Biology subjects

Ellis-Soto, D.

Publications and source records attributed to Ellis-Soto, D..

4 recordsLinked to original sources

bioRxiv2026

Biodiversity data portals as methodological filters for iNaturalist: how platform choice can shape biodiversity assessments

iNaturalist has rapidly grown to become one of the largest contributors of global biodiversity data, being widely used in academic research, to support and inform applied conservation, and to help develop policy indicators for decision makers. However, the availability of iNaturalist data varies based on the digital platform it is accessed from. Here, we assess whether the pathway used to access iNaturalist data from three commonly available biodiversity data sources: iNaturalist, the Global Biodiversity Information Facility (GBIF) and ESRIs ArcGIS Online, alters occurrence record availability and downstream analyses for ecological inference. First, we investigate iNaturalist data availability on ArcGIS Online and find that the reduced field metadata for record location and obscuration information can lead to biased assumptions in the spatial ranges of sensitive species. Second, when assessing iNaturalist records available through GBIF, we find that restrictive Creative Commons observation licenses prevent an average of 26.1% of iNaturalist Research Grade records from being exported to GBIF, and this can lead to differences in environmental niche analysis when compared to datasets including all available research grade records. Understanding differences in platform licensing when integrating across biodiversity data repositories is another consideration for researchers and practitioners when conducting biodiversity assessments. Our results show that the pathway through which iNaturalist data are accessed can function as a methodological filter, potentially altering spatial coverage, the climatic conditions represented by occurrence datasets, and downstream ecological analysis.

ecology↗

bioRxiv2025

Extreme weather risk shrinks range size estimates and alters biodiversity predictions

Extreme weather events, including heat waves, cold snaps, and droughts, are increasing in frequency and intensity with expected but little understood consequences for biodiversity. Extreme weather events can push organisms beyond their physiological thermal or hydric tolerances and thus limit where they can persist, affecting their geographic distributions. Species might be especially sensitive to extreme weather at the edges of their geographic ranges, where they are often already living near their physiological limits. However, the influence of climatic variability and extreme weather is often ignored in favor of climatic means when estimating distributional and richness patterns. Here we link hundreds of millions of citizen science bird observations from 2004-2024 to high-resolution extreme weather risk maps to explore how climatic variability and extreme weather risk alters summer and winter distributions and biodiversity patterns for 535 North American species. We find that species distribution models accounting for historical extreme weather risk performed better at predicting richness and the presence of individual species across 220 well-surveyed sites. Models incorporating extreme weather predicted narrower geographic distributions than models relying on only climatic means, with species ranges shrinking an average of 6% in summer and 10% in winter and range truncation observed at the range edges. These effects were observed in both seasons but were particularly strong in winter, a time with greater short-term weather variability than summer. Richness estimates were substantially lower when extreme weather was accounted for, especially in the US southwest and central plains (up to 30-40 fewer species), regions highly prone to extreme heat, cold and drought. Our results suggest that more mechanistically informed biodiversity predictions that account for extreme weather are critical for reliably predicting shifting distributional and biodiversity patterns.

ecology↗

bioRxiv2024

Perceived and observed biases within scientific communities: a case study in movement ecology

Who conducts biological research, where, and how the results are disseminated varies among geographies and identities. Identifying and documenting these forms of bias by research communities is a critical first step towards addressing them. We documented perceived and observed biases in movement ecology. Movement ecology is a rapidly expanding sub-discipline of biology, which is strongly underpinned by fieldwork and technology use. First, we surveyed attendees of an international conference, and discussed the results at the conference (comparing uninformed vs informed perceived bias). Although most researchers identified as bias-aware, only a subset of biases were discussed in conversation. Next, by considering author affiliations from publications in the journal Movement Ecology, we found among-country discrepancies between the country of the authors affiliation and study site location related to national economics. At the within-country scale, we found that race-gender identities of postgraduate biology researchers in the USA differed from national demographics. We discuss the role of potential specific causes for the emergence of bias in the sub-discipline, e.g. parachute-science or accessibility to fieldwork. Undertaking data-driven analysis of bias within research sub-disciplines can help identify specific barriers and first steps towards the inclusion of a greater diversity of participants in the scientific process.

ecology↗

bioRxiv2021

Linking animal migration and ecosystem processes: data-driven simulation of propagule dispersal by migratory herbivores

O_LIAnimal migration is a key process underlying active subsidies and species dispersal over long distances, which affects the connectivity and functioning of ecosystems. Despite much research describing patterns of where animals migrate, we still lack a framework for quantifying and predicting how animal migration affects ecosystem processes. C_LIO_LIIn this study, we aim to integrate animal movement behavior and ecosystem functioning by developing a predictive modeling framework that can inform ecosystem management and conservation. Our framework models individual-level migration trajectories between populations seasonal ranges as well as the resulting dispersal and fate of propagules carried by the migratory animals, and it can be calibrated using empirical data at every step of the modeling process. C_LIO_LIAs a case study, we applied our framework to model the spread of guava seeds, Psidium guajava, by a population of migratory Galapagos tortoises, Chelonoidis porteri, across Santa Cruz Island. Galapagos tortoises are large herbivores that transport seeds and nutrients across the island, while Guava is one of the most problematic invasive species in the Galapagos archipelago. C_LIO_LIOur model is able to predict the pattern of spread of guava seeds alongside tortoises downslope migration range, and it identified areas most likely to see germination success and establishment. Our results show that Galapagos tortoises seed dispersal may particularly contribute to guava range expansion on Santa Cruz Island, due to both long gut retention time and tortoises long-distance migration across vegetation zones. In particular, we predict that tortoises are dispersing a significant amount of guava seeds into the Galapagos National Park, which has important consequences for the native flora. C_LIO_LIThe flexibility and modularity of our framework allows for the integration of multiple data sources. It also allows for a wide range of applications to investigate how migratory animals affect ecosystem processes, including propagule dispersal but also other processes such as nutrient transport across ecosystems. Our framework is also a valuable tool for predicting how animal-mediated propagule dispersal can be affected by environmental change. These different applications can have important conservation implications for the management of ecosystems that include migratory animals. C_LI

ecology↗