Biology subjects

Jensen, A. J.

Publications and source records attributed to Jensen, A. J..

4 recordsLinked to original sources

bioRxiv2025

iNaturalist mammal observations classified by evidence type

Ecologists show growing interest in observational data generated by participatory scientists. For mammals, the largest participatory science platform is iNaturalist, which has more than 2.8 million observations from North American represented through images of living animals, dead animals, tracks, and scat. These different types of evidence could give insight into the underlying sampling paradigm for an observation (e.g. dead animals might be more likely to be reported near roads) and thus may be useful for scientific applications of these data. However, while iNaturalist allows users to annotate observations by evidence type, many observations are not annotated. We use machine learning (ML) to classify the evidence types associated with observations of North American mammals in iNaturalist, adding metadata that can be used to subset data or to model multiple observation processes. Here, we present a dataset containing metadata augmenting 1.33 million North American mammal iNaturalist observations with evidence type. Each observation is categorized as either live animal, dead animal, tracks, scat, or other sign, and an associated confidence score is provided.

ecology↗

bioRxiv2025

Mammal niches are not conserved over continental scales

Summary paragraphThe niche conservatism hypothesis states that a species relationships to habitat and climate conditions are maintained across space and time1-6. Niche stationarity is assumed when ecologists estimate species habitat needs or transfer findings across geographic regions7,8. Recent studies show that some species associations with climate and habitat vary spatially, contradicting the niche conservatism hypothesis9-18. The sources of this nonstationarity are unknown and potential mechanisms remain untested. Here we show that the environmental niches of 36 common North American mammals vary spatially across dimensions of human influence, climate, and landscape. Spatial variation is not explained by known genetic subspecies lineages. Instead, niches vary at a relatively fine spatial scale consistent with adaptations by animals to local conditions, which may be explained by genetic or behavioral changes or by unmodeled interactions with unobserved variables. Spatially varying ecology means that static niche models are not appropriate at large scales and extrapolating species niches to novel contexts may be impossible19. This complicates management decisions based on transferring findings across space and projecting species current environmental associations to responses to future change, but may provide optimism if nonstationarity represents potential for rapid evolutionary rescue.

ecology↗

bioRxiv2022

Distinguishing Signal from Noise: Understanding Patterns of Non-Detections to Inform Accurate Quantitative Metabarcoding

Correcting for amplification biases in genetic metabarcoding data can yield quantitative estimates of template DNA concentrations. However, a major source of uncertainty in metabarcoding data is the presence of non-detections, where a technical PCR replicate fails to detect a species observed in other replicates. Such non-detections are an important special case of variability among technical replicates in metabarcoding data, particularly in environmental samples. While many sampling and amplification processes underlie observed variation in metabarcoding data, understanding the causes of non-detections is an important step in distinguishing signal from noise in metabarcoding studies. Here, we use both simulated and empirical data to 1) develop a qualitative understanding of how non-detections arise in metabarcoding data, 2) outline steps to recognize uninformative data in practice, and 3) identify the conditions under which amplicon sequence data can reliably detect underlying biological signals. We show in both simulations and empirical data that, for a given species, the rate of non-detections among technical replicates is a function of both the template DNA concentration and species-specific amplification efficiency. Consequently, we conclude metabarcoding datasets are strongly affected by (1) deterministic amplification biases during PCR and (2) stochastic sampling of amplicons during sequencing -- both of which we can model -- but also by (3) stochastic sampling of rare molecules prior to PCR, which remains a frontier for quantitative metabarcoding. Our results highlight the importance of estimating species-specific amplification efficiencies and critically evaluating patterns of non-detection in metabarcoding datasets to better distinguish environmental signal from the noise inherent in molecular detections of rare targets.

molecular biology↗

bioRxiv2022

Toward Quantitative Metabarcoding

Amplicon-sequence data from environmental DNA (eDNA) and microbiome studies provides important information for ecology, conservation, management, and health. At present, amplicon-sequencing studies - known also as metabarcoding studies, in which the primary data consist of targeted, amplified fragments of DNA sequenced from many taxa in a mixture - struggle to link genetic observations to underlying biology in a quantitative way, but many applications require quantitative information about the taxa or systems under scrutiny. As metabarcoding studies proliferate in ecology following decades of microbial and microbiome work using similar techniques, it becomes more important to develop ways ot make them quantitative to ensure that their conclusions are adequately supported. Here we link previously disparate sets of techniques for making such data quantitative, showing that the underlying PCR mechanism explains observed patterns of amplicon data in a general way. By modeling the process through which amplicon-sequence data arises, rather than transforming the data post-hoc, we show how to estimate the starting DNA proportions from a mixture of many taxa. We illustrate how to calibrate the model using mock communities and apply the approach to simulated data and a series of empirical examples. Our approach opens the door to improve the use of metabarcoding data in a wide range of applications in ecology, public health, and related fields.

ecology↗