Toward more data publication of long‐term ecological observations

Development of a generalized pseudo-probabilistic approach for characterizing ecological conditions in estuaries using secondary data

Under the best circumstances, achieving or sustaining optimum ecological conditions in estuaries is challenging. Persistent information gaps in estuarine data make it difficult to differentiate natural variability from potential regime shifts. Long-term monitoring is critical for tracking ecological change over time. In the United States (US), many resource management programs are working at maximum capacity to address existing state and federal water quality mandates (e.g., pollutant load limits, climate impact mitigation, and fisheries management) and have little room to expand routine sampling efforts to conduct periodic ecological baseline assessments, especially at state and local scales. Alternative design, monitoring, and assessment approaches are needed to help offset the burden of addressing additional data needs to increase understanding about estuarine system resilience when existing monitoring data are sparse or spatially limited. Research presented here offers a pseudo-probabilistic approach that allows for the use of found or secondary data, such as data on hand and other acquired data, to generate statistically robust characterizations of ecological conditions in estuaries. Our approach uses a generalized pseudo-probabilistic framework to synthesize data from different contributors to inform probabilistic-like baseline assessments. The methodology relies on simple geospatial techniques and existing tools (R package functions) developed for the US Environmental Protection Agency to support ecological monitoring and assessment programs like the National Coastal Condition Assessment. Using secondary estuarine water quality data collected in the Northwest Florida (US) estuaries, demonstrations suggest that the pseudo-probabilistic approach produces estuarine condition assessment results with reasonable statistical confidence, improved spatial representativeness, and value-added information. While the pseudo-probabilistic framework is not a substitute for fully evolved monitoring, it offers a scalable alternative to bridge the gap between limitations in resource management capability and optimal monitoring strategies to track ecological baselines in estuaries over time.

A network simplification approach to ease topological studies about the food-web architecture

Food webs studies are intrinsically complex and time-consuming. Network data about trophic interaction across different large locations and ecosystems are scarce in comparison with general ecological data, especially if we consider terrestrial habitats. Here we present a complex network strategy to ease the gathering of the information by simplifying the collection of data with a taxonomic key. We test how well the topology of three different food webs retain their structure at the resolution of the nodes across distinct levels of simplification, and we estimate how community detection could be impacted by this strategy. The first level of simplification retains most of the general topological indices; betweenness and trophic levels seem to be consistent and robust even at the higher levels of simplification. This result suggests that generalisation and standardisation, as a good practice in food webs science, could benefit the community, both increasing the amount of open data available and the comparison among them, thus providing support especially for scientists that are new in this field and for exploratory analysis.

Reconstructing past thermal conditions in beach microclimates

Reconstruction of past conditions provides important information on how ecosystems have been impacted by climate change, but generally for microhabitats worldwide there are no long-term empirical measurements. In these cases, there has been protracted debate about how various large-scale environmental proxies can best be used to reconstruct local temperatures. Here we help resolve this debate by examining how well environmental proxies hindcast sand temperatures at nest depths for five sea turtle nesting sites across the world. We link instrumental air temperature and sea surface temperature records with empirical sand temperature observations in the Atlantic (Ascension Island and Cape Verde), the Indian Ocean (Chagos Archipelago), the Caribbean (St Eustatius) and the Pacific (French Polynesia). We found strong correlations between sea surface temperatures, air temperatures and sand temperatures at all our study sites. Furthermore, Granger causality testing shows variations in sea surface temperature and air temperature precede variations in sand temperatures. We found that different proxies (air or sea temperature or a combination of both) predicted mean monthly sand temperatures within <0.5°C of empirical observations. Reconstructions of sand temperatures over the last 170 years reveal a slight warming of temperatures (maximum 0.5°C per century). An analysis of 36 published datasets revealed that the gradient of the relationship between sand temperature and air temperature is relatively constant, suggesting long-term changes in sand temperature could be extended around the world to include nesting sites where there are no empirical measurements of sand temperature. Our approaches are likely to have utility for a range of microhabitats where there is an interest in long-term changes in temperature.

Open Data Practices among Users of Primary Biodiversity Data

Presence-only biodiversity data are increasingly relied on in biodiversity, ecology, and conservation research, driven by growing digital infrastructures that support open data sharing and reuse. Recent reviews of open biodiversity data have clearly documented the value of data sharing, but the extent to which the biodiversity research community has adopted open data practices remains unclear. We address this question by reviewing applications of presence-only primary biodiversity data, drawn from a variety of sources beyond open databases, in the indexed literature. We characterize how frequently researchers access open data relative to data from other sources, how often they share newly generated or collated data, and trends in metadata documentation and data citation. Our results indicate that biodiversity research commonly relies on presence-only data that are not openly available and neglects to make such data available. Improved data sharing and documentation will increase the value, reusability, and reproducibility of biodiversity research.