five ungulate species to combine known social network approaches with novel ones into a comprehensive five-step protocol. To quantify the bias and uncertainty in the network metrics obtained from a partial population, we presented novel statistical methods which are particularly suited for autocorrelated data, such as telemetry relocations. The protocol was validated using a sixth species, the fallow deer, with a known population size where ∼85%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\sim 85\\%$$\\end{document} of the individuals have been directly monitored.ResultsThrough the protocol, we demonstrated how pre-network data permutations allow researchers to assess non-random aspects of interactions within a population. The protocol assesses bias in global network metrics, obtains confidence intervals, and quantifies uncertainty of global and node-level network metrics based on the number of nodes in the network. We found that global network metrics like density remained robust even with a lowered sample size, while local network metrics like eigenvector centrality were unreliable for four of the species. The fallow deer network showed low uncertainty and bias even at lower sampling proportions, indicating the importance of a thoroughly sampled population while demonstrating the accuracy of our evaluation methods for smaller samples.ConclusionsThe protocol allows researchers to analyse GPS-based radio-telemetry or other data to determine the reliability of social network metrics. The estimates enable the statistical comparison of networks under different conditions, such as analysing daily and seasonal changes in the density of a network. The methods can also guide methodological decisions in animal social network research, such as sampling design and allow more accurate ecological inferences from the available data. The R package aniSNA enables researchers to implement this workflow on their dataset, generating reliable inferences and guiding methodological decisions.

MORPHOTYPE MATTERS: AN EXPERIMENTAL ANALYSIS OF THE MORPHOLOGICAL FIDELITY OF GASTROPOD STEINKERNS

Abstract Gastropods are commonly preserved as steinkerns (internal casts), a mode of fossilization that leads to loss of external morphological features. This loss of information is problematic for taxonomic identification and ecological inference in evaluating assemblages where original shell material is not preserved. We seek to quantify how closely gastropod steinkerns represent the morphology of their original shells. We investigated this relationship experimentally by fabricating steinkerns in silicone from modern gastropod shells and comparing their geometry to that of the shells we used to create them. In addition to recording traces of ornamentation such as ribs and spines, we used a theoretical morphospace framework to evaluate the fidelity of shell-coiling parameters in steinkerns. Our results show that some morphotypes reflect their taxonomic identification more accurately than others, indicating that steinkern fidelity is highly variable. Experimental steinkerns consistently cluster less reliably by morphotype than their original shell counterparts. Additionally, we find that shell thickness is an important factor in determining steinkern fidelity. The fidelity of the high-spired Duplicaria duplicata, for example, is significantly lower than the average value for the morphotypes investigated whereas the fidelity of planispiral Haplotrema concavum and open-coiling Epitonium is significantly higher, a trend related to shell thickness. Thus, taxonomic identification and subsequent analyses, such as community composition, of steinkern assemblages must recognize this differential fidelity to counter preservational biases.

Forecasting animal distribution through individual habitat selection: insights for population inference and transferable predictions

Habitat selection models frequently use data collected from a small geographic area over a short window of time to extrapolate patterns of relative abundance into unobserved areas or periods of time. However, such models often poorly predict the distribution of animal space‐use intensity beyond the place and time of data collection, presumably because space‐use behaviors vary between individuals and environmental contexts. Similarly, ecological inference based on habitat selection models could be muddied or biased due to unaccounted individual and context dependencies. Here, we present a modeling workflow designed to allow transparent variance‐decomposition of habitat‐selection patterns, and consequently improved inferential and predictive capacities. Using global positioning system (GPS) data collected from 238 individual pronghorn, Antilocapra americana, across three years in Utah, USA, we combine individual‐year‐season‐specific exponential habitat‐selection models with weighted mixed‐effects regressions to both draw inference about the drivers of habitat selection and predict space‐use in areas/times where/when pronghorn were not monitored. We found a tremendous amount of variation in both the magnitude and direction of habitat selection behavior across seasons, but also across individuals, geographic regions, and years. We were able to attribute portions of this variation to season, movement strategy, sex, and regional variability in resources, conditions, and risks. We were also able to partition residual variation into inter‐ and intra‐individual components. We then used the results to predict population‐level, spatially and temporally dynamic, habitat‐selection coefficients across Utah, resulting in a temporally dynamic map of pronghorn distribution at a 30 × 30 m resolution but an extent of 220 000 km2. We believe our transferable workflow can provide managers and researchers alike a way to turn limitations of traditional habitat selection models – variability in habitat selection – into a tool to understand and predict species‐habitat associations across space and time.

Fine-scale characterization of the soybean rhizosphere microbiome via synthetic long reads and avidity sequencing

BackgroundThe rhizosphere microbiome displays structural and functional dynamism driven by plant, microbial, and environmental factors. While such plasticity is a well-evidenced determinant of host health, individual and community-level microbial activity within the rhizosphere remain poorly understood, due in part to the insufficient taxonomic resolution achieved through traditional marker gene amplicon sequencing. This limitation necessitates more advanced approaches (e.g., long-read sequencing) to derive ecological inferences with practical application. To this end, the present study coupled synthetic long-read technology with avidity sequencing to investigate eukaryotic and prokaryotic microbiome dynamics within the soybean (Glycine max) rhizosphere under field conditions.ResultsSynthetic long-read sequencing permitted de novo reconstruction of the entire 18S-ITS1-ITS2 region of the eukaryotic rRNA operon as well as all nine hypervariable regions of the 16S rRNA gene. All full-length, mapped eukaryotic amplicon sequence variants displayed genus-level classification, and 44.77% achieved species-level classification. The resultant eukaryotic microbiome encompassed five kingdoms (19 genera) of protists in addition to fungi – a depth unattainable with conventional short-read methods. In the prokaryotic fraction, every full-length, mapped amplicon sequence variant was resolved at the species level, and 23.13% at the strain level. Thirteen species of Bradyrhizobium were thereby distinguished in the prokaryotic microbiome, with strain-level identification of the two Bradyrhizobium species most reported to nodulate soybean. Moreover, the applied methodology delineated structural and compositional dynamism in response to experimental parameters (i.e., growth stage, cultivar, and biostimulant application), unveiled a saprotroph-rich core microbiome, provided empirical evidence for host selection of mutualistic taxa, and identified key microbial co-occurrence network members likely associated with edaphic and agronomic properties.ConclusionsThis study is the first to combine synthetic long-read technology and avidity sequencing to profile both eukaryotic and prokaryotic fractions of a plant-associated microbiome. Findings herein provide an unparalleled taxonomic resolution of the soybean rhizosphere microbiota and represent significant biological and technological advancements in crop microbiome research.

Ecological inferences in Orchidaceae species from the Brazilian subtropical Atlantic Forest based on morphological and functional anatomical traits

Ecological anatomy studies seek to link structural aspects to environmental pressures, improving knowledge about the occurrence and distribution of species. Orchidaceae is a group with high species richness and ecological diversity, well represented in the Brazilian Atlantic Forest, a biodiversity hotspot. The Iguaçu National Park (ParNa Iguaçu) is an important remnant of the southern Brazilian Atlantic Forest with a high richness of Orchidaceae. Given the family's diversity and the relevance of ecological anatomy studies for interpreting traits associated with the survival of plants in certain environments, we studied 38 species of Orchidaceae from ParNa Iguaçu, seeking to identify possible functional groups. For this, we surveyed 57 leaf morpho-anatomical characters and conducted histochemical and statistical tests. The morpho-anatomical traits group the species into two functional groups, with Cyclopogon congestus emerging isolated. In group A, xeromorphic morphoanatomical characters predominate, and group B combines members with meso/hygromorphic characteristics. We advocate the importance of understanding the ecological behavior of species, especially Orchidaceae, due to their important ecological role, in conservation actions in protected areas. Furthermore, our results highlight the morphological and anatomical diversity of the Orchidaceae found in ParNa Iguaçu, as well contributing to the development of better management practices for these plants, considering the threats posed by the opening of clearings and climate change.

Ecological scales of effect vary across space and time

The spatial scale at which an environmental variable is summarized can have considerable impacts on ecological inference of species distribution and abundance. While several analytical approaches have emerged to determine biologically relevant spatial scales – the spatial scale that most strongly influences the ecological patterns observed – identifying key ecological drivers of scale of effect is still underway. Additionally, several predicted ecological drivers of scale of effect can vary across space and time, but little research on spatiotemporal patterns has occurred. Here, I assessed spatial and temporal variation in scales of effect across 32 North American bird species using 18 years of empirical data from the North American breeding bird survey. Scale estimation was then coupled with trait‐based analyses and hypotheses testing of underlying processes of spatial and temporal variation in scales of effect. All 32 species tested exhibited varied scales of effect across years (average annual scales of effect ranging from 0.2 to 4.97 km) and Bird Conservation Regions (BCR), with spatial variability being the most pronounced. Trait‐based analyses revealed a contrary relationship between hand‐wing index, body size, and scale of effect, though the strength of this relationship was contingent on migratory status. Temporal variation in scales of effect was best explained by changes in human development over time, indicating that avian space use can be explained by an increasing human footprint. Additionally, relative population size, consistent with theoretical predictions stemming from density‐dependent population dynamics, explained notable variation in spatial and temporal scales of effect. These findings contribute to the growing landscape ecology literature by providing empirical evidence for hypothesized drivers of scales of effect. By delineating species‐specific scales of effect and elucidating their ecological drivers, this study enhances our understanding of spatial and temporal scales in ecological processes, aiding conservation efforts in a rapidly changing world.

The Acoustic Index User's Guide: A practical manual for defining, generating and understanding current and future acoustic indices

Abstract Ecoacoustics, the study of environmental sound, is a rapidly growing discipline offering ecological insights at scales ranging from individual organisms to whole ecosystems. Substantial methodological developments over the last 15 years have streamlined extraction of ecological information from audio recordings. One widely used set of methods are acoustic indices, which offer numerical summaries of the spectral, temporal and amplitude patterns in audio recordings. Currently, the specifics of each index's background, methodology and the soundscape patterns they are designed to summarise, are spread across multiple sources. Critically, details of index calculation are sometimes scarce, making it challenging for users to understand how index values are generated. Discrepancies in understanding can lead to misuse of acoustic indices or reporting of spurious results. This hinders ecological inference, replicability and discourages adoption of these tools for conservation and ecosystem monitoring, where they might otherwise provide useful insight. Here we present the Acoustic Index User's Guide—an interactive RShiny web app that defines and deconstructs eight of the most commonly used acoustic indices to facilitate consistent application across the discipline. We break the acoustic indices calculations down into easy‐to‐follow steps to better enable practical application and critical interpretation of acoustic indices. We demonstrate typical soundscape patterns using a suite of 91 example audio recordings: 66 real‐world soundscapes from terrestrial, aquatic and subterranean systems around the world, and 25 synthetic files demonstrating archetypal soundscape patterns. Our interpretation figures signpost specific soundscape patterns likely to be reflected in acoustic indices' values. This RShiny app is a living resource; additional acoustic indices will be added in the future through collaboration with authors of pre‐existing and new indices. The app also serves as a best‐practice template for the information required when publishing new acoustic indices, so that authors can facilitate the widest possible understanding and uptake of their indices. In turn, improved understanding of acoustic indices will aid effective hypothesis generation, application and interpretation in ecological research, ecosystem monitoring and conservation management.

An ecological inference approach to the origins of proportional representation

Facing a prospective majority of socialists during the early third of the 20th century, some secular conservative and liberal parties pooled their votes to raise the majority threshold for the left, while others raised it by enacting some form of proportional representation. We use vote transfers, estimated by a new method of ecological inference, to explain those far-reaching choices. We provide a new conceptualization and measurement of the segmentation of the electoral market to test a proposition within the Boix-Rokkan framework: proportional representation reform was chosen when vote transfers foretold coalition failure. To substantiate our claim, we investigate two most similar cases, Denmark during 1910–1918 and New Zealand during 1928–1931, that diverged in the explanatory variable and in the response.

Ecological inferences on invasive carp survival using hydrodynamics and egg drift models

Bighead carp (Hypophthalmichthys nobilis), silver carp (H. molitrix), black carp (Mylopharyngodon piceus), and grass carp (Ctenopharyngodon idella), are invasive species in North America. However, they hold significant economic importance as food sources in China. The drifting stage of carp eggs has received great attention because egg survival rate is strongly affected by river hydrodynamics. In this study, we explored egg-drift dynamics using computational fluid dynamics (CFD) models to infer potential egg settling zones based on mechanistic criteria from simulated turbulence in the Lower Missouri River. Using an 8-km reach, we simulated flow characteristics with four different discharges, representing 45–3% daily flow exceedance. The CFD results elucidate the highly heterogeneous spatial distribution of flow velocity, flow depth, turbulence kinetic energy (TKE), and the dissipation rate of TKE. The river hydrodynamics were used to determine potential egg settling zones using criteria based on shear velocity, vertical turbulence intensity, and Rouse number. Importantly, we examined the difference between hydrodynamic-inferred settling zones and settling zones predicted using an egg-drift transport model. The results indicate that hydrodynamic inference is useful in determining the ‘potential’ of egg settling, however, egg drifting paths should be taken into account to improve prediction. Our simulation results also indicate that the river turbulence does not surpass the laboratory-identified threshold to pose a threat to carp eggs.

Historical Field Records Reveal Habitat as an Ecological Correlate of Locomotor Phenotypic Diversity in the Radiation of Neotropical Geophagini Fishes.

AbstractPhenotypic macroevolutionary studies provide insight into how ecological processes shape biodiversity. However, the complexity of phenotype-ecology relationships underscores the importance of also validating phenotype-based ecological inference with direct evidence of resource use. Unfortunately, macroevolutionary-scale ecological studies are often hindered by the challenges of acquiring taxonomically and spatially representative ecological data for large and widely distributed clades. The South American cichlid fish tribe Geophagini represents a continentally distributed radiation whose early locomotor morphological divergence suggests habitat as one ecological correlate of diversification, but an association between locomotor traits and habitat preference has not been corroborated. Field notes accumulated over decades of collecting across South America provide firsthand environmental records that can be mined for habitat data in support of macroevolutionary ecological research. In this study, we applied a newly developed method to transform descriptive field note information into quantitative habitat data and used it to assess habitat preference and its relationship to locomotor morphology in Geophagini. Field note-derived data shed light on geophagine habitat use patterns and reinforced habitat as an ecological correlate of locomotor morphological diversity. Our work emphasizes the rich data potential of museum collections, including often-overlooked material such as field notes, for evolutionary and ecological research.

Understanding vote transfers in two-round elections without resorting to declared data. The contribution of ecological inference, consolidated with factual information from a case study of the 2014 municipal elections in Montpellier

In electoral sociology, the analysis of vote transfers has traditionally depended on individual data obtained from surveys. Because such data suffer from a significant amount of declaration and memory bias, replacing them with the electoral statistics available down to polling station level may be advantageous. Recent developments in models of ecological inference allow us to use these aggregated data to establish estimates of vote transfers while minimising the risk of ecological error. Nonetheless, the reliability of ecological inference models for estimating vote transfers has thus far received little attention in the form of empirical evaluations. The purpose of the present article is to cast light on this blind spot by analysing a model for predicting electoral volatility in a two-round election, namely the municipal election held in Montpellier in 2014. What makes this approach original is its use of observed information – the proportion of non-voters in both rounds – first to compare this data with the estimates produced by the model and then to integrate it as a modelling parameter to measure its impact on estimated vote allocation. This analysis reveals that the initial model's results are relatively reliable regarding the known parameter, although they slightly overestimate its amplitude and underestimate its variability. The model that integrates information regarding the proportion of consistent non-voters yields estimates close to those obtained using the “raw” model. In terms of interpretative capacity, the value added by integrating this additional information is, therefore, slight. However, integrating the information does make it possible to establish narrower density intervals, reducing the uncertainty associated with the interpretation of the other parameters, particularly proportions associated with candidates who received few votes in the first round.

The importance of independence in unmarked spatial capture–recapture analysis

Wildlife populations can be unmarked, meaning individuals lack distinguishing features for individual identification. Populations may also exhibit non‐independent movements, meaning individuals move together. For populations of either unmarked or non‐independent individuals, models based on spatial capture–recapture (SCR) approaches can be used to estimate abundance, density, and other parameters critical for monitoring, management, and conservation. However, when individuals are both unmarked and non‐independent, few model options are available. One approach has been to apply unmarked models and not address the non‐independence despite unquantified impacts on bias, precision, and the ability to make robust ecological inferences. We conducted a simulation study to quantify the impact of non‐independence on the performance of spatial count (SC) and spatial partial identity models (SPIM) – two SCR‐based unmarked modeling approaches – and used the performance of fully marked and independent SCR as a reference. We varied the levels of non‐independence (aggregation and cohesion), detection probability, and the number of partial identity covariates used to resolve identities in SPIM estimation. We expected abundance estimates to be increasingly biased and precise as aggregation and cohesion increased. Results showed that models indeed became less robust to increasing non‐independence, but importantly suggested that only SPIM could be reliably applied under low levels of cohesion when sufficient partial identity covariates are available. SC yielded consistently biased estimates with poor precision. SCR was consistently robust across combinations of aggregation and cohesion, as expected. We therefore advise against the use of SC models for estimating population parameters when individuals are known to be non‐independent, caution that SPIM may be used under narrow ecological conditions, and encourage continued investigations into sampling design and methods development for estimating populations of unmarked and non‐independent individuals.

Potential Residence and Coexistence Strategy of Tursiops truncatus in a Coastal Lagoon in the Southern Gulf of Mexico: Ecological Inferences Using Stable Isotopes

The Terminos Lagoon Flora and Fauna Protection Area is the largest coastal lagoon in the southern Gulf of Mexico and has the largest population of bottlenose dolphins, Tursiops truncatus, reported in the region. Photo identification studies have reported permanent and seasonal residence of dolphins; however, how dolphins coexist ecologically is unknown. In the present study, from 17 dead specimens stranded on the coast of Isla del Carmen (barrier island) between 2014 and 2016, stable isotopes in the liver, heart, and muscle, which represent different time scales, were analyzed to determine residence through δ13C values and niche width and trophic coexistence through δ15N values. Differences between sexes (δ13C liver: p = 0.009; heart: p = 0.01) and age classes (δ15N muscle: p = 0.02) were found, which indicated trophic sexual segregation and changes in resource use during growth, respectively. The lack of significant differences in δ13C values indicated isotopic equilibrium between the tissues, indicating the possible permanent residence of 12 dolphins in Terminos Lagoon as a coastal ecotype (− 16‰) and a second group (five dolphins) as an oceanic ecotype (− 18‰). In conclusion, most dolphins stranded in the Terminos Lagoon are potential permanent residents of this habitat, with evidence of resource partitioning as a coexistence strategy.

Importance of eDNA taphonomy and sediment provenance for robust ecological inference: Insights from interfacial geochemistry

AbstractRetrieval of modern and ancient environmental DNA (eDNA) from sediments has revolutionized our ability to study past and present ecosystems. Little emphasis has been placed, however, on the fundamentals of the DNA–sediment associations in environmental settings. Consequently, our understanding of mineralogic controls and geochemical processes that take place on the DNA–sediment interface, and its implications for eDNA taphonomy and provenance, remain extremely limited. Here, we apply interfacial geochemical principles to elucidate how depositional processes and the stability of DNA–sediment associations in different environments can influence our interpretation and identify possible interpretational biases arising from neglecting mineral and geochemical controls on eDNA taphonomy. We use atomic force microscopy to show how interfacial geochemical interactions drive DNA adsorption behavior and we outline how to increase the scope and resolution of ecological interpretations from eDNA by combining mineralogic composition information with experimental adsorption data. We bring the concepts together and propose how to integrate sediment provenance as well as mineralogic and geochemical principles in eDNA taphonomy analysis for improved reconstruction of past ecosystems and monitoring of modern ecosystems from eDNA data. We provide a conceptual understanding of how eDNA taphonomy and sediment provenance can be addressed and further applied to enhance the scope, resolution, and accuracy of modern and past ecological reconstructions based on eDNA data.