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Overwintering monarch (Danaus plexippus) populations have declined since the 1990s. In response, restoration of milkweeds, including Asclepias syriaca (common milkweed), an important host plant in their breeding grounds, has become increasingly common. However, latitudinal variation in milkweed populations suggests the possibility of regional adaptation and the potential for seed provenance to affect restoration success. Using seeds from 20 populations throughout the range of A. syriaca, we tested whether seed mass, germination success, and germination time in the greenhouse demonstrate geographic clines consistent with available evidence for this species from other studies. In addition, we tested for patterns in germination traits consistent with adaptation to spring thermal conditions by planting seeds from 10 populations in growth chambers simulating Minnesota and Kentucky spring temperatures. Even after accounting for seed mass, seeds from higher latitudes germinated faster on average under all conditions. Elevated temperatures accelerated germination time and leaf development time; however, we did not detect geographic patterns in leaf development time, indicating that the processes underlying the latitudinal cline in germination time may be unique to the germination stage. In the thermal adaptation study, high-latitude populations produced larger seeds and seeds that germinated at a higher rate; however, neither latitudinal trend was observed in the geographic clines study, even though individual seed mass predicted germination success. High-latitude populations express more favorable germination traits in every setting measured, perhaps due to reduced dormancy. Consequently, we conclude that latitudinal clines are more consistent with adaptation to growing season length than to spring temperatures. 

Background Understanding how study design and monitoring strategies shape inference within, and synthesis across, studies is critical across biological disciplines. Many biological and field studies are short term and limited in scope. Monitoring studies are critical for informing public health about potential vectors of concern, such as Ixodes scapularis (black-legged ticks). Black-legged ticks are a taxon of ecological and human health concern due to their status as primary vectors of Borrelia burgdorferi , the bacteria that transmits Lyme disease. However, variation in black-legged tick monitoring, and gaps in data, are currently considered major barriers to understanding population trends and in turn, predicting Lyme disease risk. To understand how variable methodology in black-legged tick studies may influence which population patterns researchers find, we conducted a data synthesis experiment. Materials and Methods We searched for publicly available black-legged tick abundance dataset that had at least 9 years of data, using keywords about ticks in internet search engines, literature databases, data repositories and public health websites. Our analysis included 289 datasets from seven surveys from locations in the US, ranging in length from 9 to 24 years. We used a moving window analysis, a non-random resampling approach, to investigate the temporal stability of black-legged tick population trajectories across the US. We then used t-tests to assess differences in stability time across different study parameters. Results All of our sampled datasets required 4 or more years to reach stability. We also found several study factors can have an impact on the likelihood of a study reaching stability and of data leading to misleading results if the study does not reach stability. Specifically, datasets collected via dragging reached stability significantly faster than data collected via opportunistic sampling. Datasets that sampled larva reached stability significantly later than those that sampled adults or nymphs. Additionally, datasets collected at the broadest spatial scale (county) reached stability fastest. Conclusion We used 289 datasets from seven long term black-legged tick studies to conduct a non-random data resampling experiment, revealing that sampling design does shape inferences in black-legged tick population trajectories and how many years it takes to find stable patterns. Specifically, our results show the importance of study length, sampling technique, life stage, and geographic scope in understanding black-legged tick populations, in the absence of standardized surveillance methods. Current public health efforts based on existing black-legged tick datasets must take monitoring study parameters into account, to better understand if and how to use monitoring data to inform decisioning. We also advocate that potential future forecasting initiatives consider these parameters when projecting future black-legged tick population trends. 

Two organizations found ways to be more intentional about encouraging participation by a diverse spectrum of attendees at scientific meetings—the scientific community can learn from their experiences. 

Macrosystem‐scale research is supported by many ecological networks of people, infrastructure, and data. However, no network is sufficient to address all macrosystems ecology research questions, and there is much to be gained by conducting research and sharing resources across multiple networks. Unfortunately, conducting macrosystem research across networks is challenging due to the diversity of expertise and skills required, as well as issues related to data discoverability, veracity, and interoperability. The ecological and environmental science community could substantially benefit from networking existing networks to leverage past research investments and spur new collaborations. Here, we describe the need for a “network of networks” (NoN) approach to macrosystems ecological research and articulate both the challenges and potential benefits associated with such an effort. We describe the challenges brought by rapid increases in the volume, velocity, and variety of “big data” ecology and highlight how a NoN could build on the successes and creativity within component networks, while also recognizing and improving upon past failures. We argue that a NoN approach requires careful planning to ensure that it is accessible and inclusive, incorporates multimodal communications and ways to interact, supports the creation, testing, and promulgation of community standards, and ensures individuals and groups receive appropriate credit for their contributions. Additionally, a NoN must recognize important trade‐offs in network architecture, including how the degree of centralization of people, infrastructure, and data influence network scalability and creativity. If implemented carefully and thoughtfully, a NoN has the potential to substantially advance our understanding of ecological processes, characteristics, and trajectories across broad spatial and temporal scales in an efficient, inclusive, and equitable manner.

 

The COVID‐19 pandemic significantly impacted undergraduate education and fundamentally altered the structure of course delivery in higher education. In field‐based biology and ecology courses, where instructors and students typically work collaboratively and in‐person to collect data, this has been particularly challenging. In this context, faculty from the Ecological Research as Education Network (EREN) collaborated with the National Ecological Observatory Network (NEON) to design five free‐flexible learning projects for use by instructors in varied modalities (e.g., socially distanced in‐person, remote, or HyFlex). The five flexible learning projects incorporated the Ecological Society of America’s 4DEE framework and included field data collection, data analysis components, and an activity that incorporates existing NEON field protocols or datasets. Each project was designed to provide faculty members with a high degree of flexibility so that they could tailor the implementation of the projects to fit course‐specific needs. Collectively, these learning projects were designed to be flexible, inclusive, and facilitate hands‐on research while working in alternative classroom settings.

 

It is a critical time to reflect on the National Ecological Observatory Network (NEON) science to date as well as envision what research can be done right now with NEON (and other) data and what training is needed to enable a diverse user community. NEON became fully operational in May 2019 and has pivoted from planning and construction to operation and maintenance. In this overview, the history of and foundational thinking around NEON are discussed. A framework of open science is described with a discussion of how NEON can be situated as part of a larger data constellation—across existing networks and different suites of ecological measurements and sensors. Next, a synthesis of early NEON science, based on >100 existing publications, funded proposal efforts, and emergent science at the very first NEON Science Summit (hosted by Earth Lab at the University of Colorado Boulder in October 2019) is provided. Key questions that the ecology community will address with NEON data in the next 10 yr are outlined, from understanding drivers of biodiversity across spatial and temporal scales to defining complex feedback mechanisms in human–environmental systems. Last, the essential elements needed to engage and support a diverse and inclusive NEON user community are highlighted: training resources and tools that are openly available, funding for broad community engagement initiatives, and a mechanism to share and advertise those opportunities. NEON users require both the skills to work with NEON data and the ecological or environmental science domain knowledge to understand and interpret them. This paper synthesizes early directions in the community’s use of NEON data, and opportunities for the next 10 yr of NEON operations in emergent science themes, open science best practices, education and training, and community building.