The biological sciences community is increasingly recognizing the value of open, reproducible and transparent research practices for science and society at large. Despite this recognition, many researchers fail to share their data and code publicly. This pattern may arise from knowledge barriers about how to archive data and code, concerns about its reuse, and misaligned career incentives. Here, we define, categorize and discuss barriers to data and code sharing that are relevant to many research fields. We explore how real and perceived barriers might be overcome or reframed in the light of the benefits relative to costs. By elucidating these barriers and the contexts in which they arise, we can take steps to mitigate them and align our actions with the goals of open science, both as individual scientists and as a scientific community.
Ecosystems globally are under threat from ongoing anthropogenic environmental change. Effective conservation management requires more thorough biodiversity surveys that can reveal system‐level patterns and that can be applied rapidly across space and time. Using modern ecological models and community science, we integrate environmental DNA and Earth observations to produce a time snapshot of regional biodiversity patterns and provide multi‐scalar community‐level characterization. We collected 278 samples in spring 2017 from coastal, shrub, and lowland forest sites in California, a complex ecosystem and biodiversity hotspot. We recovered 16,118 taxonomic entries from eDNA analyses and compiled associated traditional observations and environmental data to assess how well they predicted alpha, beta, and zeta diversity. We found that local habitat classification was diagnostic of community composition and distinct communities and organisms in different kingdoms are predicted by different environmental variables. Nonetheless, gradient forest models of 915 families recovered by eDNA analysis and using BIOCLIM variables, Sentinel‐2 satellite data, human impact, and topographical features as predictors, explained 35% of the variance in community turnover. Elevation, sand percentage, and photosynthetic activities (NDVI32) were the top predictors. In addition to this signal of environmental filtering, we found a positive relationship between environmentally predicted families and their numbers of biotic interactions, suggesting environmental change could have a disproportionate effect on community networks. Together, these analyses show that coupling eDNA with environmental predictors including remote sensing data has capacity to test proposed Essential Biodiversity Variables and create new landscape biodiversity baselines that span the tree of life.
A Biodiversity Composition Map of California Derived from Environmental DNA 33 Metabarcoding and Earth Observation 34Abstract 35 Unique ecosystems globally are under threat from ongoing anthropogenic environmental 36 change. Effective conservation management requires more thorough biodiversity surveys that 37can reveal system-level patterns and that can be applied rapidly across space and time. We offer 38 a way to use environmental DNA, community science and remote sensing together as methods to 39 reduce the discrepancy between the magnitude of change and historical approaches to measure it. 40Taking advantages of modern ecological models, we integrate environmental DNA and Earth 41 observations to evaluate regional biodiversity patterns for a snapshot of time, and provide critical 42 community-level characterization. We collected 278 samples in Spring 2017 from coastal, shrub 43 and lowland forest sites in California, a large-scale biodiversity hotspot. We applied gradient 44 forest to model 915 family occurrences and community composition together with environmental 45 variables and multi-scalar habitat classifications to produce a statewide biodiversity-based map. 46 16,118 taxonomic entries recovered were associated with environmental variables to test their 47 predictive strength on alpha, beta, and zeta diversity. Local habitat classification was diagnostic 48 of community composition, illuminating a characteristic of biodiversity hotspots. Using gradient 49 forest models, environmental variables predicted 35% of the variance in eDNA patterns at the 50 family level, with elevation, sand percentage, and greenness (NDVI32) as the top predictors. 51This predictive power was higher than we found in published literature at global scale. In 52 addition to this indication of substantial environmental filtering, we also found a positive 53 relationship between environmentally predicted families and their numbers of biotic interactions. 54In aggregate, these analyses showed that strong eDNA community-environment correlation is a 55 4 general characteristic of temperate ecosystems, and may explain why communities easily 56 destabilize under disturbances. Our study provides the first example of integrating citizen science 57 based eDNA with biodiversity mapping across the tree of life, with promises to produce large 58 scale, high resolution assessments that promote a more comprehensive and predictive 59 understanding of the factors that influence biodiversity and enhance its maintenance. 60
The biological sciences community is increasingly recognizing the value of open, reproducible, and transparent research practices for science and society at large. Despite this recognition, many researchers remain reluctant to share their data and code publicly. This hesitation may arise from knowledge barriers about how to archive data and code, concerns about its re-use, and misaligned career incentives. Here, we define, categorise, and discuss barriers to data and code sharing that are relevant to many research fields. We explore how real and perceived barriers might be overcome or reframed in light of the benefits relative to costs. By elucidating these barriers and the contexts in which they arise, we can take steps to mitigate them and align our actions with the goals of open science, both as individual scientists and as a scientific community.
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