Predicted impact of climate change on the distribution of the Critically Endangered golden mantella (Mantella aurantiaca) in Madagascar

The impact of climate change on Malagasy amphibians remains poorly understood. Equally, deforestation, fragmentation, and lack of connectivity between forest patches may leave vulnerable species isolated in habitat that no longer suits their environmental or biological requirements. We assess the predicted impact of climate change by 2085 on the potential distribution of a Critically Endangered frog species, the golden mantella (Mantella aurantiaca), that is confined to a small area of the central rainforest of Madagascar. We identify potential population distributions and climatically stable areas. Results suggest a potential south-eastwardly shift away from the current range and a decrease in suitable habitat from 2110 km2 under current climate to between 112 km2 – 138 km2 by the year 2085 – less than 7 % of currently available suitable habitat. Results also indicate that the amount of golden mantella habitat falling within protected areas decreases by 86 % over the same period. We recommend research to ascertain future viability and the feasibility of expanding protection to newly identified potential sites. This information can then be used in future conservation actions such as habitat restoration, translocations, re introductions or the siting of further wildlife corridors or protected areas.

Seasonal mite population distribution on Caryocar brasiliense trees in the Cerrado domain

Caryocar brasiliense Camb. (Malpighiales: Caryocaraceae) trees have a wide distribution in the Cerrado, and it is protected by federal laws. The need to monitor and understand pest damage to crops and forests is a major motivation for the study of population distribution. The mites (Acari) population distributions on C. brasiliense are unknown. We studied seasonal mite population distribution and their ecological indices on C. brasiliense trees in Cerrado areas during three years. Greatest number of Agistemus sp. (Stigmaeidae), Histiostoma sp. (Histiostomidae), Proctolaelaps sp. (Ascidae), and diversity and species richness on leaves of C. brasiliense occurred in the autumn; Tetranychus sp.1 (Tetranychidae) on leaves in the autumn and winter; Histiostoma sp., and Proctolaelaps sp. in fruits in the summer. No significant effect of season was observed in the abundance of Eutetranychus sp., Tetranychus sp.2 (Tetranychidae) and Acaridae. The populations of Acaridae, Eutetranychus sp., Proctolaelaps sp. and Tetranychus sp.1 negatively correlated with temperature. Proctolaelaps sp. and Tetranychus sp.1 correlated negatively with rainfall and Eutetranychus sp. and Proctolaelaps sp. positively with sunlight. The period with low rainfall and relative humidity increases the phytophagous mites and their predators, especially Agistemus sp.. The Tetranychus sp. and Histiostoma sp. species may become pests in C. brasiliense in the Cerrado domain.

Estimating the movements of terrestrial animal populations using broad-scale occurrence data

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.

A first approximation of population distributions on the International Space Station

This article presents an analysis of data derived from thousands of publicly available photographs showing life on the International Space Station (ISS) between 2000 and 2020. The analysis uses crew and locational information from the photographs to identify the distribution of different population groups - by gender, nationality, and space agency affiliation - across modules of ISS for the first time. Given the importance of ISS as the most intensively-inhabited space habitat to date, an international cooperative initiative involving 26 countries and five space agencies, and one of the most expensive building projects ever undertaken by humans, developing an understanding of which people are using different parts of the space station is critical for future usage of this and other stations. This study also sheds light on problems faced by future space station designers who are concerned with optimal usage of their habitats.

Mutagenesis of human genomes by endogenous mobile elements on a population scale

Several large-scale Illumina whole-genome sequencing (WGS) and whole-exome sequencing (WES) projects have emerged recently that have provided exceptional opportunities to discover mobile element insertions (MEIs) and study the impact of these MEIs on human genomes. However, these projects also have presented major challenges with respect to the scalability and computational costs associated with performing MEI discovery on tens or even hundreds of thousands of samples. To meet these challenges, we have developed a more efficient and scalable version of our mobile element locator tool (MELT) called CloudMELT. We then used MELT and CloudMELT to perform MEI discovery in 57,919 human genomes and exomes, leading to the discovery of 104,350 nonredundant MEIs. We leveraged this collection (1) to examine potentially active L1 source elements that drive the mobilization of new Alu, L1, and SVA MEIs in humans; (2) to examine the population distributions and subfamilies of these MEIs; and (3) to examine the mutagenesis of GENCODE genes, ENCODE-annotated features, and disease genes by these MEIs. Our study provides new insights on the L1 source elements that drive MEI mutagenesis and brings forth a better understanding of how this mutagenesis impacts human genomes.

Projecting the effects of land subsidence and sea level rise on storm surge flooding in Coastal North Carolina

Much of the United States Atlantic coastline continues to undergo subsidence due to post glacial settlement and ground water depletion. Combined with eustatic sea level rise (SLR), this contributes to an increased rate of relative SLR. In this work, we utilize the ADvanced CIRCulation model to project storm surges across coastal North Carolina. Recent hurricanes Irene and Matthew are simulated considering SLR and subsidence estimates for 2100. Relative to present day conditions, storm surge susceptible regions increase by 27% (Irene) to 40% (Matthew) due to subsidence. Combined with SLR (+ 74 cm), results suggest more than a doubling of areal flood extent for Irene and more than a three-fold increase for Hurricane Matthew. Considering current regional population distributions, this translates to an increase in at-risk populations of 18% to 61% due to subsidence. Even further, exposed populations are projected to swell relative to Matthew and Irene baseline simulations (8200 and 28,500) by more than 70,000 in all SLR scenarios (79,400 to 133,600). While increases in surge inundation are driven primarily by SLR in the region, there remains a substantial contribution due to vertical land movement. This outlines the importance of exploring spatially variable land movement in surge prediction, independent of SLR.

Evaluating the Ability to Use Contextual Features Derived from Multi-Scale Satellite Imagery to Map Spatial Patterns of Urban Attributes and Population Distributions

With an increasing global population, accurate and timely population counts are essential for urban planning and disaster management. Previous research using contextual features, using mainly very-high-spatial-resolution imagery (<2 m spatial resolution) at subnational to city scales, has found strong correlations with population and poverty. Contextual features can be defined as the statistical quantification of edge patterns, pixel groups, gaps, textures, and the raw spectral signatures calculated over groups of pixels or neighborhoods. While they correlated with population and poverty, which components of the human-modified landscape were captured by the contextual features have not been investigated. Additionally, previous research has focused on more costly, less frequently acquired very-high-spatial-resolution imagery. Therefore, contextual features from both very-high-spatial-resolution imagery and lower-spatial-resolution Sentinel-2 (10 m pixels) imagery in Sri Lanka, Belize, and Accra, Ghana were calculated, and those outputs were correlated with OpenStreetMap building and road metrics. These relationships were compared to determine what components of the human-modified landscape the features capture, and how spatial resolution and location impact the predictive power of these relationships. The results suggest that contextual features can map urban attributes well, with out-of-sample R2 values up to 93%. Moreover, the degradation of spatial resolution did not significantly reduce the results, and for some urban attributes, the results actually improved. Based on these results, the ability of the lower resolution Sentinel-2 data to predict the population density of the smallest census units available was then assessed. The findings indicate that Sentinel-2 contextual features explained up to 84% of the out-of-sample variation for population density.

Novel Post-Glacial Haplotype Evolution in Birch—A Case for Conserving Local Adaptation

Despite constituting the western-most edge of the population distributions for several native European plants, Ireland has largely been left out of key Europe-wide phylogeographic studies. This is true for birch (Betula pubescens Ehrh. and Betula pendula Roth), for which the genetic diversity has yet to be mapped for Ireland. Here we used eight cpDNA markers (two Restriction Fragment Length Polymorphism (RFLP) and six Simple Sequence Repeat (SSR)) to map the genetic diversity of B. pubescens, B. pendula, and putative hybrid individuals sampled from 19 populations spread cross most of the island of Ireland. Within Ireland, 11 distinct haplotypes were detected, the most common of which (H1) was also detected in England, Scotland, France, and Norway. A moderate level of population structuring (GST = 0.282) was found across Ireland and the genetic diversity of its northern populations was twice that of its southern populations. This indicates that, unlike other native Irish trees, such as oak and alder, post-glacial recolonization by birch did not begin in the south (i.e., from Iberia). Rather, and in agreement with palynological data, birch most likely migrated in from eastern populations in Britain. Finally, we highlight Irish populations with comparatively unique genetic structure which may be included as part of European genetic conservation networks.

Conservation Actions in Multi-Species Systems: Species Interactions and Dispersal Costs

We use the General Equilibrium Ecosystem Model (GEEM) parameterized to Wyoming sagebrush to explore the impact of two common simplifications in bio-economic policy frameworks on species conservation decisions. First, we compare conservation policies based on 2-species food web models to those based on a more complex food web. We find that using the simpler model can miss opportunities for more conservation benefits in the presence of species interactions. Second, we define the impact of species dispersal costs on population distributions in a heterogenous landscape and explore conservation policies to reduce those costs to enable species to move away from disturbed areas. Conservation actions that reduce dispersal costs for all species reflect species interactions and thresholds that determine which species disperse.

942Global Indigenous and Tribal Peoples Data Governance in Health Research: A systematic review

Background Despite a broad range of research using Indigenous specific data, there is limited research to describe what constitutes data governance. To identify priorities and monitor progress in health, data is critical. Accurate collection and appropriate analysis of population level data is necessary in understanding population distributions of health and wellbeing. The aim of this review is to provide a comprehensive understanding of the current literature describing Indigenous Data Governance (IDG) processes in health research. Methods A comprehensive a-priori search strategy has been developed and submitted for registration through PROSPERO(APP170033). Literature will be sourced from bibliographic databases, review articles, key journals, conference proceedings, grey literature and referrals by key experts up until 01/2020 and synthesised (through meta-study and meta-aggregation approaches) in accordance with PRISMA guidelines. Results Once completed, a descriptive overview along with discussion on IDG processes in decision making specific to health research will be synthesized. IDG 'interventions' may include, but are not limited to, community advisory committees, Indigenous leadership, or institutional and/or project policies pertaining to decision making processes in the use of data in health research. Conclusions This review will provide evidence of definitions and procedures specific to IDG in the literature and how IDG, as a process, is operationalised in Indigenous health research across the globe. Key messages Preliminary Key messages include the lack of IDG processes described in the literature and the need for the development of guidelines to support researchers in operationalising Indigenous Data Sovereignty.