Ecological Observatory Research Articles

Estimating Individual Tree Mortality in the Sierra Nevada Using Lidar and Multispectral Reflectance Data

AbstractWidespread tree mortality events occur during periods of severe drought in temperate conifer forests and are expected to become more frequent in many areas due to climate change. Improved mapping of individual tree mortality is needed to identify risk factors and design effective conservation strategies. In this study, we used National Ecological Observatory Network (NEON) lidar and multispectral reflectance airborne observations to map individual tree mortality over a 160 km2 area during and after the 2012–2016 drought for two sites in California's Sierra National Forest. We used NEON lidar to derive tree locations and crown perimeters and multispectral data to map tree mortality for more than 1 million trees. We found that 25.4% of the trees in our study area died between 2013 and 2017, with considerably higher mortality at the lower‐elevation Soaproot Saddle site. Between 2017 and 2019, an additional 2.0%–2.8% of the trees died each year. Two wildfires in 2020 and 2021 increased tree mortality within burned area perimeters by 49%–89% between 2019 and 2021. Consistent with previous work, we found that tree mortality risk increased as a function of tree height. Tree mortality was positively associated with distance from rivers, trees per hectare, and decreasing slope at the lower elevation site. In contrast, increasing slope was positively associated with tree mortality at the higher elevation site. Our approach and dataset provide a means to study the combined effects of drought and wildfire on tree mortality and may improve projections of forest resilience under a changing climate.

Near‐term forecasts of NEON lakes reveal gradients of environmental predictability across the US

The US National Ecological Observatory Network's (NEON's) standardized monitoring program provides an unprecedented opportunity for comparing the predictability of ecosystems. To harness the power of NEON data for examining environmental predictability, we scaled a near‐term, iterative, water temperature forecasting system to all six NEON lakes in the conterminous US. We generated 1‐day‐ahead to 35‐days‐ahead forecasts using a process‐based hydrodynamic model that was updated with observations as they became available. Among lakes, forecasts were more accurate than a null model up to 35‐days‐ahead, with an aggregated 1‐day‐ahead root‐mean square error (RMSE) of 0.61°C and a 35‐days‐ahead RMSE of 2.17°C. Water temperature forecast accuracy was positively associated with lake depth and water clarity, and negatively associated with fetch and catchment size. The results of our analysis suggest that lake characteristics interact with weather to control the predictability of thermal structure. Our work provides some of the first probabilistic forecasts of NEON sites and a framework for examining continental‐scale predictability.

Habitat–trait interactions that control response to climate change: North American ground beetles (Carabidae)

AbstractAimAs one of the most diverse and economically important families on Earth, ground beetles (Carabidae) are viewed as a key barometer of climate change. Recent meta‐analyses provide equivocal evidence on abundance changes of terrestrial insects. Generalizations from traits (e.g., body size, diets, flights) provide insights into understanding community responses, but syntheses for the diverse Carabidae have not yet emerged. We aim to determine how habitat and trait syndromes mediate risks from contemporary and future climate change on the Carabidae community.LocationNorth America.Time period2012–2100.Major taxa studiedGround beetles (Carabidae).MethodsWe synthesized the abundance and trait data for 136 species from the National Ecological Observatory Network (NEON) and additional raw data from studies across North America with remotely sensed habitat characteristics in a generalized joint attribute model. Combined Light Detection and RAnging (LiDAR) and hyperspectral imagery were used to derive habitat at a continental scale. We evaluated climate risks on the joint response of species and traits by expanding climate velocity to response velocity given habitat change.ResultsHabitat contributes more variations in species abundance and community‐weighted mean traits compared to climate. Across North America, grassland fliers benefit from open habitats in hot, dry climates. By contrast, large‐bodied, burrowing omnivores prefer warm‐wet climates beneath closed canopies. Species‐specific abundance changes predicted by the fitted model under future shared socioeconomic pathways (SSP) scenarios are controlled by climate interactions with habitat heterogeneity. For example, the mid‐size, non‐flier is projected to decline across much of the continent, but the magnitudes of declines are reduced or even reversed where canopies are open. Conversely, temperature dominates the response of the small, frequent flier Agonoleptus conjunctus, causing projected change to be more closely linked to regional temperature changes.Main conclusionsCarabidae community reorganization under climate change is being governed by climate–habitat interactions (CHI). Species‐specific responses to CHI are explained by trait syndromes. The fact that habitat mediates warming impacts has immediate application to critical habitat designation for carabid conservation.

Climate, soil mineralogy and mycorrhizal fungi influence soil organic matter fractions in eastern US temperate forests

Abstract Identifying the primary controls of particulate (POM) and mineral‐associated organic matter (MAOM) content in soils is critical for determining future stocks of soil carbon (C) and nitrogen (N) across the globe. However, drivers of these soil organic matter fractions are likely to vary among ecosystems in response to climate, soil type and the composition of local biological communities. We tested how soil factors, climate and plant–fungal associations influenced the distribution and concentrations of C and N in MAOM and POM in seven temperate forests in the National Ecological Observatory Network (NEON) across the eastern United States. Samples of upper mineral horizon soil within each forest were collected in plots representing a gradient of dominant tree–mycorrhizal association, allowing us to test how plant and microbial communities influenced POM and MAOM across sites differing in climate and soil conditions. We found that concentrations of C and N in soil organic matter were primarily driven by soil mineralogy, but the relative abundance of MAOM versus POM C was strongly linked to plot‐level mycorrhizal dominance. Furthermore, the effect of dominant tree mycorrhizal type on the distribution of N among POM and MAOM fractions was sensitive to local climate: in cooler sites, an increasing proportion of ectomycorrhizal‐associated trees was associated with lower proportions of N in MAOM, but in warmer sites, we found the reverse. As an indicator of soil carbon age, we measured radiocarbon in the MAOM fraction but found that within and across sites, Δ14C was unrelated to mycorrhizal dominance, climate, or soil factors, suggesting that additional site‐specific factors may be primary determinants of long‐term SOM persistence. Synthesis. Our results indicate that while soil mineralogy primarily controls SOM C and N concentrations, the distribution of SOM among density fractions depends on the composition of vegetation and microbial communities, with these effects varying across sites with distinct climates. We also suggest that within biomes, the age of mineral‐associated soil carbon is not clearly linked to the factors that control concentrations of MAOM C and N.

Combining local, landscape, and regional geographies to assess plant community vulnerability to invasion impact.

Invasive species science has focused heavily on the invasive agent. However, management to protect native species also requires a proactive approach focused on resident communities and the features affecting their vulnerability to invasion impacts. Vulnerability is likely the result of factors acting across spatial scales, from local to regional, and it is the combined effects of these factors that will determine the magnitude of vulnerability. Here, we introduce an analytical framework that quantifies the scale-dependent impact of biological invasions on native richness from the shape of the native species-area relationship (SAR). We leveraged newly available, biogeographically extensive vegetation data from the U.S. National Ecological Observatory Network to assess plant community vulnerability to invasion impact as a function of factors acting across scales. We analyzed more than 1000 SARs widely distributed across the USA along environmental gradients and under different levels of non-native plant cover. Decreases in native richness were consistently associated with non-native species cover, but native richness was compromised only at relatively high levels of non-native cover. After accounting for variation in baseline ecosystem diversity, net primary productivity, and human modification, ecoregions that were colder and wetter were most vulnerable to losses of native plant species at the local level, while warmer and wetter areas were most susceptible at the landscape level. We also document how the combined effects of cross-scale factors result in a heterogeneous spatial pattern of vulnerability. This pattern could not be predicted by analyses at any single scale, underscoring the importance of accounting for factors acting across scales. Simultaneously assessing differences in vulnerability between distinct plant communities at local, landscape, and regional scales provided outputs that can be used to inform policy and management aimed at reducing vulnerability to the impact of plant invasions.

Accuracy Assessment and Impact Factor Analysis of GEDI Leaf Area Index Product in Temperate Forest

The leaf area index (LAI) is a vital parameter for quantifying the material and energy exchange between terrestrial ecosystems and the atmosphere. The Global Ecosystem Dynamics Investigation (GEDI), with its mission to produce a near-global map of forest structure, provides a product of the effective leaf area index (referred to as GEDI LAIe). However, it is unclear about the performance of GEDI LAIe across different temperate forest types and the degree of factors influencing GEDI LAIe performance. This study assessed the accuracy of GEDI LAIe in temperate forests and quantifies the effects of various factors, such as the difference of gap fraction (DGF) between GEDI and discrete point cloud Lidar of the National Ecological Observatory Network (NEON), sensor system parameters, and characteristics of the canopy, topography, and soil. The reference data for the LAIe assessment were derived from the NEON discrete point cloud Lidar, referred to as NEON Lidar LAIe, covering 12 forest types across 22 sites in the Continental United States (the CONUS). Results showed that GEDI underestimated LAIe (Bias: −0.56 m2/m2), with values of the mean absolute error (MAE), root mean square error (RMSE), percent bias (%Bias), and percent RMSE (%RMSE) of 0.70 m2/m2, 0.89 m2/m2, −0.20, and 0.31, respectively. Among forest types, the underestimation of GEDI LAIe in broadleaf forests and mixed forests was generally greater than that in coniferous forests, which showed a moderate error (%RMSE: 0.33~0.52). Factor analysis indicated that multiple factors explained 52% variance of the GEDI LAIe error, among which the DGF contributed the most with a relative importance of 49.82%, followed by characteristics of canopy and soil with a relative importance of 23.20% and 16.18%, respectively. The DGF was a key pivot for GEDI LAIe error; that is, other factors indirectly influence the GEDI LAIe error by affecting the DGF first. Our findings demonstrated that the GEDI LAIe product has good performance, and the factor analysis is expected to shed some light on further improvements in GEDI LAIe estimation.

Synthesizing forecasts to inform decision‐making and advance ecological theory

Synthesizing forecasts to inform decision‐making and advance ecological theory

Spatial and temporal sampling strategy connecting NEON Terrestrial Observation System protocols

AbstractThe National Ecological Observatory Network Terrestrial Observation System (NEON TOS) produces open‐access data products that allow data users to investigate the impact of change drivers on key “sentinel” taxa and soils. The spatial and temporal sampling strategy that coordinates implementation of these protocols enables integration across TOS products and with products generated by NEON aquatic, remote sensing, and terrestrial instrument subsystems. Here, we illustrate the plots and sampling units that make up the physical foundation of a NEON TOS site, and we describe the scales (subplot, plot, airshed, and site) at which sampling is spatially colocated across protocols and subsystems. We also describe how moderate resolution imaging spectroradiometer‐enhanced vegetation index (MODIS‐EVI) phenology data are used to temporally coordinate TOS sampling within and across years at the continental scale of the observatory. Individually, TOS protocols produce data products that provide insight into populations, communities, and ecosystem processes. Within the spatial and temporal framework that guides cross‐protocol implementation, the ability to draw inference across data products is enhanced. To illustrate this point, we develop an example using R software that links two TOS data products collected with different temporal frequencies at both plot and site spatial scales. A thorough understanding of how TOS protocols are integrated with each other in space and time, and with other NEON subsystems, is necessary to leverage NEON data products to maximum effect. For example, a researcher must understand the spatial and temporal scales at which soil biogeochemistry data, soil microbe biomass data, and plant litter production and chemistry data may be combined to quantify soil nutrient stocks and fluxes across NEON sites. We present clear links among TOS protocols and across NEON subsystems that will enhance the utility of NEON TOS data products for the data user community.

User-focused evaluation of National Ecological Observatory Network streamflow estimates

Accurately estimating stream discharge is crucial for many ecological, biogeochemical, and hydrologic analyses. As of September 2022, The National Ecological Observatory Network (NEON) provided up to 5 years of continuous discharge estimates at 28 streams across the United States. NEON created rating curves at each site in a Bayesian framework, parameterized using hydraulic controls and manual measurements of discharge. Here we evaluate the reliability of these discharge estimates with three approaches. We (1) compared predicted to observed discharge, (2) compared predicted to observed stage, and (3) calculated the proportion of discharge estimates extrapolated beyond field measurements. We considered 1,523 site-months of continuous streamflow predictions published by NEON. Of these, 39% met our highest quality criteria, 11% fell into an intermediate classification, and 50% of site-months were classified as unreliable. We provided diagnostic metrics and categorical evaluations of continuous discharge and stage estimates by month for each site, enabling users to rapidly query for suitable NEON data.

Scale dependency of lidar‐derived forest structural diversity

AbstractLidar‐derived forest structural diversity (FSD) metrics—including measures of forest canopy height, vegetation arrangement, canopy cover (CC), structural complexity and leaf area and density—are increasingly used to describe forest structural characteristics and can be used to infer many ecosystem functions. Despite broad adoption, the importance of spatial resolution (grain and extent) over which these structural metrics are calculated remains largely unconsidered. Often researchers will quantify FSD at the spatial grain size of the process of interest without considering the scale dependency or statistical behaviour of the FSD metric employed.We investigated the appropriate scale of inference for eight lidar‐derived spatial metrics—CC, canopy relief ratio, foliar height diversity, leaf area index, mean and median canopy height, mean outer canopy height, and rugosity (RT)‐‐representing five FSD categories—canopy arrangement, CC, canopy height, leaf area and density, and canopy complexity. Optimal scale was determined using the representative elementary area (REA) concept whereby the REA is the smallest grain size representative of the extent. Structural metrics were calculated at increasing canopy spatial grain (from 5 to 1000 m) from aerial lidar data collected at nine different forested ecosystems including sub‐boreal, broadleaf temperate, needleleaf temperate, dry tropical, woodland and savanna systems, all sites are part of the National Ecological Observatory Network within the conterminous United States. To identify the REA of each FSD metric, we used changepoint analysis via segmented or piecewise regression which identifies significant changepoints for both the magnitude and variance of each metric.We find that using a spatial grain size between 25 and 75 m sufficiently captures the REA of CC, canopy arrangement, canopy leaf area and canopy complexity metrics across multiple forest types and a grain size of 30–150 m captures the REA of canopy height metrics. However, differences were evident among forest types with higher REA necessary to characterize CC in evergreen needleleaf forests, and canopy height in deciduous broadleaved forests.These findings indicate the appropriate range of spatial grain sizes from which inferences can be drawn from this set of FSD metrics, informing the use of lidar‐derived structural metrics for research and management applications.

Understanding Organisms Using Ecological Observatory Networks.

Human activities are rapidly changing ecosystems around the world. These changes have widespread implications for the preservation of biodiversity, agricultural productivity, prevalence of zoonotic diseases, and sociopolitical conflict. To understand and improve the predictive capacity for these and other biological phenomena, some scientists are now relying on observatory networks, which are often composed of systems of sensors, teams of field researchers, and databases of abiotic and biotic measurements across multiple temporal and spatial scales. One well-known example is NEON, the US-based National Ecological Observatory Network. Although NEON and similar networks have informed studies of population, community, and ecosystem ecology for years, they have been minimally used by organismal biologists. NEON provides organismal biologists, in particular those interested in NEON's focal taxa, with an unprecedented opportunity to study phenomena such as range expansions, disease epidemics, invasive species colonization, macrophysiology, and other biological processes that fundamentally involve organismal variation. Here, we use NEON as an exemplar of the promise of observatory networks for understanding the causes and consequences of morphological, behavioral, molecular, and physiological variation among individual organisms.

Patterns and controls of foliar nutrient stoichiometry and flexibility across United States forests.

Plant element stoichiometry and stoichiometric flexibility strongly regulate ecosystem responses to global change. Here, we tested three potential mechanistic drivers (climate, soil nutrients, and plant taxonomy) of both using paired foliar and soil nutrient data from terrestrial forested National Ecological Observatory Network sites across the USA. We found that broad patterns of foliar nitrogen (N) and foliar phosphorus (P) are explained by different mechanisms. Plant taxonomy was an important control over all foliar nutrient stoichiometries and concentrations, especially foliar N, which was dominantly related to taxonomy and did not vary across climate or soil gradients. Despite a lack of site-level correlations between N and environment variables, foliar N exhibited intraspecific flexibility, with numerous species-specific correlations between foliar N and various environmental factors, demonstrating the variable spatial and temporal scales on which foliar chemistry and stoichiometric flexibility can manifest. In addition to plant taxonomy, foliar P and N:P ratios were also linked to soil nutrient status (extractable P) and climate, especially actual evapotranspiration rates. Our findings highlight the myriad factors that influence foliar chemistry and show that broad patterns cannot be explained by a single consistent mechanism. Furthermore, differing controls over foliar N versus P suggests that each may be sensitive to global change drivers on distinct spatial and temporal scales, potentially resulting in altered ecosystem N:P ratios that have implications for processes ranging from productivity to carbon sequestration.

Documenting the impacts of increasing salinity in freshwater and coastal ecosystems: Introduction to the special issue

Documenting the impacts of increasing salinity in freshwater and coastal ecosystems: Introduction to the special issue

Plant‐Soil Relationships Influence Observed Trends Between Manganese and Carbon Across Biomes

AbstractManganese (Mn) is an essential plant micronutrient that plays a critical role in the litter decomposition by oxidizing and degrading complex organic molecules. Previous studies report a negative correlation between Mn concentrations and carbon (C) storage in organic horizons and suggest that high Mn concentrations in leaf litter reduce soil C storage in forest ecosystems, presumably by stimulating the oxidation of lignin by fungal enzymes. Yet, the relationship between Mn and C in the litter layer and organic soil remains poorly understood and restricted to a few biomes, hampering our ability to improve mechanistic understanding of soil C accumulation. To examine plant‐soil interactions that underlie observed relationships between Mn and C across a wide range of biomes, we extracted biogeochemical data reported for plants and soils from the National Ecological Observatory Network (NEON) database. We found that increased C and nitrogen (N) storage in organic horizons were associated with declines in Mn concentrations across diverse ecosystems at the continental scale, and this relationship was associated with the degree of organic matter decomposition (i.e., Oi, Oe, and Oa). Carbon and N stocks were more strongly correlated with Mn than with climatic variables (i.e., temperature and precipitation). Foliar Mn was strongly correlated with foliar lignin, and both these parameters increased with a decrease in soil pH, indicating links between soil pH, foliar chemistry, and litter decomposability. Our observations suggest that increased Mn bioavailability and accumulation in foliage under moderately acidic soil conditions support fungal decomposition of lignin‐rich litter and contributes to lower soil C stocks.

Detecting Woody Plants in Southern Arizona Using Data from the National Ecological Observatory Network (NEON)

Land cover changes and conversions are occurring rapidly in response to human activities throughout the world. Woody plant encroachment (WPE) is a type of land cover conversion that involves the proliferation and/or densification of woody plants in an ecosystem. WPE is especially prevalent in drylands, where subtle changes in precipitation and disturbance regimes can have dramatic effects on vegetation structure and degrade ecosystem functions and services. Accurately determining the distribution of woody plants in drylands is critical for protecting human and natural resources through woody plant management strategies. Using an object-based approach, we have used novel open-source remote sensing and in situ data from Santa Rita Experimental Range (SRER), National Ecological Observatory Network (NEON), Arizona, USA with machine learning algorithms and tested each model’s efficacy for estimating fractional woody cover (FWC) to quantify woody plant extent. Model performance was compared using standard model assessment metrics such as accuracy, sensitivity, specificity, and runtime to assess model variables and hyperparameters. We found that decision tree-based models with a binary classification scheme performed best, with sequential models (Boosting) slightly outperforming independent models (Random Forest) for both object classification and FWC estimates. Mean canopy height and mean, median, and maximum statistics for all vegetation indices were found to have highest variable importance. Optimal model hyperparameters and potential limitations of the NEON dataset for classifying woody plants in dryland regions were also identified. Overall, this study lays the groundwork for developing machine learning models for dryland woody plant management using solely NEON data.

Variability and drivers of CO2, CH4, and N2O concentrations in streams across the United States

AbstractStreams and rivers are major sources of greenhouse gases (GHGs) to the atmosphere, as carbon and nitrogen are converted and outgassed during transport. Although our understanding of drivers of individual GHG fluxes has improved with numerous site‐specific studies and global‐scale compilations, our ability to parse out interrelated physical and biogeochemical drivers of gas concentrations is limited by a lack of consistently collected, temporally continuous samples of GHGs and their associated drivers. We present a first analysis of such a dataset collected by the National Ecological Observatory Network across 27 streams and rivers across ecoclimatic domains of the United States. Average concentrations of CO2 ranged from 36.9 ± 0.88 to 404 ± 33 μmol L−1, CH4 from 0.003 ± 0.0003 to 4.99 ± 0.72 μmol L−1, and N2O from 0.015 to 0.04 μmol L−1 and spanned ranges of previous global compilations. Both CO2 and CH4 were strongly affected by physical drivers including mean air temperature and stream slope, as well as by dissolved oxygen and total nitrogen concentrations. N2O was exclusively correlated with total nitrogen concentrations. Results suggested that potential for gas exchange dominated patterns in gas concentrations at the site level, but contributions of in‐stream aerobic and anaerobic metabolism, and groundwater also likely varied across sites. The highest gas concentrations as well as highest variability occurred in low‐gradient, warmer, and nonperennial systems. These results are a first step in providing unprecedented, continuous estimates of GHG flux constrained by temporally variable physical and biogeochemical drivers of GHG production.

The effect of hurricanes on body size in Puerto Rico fish populations

Hurricanes Irma and Maria were category 5 hurricanes that devastated parts of Central America in September 2017. Hurricane Irma passed northern Puerto Rico on September 6; Maria reached Puerto Rico as a category 4 hurricane on 20 Sep 2017. These hurricanes had devastating effects on the people living in Puerto Rico, but less is known about how the hurricanes impacted Puerto Rico's wildlife. The National Ecological Observatory Network (NEON) provides a large ecological database covering all American states and Puerto Rico. NEON includes comprehensive information about fish species and environmental variables at multiple sites collected at time periods from late 2014 to present. Aquatic sample collection in Puerto Rico has been performed at Rio Guilarte and Rio Cupeyes. Data collection from these two sites occurred around the same time before the hurricanes in February 2017, and also occurred shortly after the hurricanes. Gambusia affinis (western mosquitofish), Xiphophorus hellerii (green swordtail), and Poecilia reticulata (guppy) were collected in Rio Guilarte. Agonostomus monticola (mountain mullet) was collected in Rio Cupeyes. The focus of this study was to determine if the hurricanes had an impact on the mean length of each fish species, which would suggest eco-evolutionary effects of hurricanes on fish populations. The results indicate that while all four species' lengths decreased, on average, only the decreases in green swordtail and mountain mullet were significant. Hurricanes, storms, and floods are happening at increasingly higher severity. This analysis identifies direct biological effects caused by severe short-term ecological disruptions on multiple fish species.

Geography, taxonomy, and ecological guild: Factors impacting freshwater macroinvertebrate gut microbiomes.

Despite their diversity, global distribution, and apparent effects on host biology, the rules of life that govern variation in microbiomes among host species remain unclear, particularly in freshwater organisms. In this study, we sought to assess whether geographic location, taxonomy (order, family, and genus), or functional feeding group (FFG) designations would best explain differences in the gut microbiome composition among macroinvertebrates sampled across 10 National Ecological Observatory Network's (NEON) freshwater stream sites in the United States. Subsequently, we compared the beta diversity of microbiomes among locations, taxonomy (order, family, and genus), and FFGs in a single statistical model to account for variation within the source microbial community and the types of macroinvertebrates sampled across locations. We determined significant differences in community composition among macroinvertebrate orders, families, genera, and FFGs. Differences in microbiome compositions were underscored by different bacterial ASVs that were differentially abundant among variables (four bacterial ASVs across the 10 NEON sites, 43 ASVs among the macroinvertebrate orders, and 18 bacterial ASVs differing among the five FFGs). Analyses of variations in microbiome composition using the Bray-Curtis distance matric revealed FFGs as the dominant source of variation (mean standard deviation of 0.8), followed by stream site (mean standard deviation of 0.5), and finally family and genus (mean standard deviation of 0.3 each). Our findings revealed a principal role for FFG classification in insect gut microbiome beta diversity with additional roles for geographic distribution and taxonomy.

Monitoring small mammal abundance using NEON data: are calibrated indices useful?

Abstract Small mammals are important to the functioning of ecological communities with changes to their abundances used to track impacts of environmental change. While capture–recapture estimates of absolute abundance are preferred, indices of abundance continue to be used in cases of limited sampling, rare species with little data, or unmarked individuals. Improvement to indices can be achieved by calibrating them to absolute abundance but their reliability across years, sites, or species is unclear. To evaluate this, we used the US National Ecological Observatory Network capture–recapture data for 63 small mammal species over 46 sites from 2013 to 2019. We generated 17,155 absolute abundance estimates using capture–recapture analyses and compared these to two standard abundance indices, and three types of calibrated indices. We found that neither raw abundance indices nor index calibrations were reliable approximations of absolute abundance, with raw indices less correlated with absolute abundance than index calibrations (raw indices overall R2 < 0.5, index calibration overall R2 > 0.6). Performance of indices and index calibrations varied by species, with those having higher and less variable capture probabilities performing best. We conclude that indices and index calibration methods should be used with caution with a count of individuals being the best index to use, especially if it can be calibrated with capture probability. None of the indices we tested should be used for comparing different species due to high variation in capture probabilities. Hierarchical models that allow for sharing of capture probabilities over species or plots (i.e., joint-likelihood models) may offer a better solution to mitigate the cost and effort of large-scale small mammal sampling while still providing robust estimates of abundance.

Temperature-habitat interactions constrain seasonal activity in a continental array of pitfall traps.

Activity density (AD), the rate at which animals collectively move through their environment, emerges as the product of a taxon's local abundance and its velocity. We analyze drivers of seasonal AD using 47 localities from the National Ecological Observatory Network (NEON) both to better understand variation in ecosystem rates like pollination and seed dispersal as well as the constraints of using AD to monitor invertebrate populations. AD was measured as volume from biweekly pitfall trap arrays (mltrap-1 14days-1 ). Pooled samples from 2017 to 2018 revealed AD extrema at most temperatures but with a strongly positive overall slope. However, habitat types varied widely in AD's seasonal temperature sensitivity, from negative in wetlands to positive in mixed forest, grassland, and shrub habitats. The temperature of maximum AD varied threefold across the 47 localities; it tracked the threefold geographic variation in maximum growing season temperature with a consistent gap of ca. 3°C across habitats, a novel macroecological result. AD holds potential as an effective proxy for investigating ecosystem rates driven by activity. However, our results suggest that its use for monitoring insect abundance is complicated by the many ways that both abundance and velocity are constrained by a locality's temperature and plant physiognomy.