Biology subjects

Viana, D. S.

Publications and source records attributed to Viana, D. S..

4 recordsLinked to original sources

bioRxiv2025

Falcons use wind assistance and remote islands to mitigate risk during ocean-crossings

Wind conditions play a major role in determining how birds negotiate ecological barriers during long-distance migration. Migrants typically minimize flights over barriers in opposing winds, while crossing the same barriers directly in supportive winds. Nevertheless, even in supportive conditions, barrier-crossings likely involve a number of risk-mitigating behaviors, including the use of stepping stones. We aimed to understand how seasonal ocean-crossings of Eleonoras falcons (Falco eleonorae) compare in terms of wind support and flight effort, and whether falcons use islands to cross safely in variable wind conditions. To do this we combined GPS-tracking data from 19 individuals over a decade (2012-2022) with wind data from an atmospheric reanalysis model. Despite major differences in wind support, falcons achieved similar ground speeds in both seasons. That is because falcons reduce instantaneous flight effort by flying at lower airspeeds in supportive spring winds (31.5 {+/-} 6.0 km h-1) than in adverse autumn winds (47.4 {+/-} 9.3 km h-1). Overall, it took falcons twice as many flight hours to complete the spring crossings that were 40% greater in terms of air distance compared to autumn crossings. Islands were more frequently used during spring crossings (57.9% individuals in spring vs 21.1% in autumn), and the probability of a falcon using an island significantly increased with weaker wind support in spring (but not in autumn). While wind support partially offsets the extra distance flown over the ocean in spring, isolated islands offer emergency stop-over opportunities when Eleonoras falcons experience relatively weak wind support during protracted ocean crossings.

animal behavior and cognition↗

bioRxiv2020

Disentangling metacommunity processes using multiple metrics in space and time

Metacommunity ecology has focused on using observational and analytical approaches to disentangle the role of critical assembly processes, such as dispersal limitation and environmental filtering. Many methods have been proposed for this purpose, most notably multivariate analyses of species abundance and its association with variation in spatial and environmental conditions. These approaches tend to focus on few emergent properties of metacommunities and have largely ignored temporal community dynamics. By doing so, these are limited in their ability to differentiate metacommunity dynamics. Here, we develop a Virtual ecologist approach to evaluate critical metacommunity assembly processes based on a number of summary statistics of community structure across space and time. Specifically, we first simulate metacommunities emphasizing three main processes that underlie metacommunity dynamics (density-independent responses to abiotic conditions, density-dependent biotic interactions, and dispersal). We then calculate a number of commonly used summary statistics of community structure in space and time, and use random forests to evaluate their utility for understanding the strength of these three processes. We found that: (i) time series are necessary to disentangle metacommunity processes, (ii) each of the three studied processes is distinguished with different descriptors, (iii) each summary statistic is differently sensitive to temporal and spatial sampling effort. Some of the most useful statistics include the coefficient of variation of abundances through time and metrics that incorporate variation in the relative abundances (evenness) of species. Surprisingly, we found that when we only used a single snapshot of community variation in space, the most commonly used approaches based on variation partitioning were largely uninformative regarding assembly processes, particularly, variation in dispersal. We conclude that a combination of methods and summary statistics will be necessary to understand the processes that underlie metacommunity assembly through space and time.

ecology↗

bioRxiv2019

Partitioning environment and space in species-by-site matrices: a comparison of methods for community ecology and macroecology

Community ecologists and macroecologists have long sought to evaluate the importance of environmental conditions in determining species distributions, community composition, and diversity across sites. Different methods have been used to estimate species-environment relationships, but their differences to jointly fit and disentangle spatial autocorrelation and structure remain poorly studied. We compared how methods in four broad families of statistical models estimated the contribution of the environment and space to variation in species binary occurrence and abundance. These methods included distance-based regression, generalized linear models (GLM, and the special case of RDA), generalized additive models (GAM), and tree-based machine learning (ML): regression trees, boosted regression trees (BRT), and random forests. The spatial component of the model consisted of spatial distance (in distance-based regression), Morans Eigenvector Maps (MEM; in GLM and ML), smooth spatial splines (in GAM), or tree-based non-linear modelling of spatial coordinates (in ML). We simulated typical site-by-species data to assess the methods performance in (1) fitting environmental and spatial models, and (2) partitioning the variation explained by environmental and spatial predictors. We observed marked differences in performance mostly caused by imbalanced performance in estimating environmental and spatial effects. Such differences also manifested when analyzing eight different empirical datasets. GLM and BRT with MEMs were generally the most reliable methods for partitioning the variation explained by environmental and spatial effects across a wide range of simulated scenarios. The remaining methods tended to underfit simulated spatial structures, causing underestimation of spatial fractions of variation. Our results suggest that previously overlooked methods for performing variation partitioning, especially tree-based ML, offer flexible approaches to analyze site-by-species matrices. We provide general guidelines on the usefulness of different models under different ecological and sampling scenarios, for species distribution modelling, community ecology, and macroecology.

ecology↗

bioRxiv2019

A process-based framework for metacommunity ecology

The metacommunity concept has the potential to integrate local and regional dynamics within a general community ecology framework. To this end, the concept must move beyond the discrete archetypes that have largely defined it (e.g. neutral vs. species sorting) and better incorporate local scale species interactions and coexistence mechanisms. Here, we present a fundamental reconception of the framework that explicitly links local coexistence theory to the spatial processes inherent to metacommunity theory, allowing for a continuous range of competitive community dynamics. These dynamics emerge from the three underlying processes that shape ecological communities: 1) density-independent responses to abiotic conditions, 2) density-dependent biotic interactions, and 3) dispersal. Stochasticity is incorporated in the demographic realization of each of these processes. We formalize this framework using a simulation model that explores a wide range of competitive metacommunity dynamics by varying the strength of the underlying processes. Using this model and framework, we show how existing theories, including the traditional metacommunity archetypes, are linked by this common set of processes. We then use the model to generate new hypotheses about how the three processes combine to interactively shape diversity, functioning, and stability within metacommunities. Statement of authorshipThis project was conceived at the sTURN working group, of which all authors are members. PLT developed the framework and model with input from all authors. PLT wrote the model code. PLT and LMG performed the simulations. PLT produced the figures and wrote the first draft with input from LMG and JMC. All authors provided feedback and edits on several versions of the manuscript. Data accessibilityAll code for running the simulation model and producing the figures is archived on Zenodo - https://doi.org/10.5281/zenodo.3833035.

ecology↗