Biology subjects

Cobos, M. E.

Publications and source records attributed to Cobos, M. E..

8 recordsLinked to original sources

bioRxiv2025

Spatiotemporal dynamics of questing activity by four tick species in the central Great Plains

In this study, we explore a suite of new ecological niche modeling approaches to illuminate the distributional potential and distributional dynamics of questing individuals of four tick species in the central Great Plains region. Specifically, we improve on typical approaches in distributional ecology by (1) assigning time-specific environmental values to sampling events so that repeated sampling at the same points yields additional information, (2) using both positive and negative records of the species as inputs in model development, and (3) constraining model response shapes in models to resemble the shapes of fundamental ecological niches. Model outcomes demonstrate both seasonal dynamics and differences between species in terms of the geographic potential of questing behavior by Great Plains tick species. These improved approaches in distributional ecology have considerable potential to enrich distributional understanding of vector species distributions.

ecology↗

bioRxiv2024

Selecting sites for strategic surveillance of zoonotic pathogens: a case study in Panama

Surveillance and monitoring of zoonotic pathogens is key to identifying and mitigating emerging public health threats. Surveillance is often designed to be taxonomically targeted or systematically dispersed across geography, however, those approaches may not represent the breadth of environments inhabited by a host, vector, or pathogen, leaving significant gaps in our understanding of pathogen dynamics in their natural reservoirs and environments. As a case study on the design of pathogen surveillance programs, we assess how well 20 years of small mammal surveys in Panama have sampled available environments and propose a multistep approach to selecting survey localities in the future. We use >8,000 georeferenced mammal specimen records, collected as part of a long-term hantavirus surveillance program, to test the completeness of country-wide environmental sampling. Despite 20 years of surveillance, our analyses identified a few key environmental sampling gaps. To refine surveillance strategies, we selected a series of core historically sampled localities, supplemented with additional environmentally distinct sites to more completely represent Panamas environments. Based on lessons learned through decades of surveillance, we propose a series of recommendations to improve strategic sampling for zoonotic pathogen surveillance.

ecology↗

bioRxiv2024

Spatiotemporal dynamic models of Amblyomma americanum questing activity in the Central Great Plains

Ticks represent important vectors of a number of bacterial and viral disease agents, owing to their hematophagous nature and their questing behavior (the process in which they seek new hosts). Questing activity is notably seasonal with spatiotemporal dynamics that needs to be understood in detail as part of mediating and mitigating tick-borne disease risk. Models of the geography of tick questing activity developed to date, however, have ignored the temporal dimensions of that behavior; more fundamentally, they have often not considered the sampling underlying available occurrence data. Here, we have addressed these shortfalls for Amblyomma americanum, the most commonly encountered tick in the central Great Plains, via (1) detailed, longitudinal sampling to characterize the spatiotemporal dimensions of tick questing activity; (2) randomization tests to establish in which environmental dimensions a species is manifesting selective use; and (3) modeling methods that include both presence data and absence data, taking fullest advantage of the information available in the data resource. The outcome was a detailed picture of geographic and temporal variation in suitability for the species through the two-year course of this study. Such models that take full advantage of available information will be crucial in understanding the risk of tick-borne disease into the future.

ecology↗

bioRxiv2024

enmpa: An R package for ecological niche modeling using presence-absence data and generalized linear models

Here, we present the new R package "enmpa," which includes a range of tools for modeling ecological niches using presence-absence data via logistic generalized linear models. The package allows users to calibrate, select, project, and evaluate models using independent data. We have emphasized a comprehensive search for ideal predictor combinations, including linear, quadratic, and two-way interaction responses, to provide more detailed and robust model calibration processes. We demonstrate the use of the package with an example of a simulated pathogen and its niche. Since enmpa is designed specifically to work with presence-absence data, our tools are particularly useful for studies with data derived from a detection or non-detection sampling universe, such as pathogen testing results. enmpa can be downloaded from CRAN, and the source code is freely available on GitHub.

ecology↗

bioRxiv2022

Broad-scale factors shaping the ecological niche and geographic distribution of Spirodela polyrhiza

The choice of appropriate independent variables to create models characterizing ecological niches of species is of critical importance in distributional ecology. This set of dimensions in which a niche is defined can inform about what factors limit the distributional potential of a species. We used a multistep approach to select relevant variables for modeling the ecological niche of the aquatic Spirodela polyrriza, taking into account variability arising from using distinct algorithms, calibration areas, and spatial resolutions of variables. We found that, even after an initial selection of meaningful variables, the final set of variables selected based on statistical inference varied considerably depending on the combination of algorithm, calibration area, and spatial resolution used. However, variables representing extreme temperatures and dry periods were more consistently selected than others, highlighting their importance in shaping the distribution of this species. Other variables related to seasonality of solar radiation, summer solar radiation, and some soil proxies of nutrients in water, were selected commonly but not as frequently as the ones mentioned above. We suggest that the these later variables are also important to understanding the distributional potential of the species, but that their effects may be less pronounced at the scale at which they are represented for the needs of this type of modeling.

ecology↗

bioRxiv2021

New distributional opportunities with niche innovation in Eurasian snowfinches

This study explores the evolutionary history of ecological niche characters in the Eurasian snowfinch lineage. Specifically, we use new analytical approaches to reconstruct ecological niche evolution, taking uncertainty in knowledge of the ecological niche limits into account. We found an overall pattern of niche conservatism in relation to both temperature and precipitation characteristics of niches, but for one dramatic niche evolution event, in Montifringilla nivalis. Interestingly, this species is also that which has by far the broadest geographic distribution among snowfinches. We conclude that an evolutionary change in niche characteristics perhaps within M. nivalis (i.e., present in some and not all of its populations) made possible the broad, westward range expansion of that species, thus changing the distributional potential of the snowfinch lineage dramatically.

evolutionary biology↗

bioRxiv2020

Geographic potential of the world largest hornet, Vespa mandarinia Smith (Hymenoptera: Vespidae), worldwide and particularly in North America

The Asian giant hornet (AGH, Vespa mandarinia) is the worlds largest hornet, occurring naturally in the Indomalayan region, where it is a voracious predator of pollinating insects including honey bees. In September 2019, a nest of Asian giant hornets was detected outside of Vancouver, British Columbia and in May 2020 an individual was detected nearby in Washington state, indicating that the AGH successfully overwintered in North America. Because hornets tend to spread rapidly and become pests, reliable estimates of the potential invasive range of V. mandarinia in North America are needed to assess likely human and economic impacts, and to guide future eradication attempts. Here, we assess climatic suitability for AGH in North America, and suggest that, without control, this species could establish populations across the Pacific Northwest and much of eastern North America. Predicted suitable areas for AGH in North America overlap broadly with areas where honey production is highest, as well as with species-rich areas for native bumble bees and stingless bees of the genus Melipona in Mexico, highlighting the economic and environmental necessity of controlling this nascent invasion.

ecology↗

bioRxiv2019

Assessment and representation of variability in ecological niche model predictions

Ecological niche models are popular tools used in fields such as ecology, biogeography, conservation biology, and epidemiology. These models are used commonly to produce representations of species potential distributions, which are then used to answer other research questions; for instance, where species richness is highest, where potential impacts of climate change can be anticipated, or where to expect spread of invasive species or disease vectors. Although these representations of potential distributions are variable which contributes to uncertainty in these predictions, model variability is neglected when presenting results of ecological niche model analyses. Here, we present examples of how to quantify and represent variability in models, particularly when models are transferred in space and time. To facilitate implementations of analyses of variability, we developed R functions and made them freely available. We demonstrate means of understanding how much variation exists and where this variation is manifested in geographic space. Representing model variability in geographic space gives a reference of the uncertainty in predictions, so analyzing this aspect of model outcomes must be a priority when policy is to be set or decisions taken based on these models. Our open access tools also facilitate post modeling process that otherwise could take days of manual work.

bioinformatics↗