Search for: All records

Ecologists have increasingly recognized opportunities to adapt and adopt methodologies and information originally intended for other purposes in a “data fusion” approach. Recently, there has been an influx of studies and training focused on using unmanned aerial vehicles (UAV’s) and remote sensing in wildlife research. Leveraging these technologies could supplement the often resource-intensive field approaches used to monitor population and habitat dynamics for forest dwelling species such as the snowshoe hare (Lepus americanus). Barriers remain, however, especially as agencies lacking the resources to collect data using UAV’s are restricted to freely available, not wildlife-specific, products. Furthermore, technologies may not be advanced enough to “see through” the canopy to the understory, relevant for species that rely on vegetation cover. We thereby conducted a case study to determine whether an approach outlined by previous authors could be successful, wherein the remote sensing products were accessible and originally collected for broader purposes. Our models did not adequately predict snowshoe hare fecal pellet numbers, pointing to deficiencies in the scale and type of available data derived from remote sensing. We also note potential shortcomings in non-invasive field techniques. Regardless, we maintain that open-access remotely sensed imagery is valuable when ground-truthed and combined with supplemental information, adding to knowledge within and beyond the fields of forestry and wildlife biology. 

Abstract The general public and scientific community alike are abuzz over the release of ChatGPT and GPT-4. Among many concerns being raised about the emergence and widespread use of tools based on large language models (LLMs) is the potential for them to propagate biases and inequities. We hope to open a conversation within the environmental data science community to encourage the circumspect and responsible use of LLMs. Here, we pose a series of questions aimed at fostering discussion and initiating a larger dialogue. To improve literacy on these tools, we provide background information on the LLMs that underpin tools like ChatGPT. We identify key areas in research and teaching in environmental data science where these tools may be applied, and discuss limitations to their use and points of concern. We also discuss ethical considerations surrounding the use of LLMs to ensure that as environmental data scientists, researchers, and instructors, we can make well-considered and informed choices about engagement with these tools. Our goal is to spark forward-looking discussion and research on how as a community we can responsibly integrate generative AI technologies into our work. 

Reproductive character displacement has long been hypothesized to be a key determinant of speciation and co-existence in flowering plants. A central tenet of this hypothesis is that reproductive traits of close relatives growing in sympatry diverge more than they do where close relatives do not grow together. However, this idea remains untested across taxa and at large spatial scales. Here, we use data collected from tens of thousands of herbarium specimens to examine evidence for character displacement in flowering time for 91 closely-related pairs of animal-pollinated angiosperm species in the eastern USA. We see no evidence for overall phenological divergence in sympatry across regions, clades, or life histories. Rather our results indicate widespread convergence of flowering times in sympatry for species pairs that generally tend to flower close in time. We also find that climate change could alter the nature of these convergent flowering events by shifting them further apart in a majority species pair comparisons. Specifically, congeneric species in New England and the Atlantic Coastal Plain are projected to flower 2–4 days further apart, on average, by the mid-21st century as warming temperatures drive species-specific phenological shifts within genera. This may have significant consequences for species interactions and gene flow, especially if current sympatric convergence in flowering times has resulted from facilitative interactions between species. 

Interactions between species can influence access to resources and successful reproduction. One possible outcome of such interactions is reproductive character displacement. Here, the similarity of reproductive traits – such as flowering time – among close relatives growing in sympatry differ more so than when growing apart. However, evidence for the overall prevalence and direction of this phenomenon, or the stability of such differences under environmental change, remains untested across large taxonomic and spatial scales. We apply data from tens of thousands of herbarium specimens to examine character displacement in flowering time across 110 animal-pollinated angiosperm species in the eastern USA. We demonstrate that the degree and direction of phenological displacement among co-occurring closely related species pairs varies tremendously. Overall, flowering time displacement in sympatry is not common. However, displacement is generally greater among species pairs that flower close in time, regardless of direction. We additionally identify that future climate change may alter the nature of phenological displacement among many of these species pairs. On average, flowering times of closely related species were predicted to shift further apart by the mid-21st century, which may have significant future consequences for species interactions and gene flow.Competing Interest StatementThe authors have declared no competing interest. 

Summary Interactions between species can influence successful reproduction, resulting in reproductive character displacement, where the similarity of reproductive traits – such as flowering time – among close relatives growing together differ from when growing apart. Evidence for the overall prevalence and direction of this phenomenon, and its stability under environmental change, remains untested across large scales.Using the power of crowdsourcing, we gathered phenological information from over 40 000 herbarium specimens, and investigated displacement in flowering time across 110 animal‐pollinated species in the eastern USA.Overall, flowering time displacement is not common across large scales. However, displacement is generally greater among species pairs that flower close in time, regardless of direction. Furthermore, with climate change, the flowering times of closely related species are predicted, on average, to shift further apart by the mid‐21^stcentury.We demonstrate that the degree and direction of phenological displacement among co‐occurring closely related species pairs varies tremendously. However, future climate change may alter the differences in reproductive timing among many of these species pairs, which may have significant consequences for species interactions and gene flow. Our study provides one promising path towards understanding how the phenological landscape is structured and may respond to future environmental change. 

Abstract In recent years, the availability of airborne imaging spectroscopy (hyperspectral) data has expanded dramatically. The high spatial and spectral resolution of these data uniquely enable spatially explicit ecological studies including species mapping, assessment of drought mortality and foliar trait distributions. However, we have barely begun to unlock the potential of these data to use direct mapping of vegetation characteristics to infer subsurface properties of the critical zone. To assess their utility for Earth systems research, imaging spectroscopy data acquisitions require integration with large, coincident ground‐based datasets collected by experts in ecology and environmental and Earth science. Without coordinated, well‐planned field campaigns, potential knowledge leveraged from advanced airborne data collections could be lost. Despite the growing importance of this field, documented methods to couple such a wide variety of disciplines remain sparse.We coordinated the first National Ecological Observatory Network Airborne Observation Platform (AOP) survey performed outside of their core sites, which took place in the Upper East River watershed, Colorado. Extensive planning for sample tracking and organization allowed field and flight teams to update the ground‐based sampling strategy daily. This enabled collection of an extensive set of physical samples to support a wide range of ecological, microbiological, biogeochemical and hydrological studies.We present a framework for integrating airborne and field campaigns to obtain high‐quality data for foliar trait prediction and document an archive of coincident physical samples collected to support a systems approach to ecological research in the critical zone. This detailed methodological account provides an example of how a multi‐disciplinary and multi‐institutional team can coordinate to maximize knowledge gained from an airborne survey, an approach that could be extended to other studies.The coordination of imaging spectroscopy surveys with appropriately timed and extensive field surveys, along with high‐quality processing of these data, presents a unique opportunity to reveal new insights into the structure and dynamics of the critical zone. To our knowledge, this level of co‐aligned sampling has never been undertaken in tandem with AOP surveys and subsequent studies utilizing this archive will shed considerable light on the breadth of applications for which imaging spectroscopy data can be leveraged.