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Abstract As human and automated sensor networks collect increasingly massive volumes of animal observations, new opportunities have arisen to use these data to infer or track species movements. Sources of broad scale occurrence datasets include crowdsourced databases, such as eBird and iNaturalist, weather surveillance radars, and passive automated sensors including acoustic monitoring units and camera trap networks. Such data resources represent static observations, typically at the species level, at a given location. Nonetheless, by combining multiple observations across many locations and times it is possible to infer spatially continuous population-level movements. Population-level movement characterizes the aggregated movement of individuals comprising a population, such as range contractions, expansions, climate tracking, or migration, that can result from physical, behavioral, or demographic processes. A desire to model population movements from such forms of occurrence data has led to an evolving field that has created new analytical and statistical approaches that can account for spatial and temporal sampling bias in the observations. The insights generated from the growth of population-level movement research can complement the insights from focal tracking studies, and elucidate mechanisms driving changes in population distributions at potentially larger spatial and temporal scales. This review will summarize current broad-scale occurrence datasets, discuss the latest approaches for utilizing them in population-level movement analyses, and highlight studies where such analyses have provided ecological insights. We outline the conceptual approaches and common methodological steps to infer movements from spatially distributed occurrence data that currently exist for terrestrial animals, though similar approaches may be applicable to plants, freshwater, or marine organisms. 

Abstract Biodiversity metrics often integrate data on the presence and abundance of multiple species. Yet our understanding of covariation between changes to the numbers of individuals, the evenness of species relative abundances, and the total number of species remains limited. Using individual‐based rarefaction curves, we show how expected positive relationships among changes in abundance, evenness and richness arise, and how they can break down. We then examined interdependencies between changes in abundance, evenness and richness in more than 1100 assemblages sampled either through time or across space. As predicted, richness changes were greatest when abundance and evenness changed in the same direction, and countervailing changes in abundance and evenness acted to constrain the magnitude of changes in species richness. Site‐to‐site differences in abundance, evenness, and richness were often decoupled, and pairwise relationships between these components across assemblages were weak. In contrast, changes in species richness and relative abundance were strongly correlated for assemblages varying through time. Temporal changes in local biodiversity showed greater inertia and stronger relationships between the component changes when compared to site‐to‐site variation. Overall, local variation in assemblage diversity was rarely due to repeated passive samples from an approximately static species abundance distribution. Instead, changing species relative abundances often dominated local variation in diversity. Moreover, how changing relative abundances combined with changes to total abundance frequently determined the magnitude of richness changes. Embracing the interdependencies between changing abundance, evenness and richness can provide new information to better understand biodiversity change in the Anthropocene. 

Abstract There is a clear demand for quantitative literacy in the life sciences, necessitating competent instructors in higher education. However, not all instructors are versed in data science skills or research-based teaching practices. We surveyed biological and environmental science instructors (n = 106) about the teaching of data science in higher education, identifying instructor needs and illuminating barriers to instruction. Our results indicate that instructors use, teach, and view data management, analysis, and visualization as important data science skills. Coding, modeling, and reproducibility were less valued by the instructors, although this differed according to institution type and career stage. The greatest barriers were instructor and student background and space in the curriculum. The instructors were most interested in training on how to teach coding and data analysis. Our study provides an important window into how data science is taught in higher education biology programs and how we can best move forward to empower instructors across disciplines. 

Human activities are fundamentally altering biodiversity. Projections of declines at the global scale are contrasted by highly variable trends at local scales, suggesting that biodiversity change may be spatially structured. Here, we examined spatial variation in species richness and composition change using more than 50,000 biodiversity time series from 239 studies and found clear geographic variation in biodiversity change. Rapid compositional change is prevalent, with marine biomes exceeding and terrestrial biomes trailing the overall trend. Assemblage richness is not changing on average, although locations exhibiting increasing and decreasing trends of up to about 20% per year were found in some marine studies. At local scales, widespread compositional reorganization is most often decoupled from richness change, and biodiversity change is strongest and most variable in the oceans. 

ABSTRACT MotivationHere, we make available a second version of the BioTIME database, which compiles records of abundance estimates for species in sample events of ecological assemblages through time. The updated version expands version 1.0 of the database by doubling the number of studies and includes substantial additional curation to the taxonomic accuracy of the records, as well as the metadata. Moreover, we now provide an R package (BioTIMEr) to facilitate use of the database. Main Types of Variables IncludedThe database is composed of one main data table containing the abundance records and 11 metadata tables. The data are organised in a hierarchy of scales where 11,989,233 records are nested in 1,603,067 sample events, from 553,253 sampling locations, which are nested in 708 studies. A study is defined as a sampling methodology applied to an assemblage for a minimum of 2 years. Spatial Location and GrainSampling locations in BioTIME are distributed across the planet, including marine, terrestrial and freshwater realms. Spatial grain size and extent vary across studies depending on sampling methodology. We recommend gridding of sampling locations into areas of consistent size. Time Period and GrainThe earliest time series in BioTIME start in 1874, and the most recent records are from 2023. Temporal grain and duration vary across studies. We recommend doing sample‐level rarefaction to ensure consistent sampling effort through time before calculating any diversity metric. Major Taxa and Level of MeasurementThe database includes any eukaryotic taxa, with a combined total of 56,400 taxa. Software Formatcsv and. SQL. 

Abstract The species composition of plant and animal assemblages across the globe has changed substantially over the past century. How do the dynamics of individual species cause this change? We classified species into seven unique categories of temporal dynamics based on the ordered sequence of presences and absences that each species contributes to an assemblage time series. We applied this framework to 14,434 species trajectories comprising 280 assemblages of temperate marine fishes surveyed annually for 20 or more years. Although 90% of the assemblages diverged in species composition from the baseline year, this compositional change was largely driven by only 8% of the species' trajectories. Quantifying the reorganization of assemblages based on species shared temporal dynamics should facilitate the task of monitoring and restoring biodiversity. We suggest ways in which our framework could provide informative measures of compositional change, as well as leverage future research on pattern and process in ecological systems.