Species invasions represent a significant dimension of global change yet the dynamics of invasions remain poorly understood and are considered rather unpredictable. We explored interannual dynamics of the invasion process in the Eurasian collared dove (Streptopelia decaocto) and tested whether the advance of the invasion front of the species in North America relates to centrality (versus peripherality) within its estimated fundamental ecological niche. We used ecological niche modelling approaches to estimate the dimensions of the fundamental ecological niche on the Old World distribution of the species, and then transferred that model to the New World as measures of centrality versus peripherality within the niche for the species. Although our hypothesis was that the invasion front would advance faster over more favourable (i.e. more central) conditions, the reverse was the case: the invasion expanded faster in areas presenting less favourable (i.e. more peripheral) conditions for the species as it advanced across North America. This result offers a first view of a predictive approach to the dynamics of species' invasions, and thereby has relevant implications for the management of invasive species, as such a predictive understanding would allow better anticipation of coming steps and advances in the progress of invasions, important to designing and guiding effective remediation and mitigation efforts.
Abstract Detailed spatio‐temporal information about geographic distributions of species is critical for biodiversity analyses in conservation and planning. Traditional correlative modelling approaches use species observational data in model calibration and testing in a time‐averaged framework. This method averages environmental values through time to yield a single environmental value for each location. Although valuable for exploring distributions of species at a broad level, this averaging is one of myriad factors impacting model quality and reliability. We sought to optimize traditional correlative niche model performance in distributional ecology contexts by incorporating time specificity into the existing modelling framework. We modified the existing framework to account for temporal dynamics in species' distributions to produce more robust, temporally explicit models. Using the Wood Thrush Hylostichla mustelina as our study species, we introduce a method of (a) deriving a temporally explicit pseudo‐absence dataset using kernel density estimates to replicate relative sampling of sites through time, and (b) incorporating temporally explicit covariates in model calibration. By accounting for location, and month and year of primary data collection, the time‐specific models successfully yielded dynamic predictions reflecting known distributional shifts in Hylocichla mustelina's annual movement pattern. The modified data preparation steps that we present incorporate temporal dimensions into traditional correlational modelling approaches improving predictive capacity and overall utility of these models for highly mobile, short‐lived or behaviourally complex species. With the ability to estimate species' niches in greater detail, time‐specific models will be able to address specific concerns of species‐level management and policy development for highly mobile and/or migratory species, as well as disease vectors of public health interest.
The Biodiversity Informatics Training Curriculum represents an integration of three years of teaching and interaction by many instructors and students in a series of interactions in courses across Africa. Digital videos of these courses--shared openly via YouTube--have been compiled into a first field-wide curriculum, which is presented herein. The compilation is, in effect, a digital textbook covering the entire field of biodiversity informatics.
Abstract We point out complications inherent in biodiversity inventory metrics when applied to large-scale datasets. The number of samples in which a species is detected saturates, such that crucial numbers of detections of rare species approach zero. Any rare errors can then come to dominate species richness estimates, creating upward biases in estimates of species numbers. We document the problem via simulations of sampling from virtual biotas, illustrate its potential using a large empirical dataset (bird records from Cape May, New Jersey, USA), and outline the circumstances under which these problems may be expected to emerge.
Beginning in 2012, the JRS Biodiversity Foundation funded the University of Kansas to carry out a series of courses covering the breadth of the field of biodiversity informatics in cities across Africa. The Biodiversity Informatics Training Curriculum (BITC) was created from these events that used in-person courses taught by world experts in biodiversity informatics fields. The courses reached 120+ students and young professionals from 23 countries across Africa, and the digital videos of the courses reached thousands of viewers and users worldwide. BITC1 concluded in 2015 and a biodiversity informatics "curriculum" was published in the form of a compendium of BITC course materials (Peterson and Ingenloff 2016)—this curriculum has now been used as the basis for multiple programs of study around the world. BITC2 is now funded, again thanks to the JRS Biodiversity Foundation—this second version involves longer, more thematically oriented courses to be held in three east and southern African countries. The first will be held in Rwanda, in September 2019, and will focus on "Measuring Essential Biodiversity Variables and Ecosystem Services." BITC2 courses will begin with an open symposium, and then will proceed with intensive, hands-on, goal-oriented workshops with a small number of participants. The overall objective of the BITC is a community-oriented and community-run set of training events that can provide avenues to advanced study, international collaboration, and region-wide integration of efforts in biodiversity informatics. BITC2 has four main goals: implement new training dimensions in both an open symposium forum and an intensive, goal-and-product-focused approach that we tested in the later courses of BITC1; expand access to learning resources via subtitling and translation; build collaborative BITC communities via social media platforms; and build within-Africa capacity in biodiversity informatics via high-level involvement of a number of African scientists and educators. implement new training dimensions in both an open symposium forum and an intensive, goal-and-product-focused approach that we tested in the later courses of BITC1; expand access to learning resources via subtitling and translation; build collaborative BITC communities via social media platforms; and build within-Africa capacity in biodiversity informatics via high-level involvement of a number of African scientists and educators. These goals will be achieved via three, once-yearly symposium/course combination events which will be organized and implemented by combined leadership teams from the UK, USA, and African countries, and are intended to provide a combined total of 30-33 days of training to 36-42 trainees. Here we present the format of the BITC1 and BITC2 courses, and the positive and negative outcomes that have resulted, with an eye to optimal design of future such initiatives.
Abstract We point out complications inherent in biodiversity inventory metrics when applied to large-scale datasets. The number of samples in which a species is detected saturates, such that crucial numbers of detections of rare species approach zero. Any rare errors can then come to dominate species richness estimates, creating upward biases in estimates of species numbers. We document the problem via simulations of sampling from virtual biotas, illustrate its potential using a large empirical dataset (bird records from Cape May, New Jersey, USA), and outline the circumstances under which these problems may be expected to emerge.
This presentation was given as part of the GIS Day@KU symposium on November 18, 2015. For more information about GIS Day@KU activities, please see http://www.gis.ku.edu/gisday/2015/.
Assessing and addressing biodiversity needs are of critical and time-sensitive importance, with the post-2020 Global Biodiversity Framework’s Global Taxonomy Initiative underscoring the need to build capacity in how we conceptualize biodiversity (Abrahamse et al. 2021). Species—as biological units—and their names are the backbone for the data integration and synthesis needed for biodiversity research and conservation decision-making (Grace et al. 2021). In integrating name sources for a single taxonomic group, barriers frequently limit linking species names across regional and global authoritative sources. In response to such challenges, we present a case study testing a Globally Integrated Structure of Taxonomy (GIST) to ensure the integration of taxonomy in biodiversity and conservation sciences . This framework characterizes the components enabling transparent association of species names through synonymy (alternate names or spellings), authorship, specimens, versions and timestamps, and taxonomic relationships in space and time. Taxonomy provides a critical link between biodiversity data types and databases. Efforts towards global taxonomic integration are confounded by insufficient connectivity between taxonomic assemblages, with implications for research, monitoring, and conservation practice (Christie et al. 2021, Jetz et al. 2019, McClure et al. 2020). In attempting to match multiple taxonomic groups across databases, we highlight current progress and remaining challenges to produce and use a GIST . We evaluate the standardized, comprehensive taxonomies of mammals, birds, reptiles, amphibians, dragonflies and damselflies, butterflies, ants, plants, and crabs produced for the Map of Life project (Jetz et al. 2012), identifying which missing components impede their utility. We show that for terrestrial mammals, GIST standards are almost fulfilled, but for invertebrate taxa, such as butterflies, GIST standards are unmet, resulting in broken taxonomic links between aggregators of genetic, spatial, functional, and physical data. We find that even the comprehensive taxonomies we examine do not harmonize well with taxonomies of global genetics, phylogenetics, macroecology, and conservation databases. This is because current taxonomic data infrastructures on biodiversity respositories lack the necessary structural components, searchability, and name source transparency to fully integrate taxonomies, as different independently advancing data sources lack standard metadata practices and operable interfaces. The GIST components enable data linkage and provide clear sourcing and metadata, enabling taxonomic data accessibility, reuse, and interoperability. This structure can act as a step toward open and FAIR (Findable, Accessible, Interoperable, and Reusable) data practice as it relates to taxon names (Wilkinson et al. 2016). Without transparent, integrated, accessible, and updated taxonomic information, macroecological inferences and conservation decisions for even charismatic groups are impeded.
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