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In North America, winters are becoming more variable such that warm and cold extremes are increasingly common. Refugia (in time or space) can reduce the exposure animals experience to extreme temperatures. However, animals must be able to adjust their behavior to capitalize on refugia. Our goal was to identify the behavioral mechanisms that grant access to refugia in time and space, focusing on a northern ungulate (white-tailed deer, Odocoileus virginianus) as a model. We drew upon an extensive camera trap network in Wisconsin, USA, over two winters that experienced both warm and cold extremes. To understand the use of temporal refugia, we modeled deer activity (at daily resolution) during night, dawn, day, and dusk as a function of weather predictors. To understand the use of spatial refugia, we modeled deer activity at camera locations (at daily resolution) as a function of landscape characteristics, weather conditions, and landscape–weather interactions. During anomalously cold temperatures, deer became more diurnal; conversely, on anomalously warm days, deer were more nocturnal. Deer were more active in conifer-dominated landscapes on cold days. Conversely, during warm extremes, deer increased activity in deciduous-dominated landscapes. Finally, deer showed multiple modes of behavioral flexibility (activity in time as well as space) and demonstrated stronger responses to temperature anomalies later in the winter, suggesting that the effects of extreme events are dependent upon their seasonal timing. Behavioral shifts presumably reduce exposure to extremes and may render species more resilient to increasingly variable winter climates. [ABSTRACT FROM AUTHOR]

Airborne imaging spectroscopy data provide soil and vegetation information over relatively large areas at high spatial resolutions (<5 m). We combined airborne hyperspectral data with space‐borne data (LiDAR DEM and Sentinel‐1) to map soil properties and investigate the contributions of the different sensor data to the mapping accuracy. The study was conducted on a 330‐ha farm in south‐central Wisconsin, USA, where soils are relatively young and soil variation is high. Seventy‐three soil samples (0–10 cm depth) were taken from cropped fields before planting. The soil data were used with remote sensing data for mapping clay, silt, sand, total carbon (TC), Mg, Al, Si, Fe, Ca, Ti, Mn and Zr. Three types of variables were compared: (a) DEM + Sentinel‐1 & 2, as these are easy‐to‐obtain, (b) hyperspectral data with high‐spatial resolution, and (c) hyperspectral data + DEM + Sentinel‐1, to evaluate if the prediction can be improved by combining hyperspectral data with DEM + Sentinel‐1, and if combining DEM + Sentinel‐1 with hyperspectral data had higher prediction accuracy than combination with Sentinel‐2 data. The partial least square regression (PLSR) model was used for establishing relationships between soil and remote sensing data. It was found that airborne hyperspectral imaging can accurately map the spatial distributions of soil clay content, Si and Fe concentrations. Combining hyperspectral data with DEM and Sentinel‐1 improved the performance of models for mapping a range of soil properties (e.g., clay, silt, sand, Al, Ti, Mn and Zr). Total C, Mg and Ca concentrations cannot be predicted from the combination of hyperspectral data, Sentinel‐1 and terrain attributes. In a highly heterogeneous landscape, surface soil properties can be accurately mapped combining LiDAR DEM, Sentinel‐1 and hyperspectral data. Highlights: Airborne imaging spectroscopy, DEM and Sentinel‐1 were used to map surface soil properties.Hyperspectral data can accurately map clay, Si and Fe.Combining hyperspectral data with DEM, Sentinel‐1 improved prediction accuracy of several soil properties.Total C, Mg and Ca cannot be predicted from hyperspectral data, DEM and Sentinel‐1. [ABSTRACT FROM AUTHOR]

Detection/nondetection data are widely collected by ecologists interested in estimating species distributions, abundances, and phenology, and are often imperfect. Recent model development has focused on accounting for both false‐positive and false‐negative errors given evidence that misclassification is common across many sampling protocols. To date, however, model‐based solutions to false‐positive error have largely addressed occupancy estimation. We describe a generalized model structure that allows investigators to account for false‐positive error in detection/nondetection data across a broad range of ecological parameters and model classes, and demonstrate that previously developed model‐based solutions are special cases of the generalized model. Simulation results demonstrate that estimators for abundance and migratory arrival time ignoring false‐positive error exhibit severe (20–70%) relative bias even when only 5–10% of detections are false positives. Bias increased when false‐positive detections were more likely to occur at sites or within occasions in which true positive detections were unlikely to occur. Models accounting for false‐positive error following the site‐confirmation or observation‐confirmation designs generally reduced bias substantially, even when few detections were confirmed as true or false positives or when the process model for false‐positive error was misspecified. Results from an empirical example focusing on gray fox (Urocyon cinereoargenteus) abundance in Wisconsin, USA reinforce concerns that biases induced by false‐positive error can also distort spatial predictions often used to guide decision making. Model sensitivity to false‐positive error extends well beyond occupancy estimation, but encouragingly, model‐based solutions developed for occupancy estimators are generalizable and effective across a range of models widely used in ecological research. [ABSTRACT FROM AUTHOR]

Questions: Spatial characteristics of ecotones can affect ecotone function and define their role in ecological systems. Identifying ecotone location is necessary to quantify the relationship between spatial characteristics and function, yet there is no standard approach, particularly for compositionally complex ecotones where binary classification for the two adjacent systems is difficult to assign. We tested a new approach for identifying ecotone location based on the co‐occurrence property, which recognizes ecotones as areas where species from adjacent systems co‐occur. We assessed whether the approach can objectively identify the location of the Tension Zone in Wisconsin. Location: Wisconsin, USA. Methods: Ecotone Co‐occurrence Mapping (ECoMap) is a multi‐step approach: (a) indicator species analysis identifies representative species of two adjacent systems; (b) a ratio map constructed from the spatial densities of the indicator species identifies high co‐occurrence areas; and (c) contour lines encapsulate highest co‐occurrence areas. We applied this approach to identify the Tension Zone location using point locations of witness trees from the mid‐1800s Public Land Survey. We assessed the approach by examining results of multiple permutations, testing sensitivity to different initial boundary configurations, and comparing it to a subjectively derived map, the Curtis Tension Zone. Results: The location of the Tension Zone was identified consistently in multiple permutations, though there is minor variability in certainty of final ecotone location. Ecotone width varied substantially along the Tension Zone. The location was similar to the Curtis Tension Zone but slightly farther north and more spatially detailed. Sensitivity to initial boundary configuration was low. Conclusions: The ECoMap approach was robust and identified a spatially detailed Tension Zone location. This approach is broadly applicable and can be used to identify the location of simple or compositionally complex ecotones where spatial data of species occurrences and modest prior knowledge of general boundary location are available. [ABSTRACT FROM AUTHOR]

Increases in natural or noncrop habitat surrounding agricultural fields have been shown to be correlated with declines in insect crop pests. However, these patterns are highly variable across studies suggesting other important factors, such as abiotic drivers, which are rarely included in landscape models, may also contribute to variability in insect population abundance. The objective of this study was to explicitly account for the contribution of temperature and precipitation, in addition to landscape composition, on the abundance of a widespread insect crop pest, the soybean aphid ( Aphis glycines Matsumura), in Wisconsin soybean fields. We hypothesized that higher soybean aphid abundance would be associated with higher heat accumulation (e.g., growing degree days) and increasing noncrop habitat in the surrounding landscape, due to the presence of the overwintering primary hosts of soybean aphid. To evaluate these hypotheses, we used an ecoinformatics approach that relied on a large dataset collected across Wisconsin over a 9-year period (2003-2011), for an average of 235 sites per year ( n = 2,110 fields total). We determined surrounding landscape composition (1.5-km radius) using publicly available satellite-derived land cover imagery and interpolated daily temperature and precipitation information from the National Weather Service COOP weather station network. We constructed linear mixed models for soybean aphid abundance based on abiotic and landscape explanatory variables and applied model averaging for prediction using an information theoretic framework. Over this broad spatial and temporal extent in Wisconsin, we found that variation in growing season precipitation was positively related to soybean aphid abundance, while higher precipitation during the nongrowing season had a negative effect on aphid populations. Additionally, we found that aphid populations were higher in areas with proportionally more forest but were lower in areas where minor crops, such as small grains, were more prevalent. Thus, our findings support our hypothesis that including abiotic drivers increases our understanding of crop pest abundance and distribution. Moreover, by explicitly modeling abiotic factors, we may be able to explore how variable climate in tandem with land cover patterns may affect current and future insect populations, with potentially critical implications for crop yields and agricultural food webs. [ABSTRACT FROM AUTHOR]

Foliar biochemical traits are important indicators of ecosystem functioning and health that are impractical to characterize at large spatial and temporal scales using traditional measurements. However, comprehensive inventories of foliar traits are important for understanding ecosystem responses to anthropogenic and natural disturbances, as inputs into ecosystem process models, and for quantifying spatial variation in functional diversity. Imaging spectroscopy has been demonstrated as a valuable tool for developing maps of ecologically important foliar traits at large scales, but its application to mapping foliar traits over the course of the growing season has been limited. We collected high-resolution imaging spectroscopy data over Blackhawk Island, Wisconsin, USA at eight time points during the 2018 growing season (May – October). Using partial least squares regression (PLSR) we developed predictive models applicable to all dates to produce canopy-level maps of eight traits related to ecophysiological function: chlorophyll content, leaf mass per area and concentrations of calcium, nitrogen, phosphorus, potassium, phenolics and lignin. The accuracy of our models varied across traits (R2: 0.25–0.86); traits with well-defined absorption features were retrieved with high accuracy including chlorophyll (R2: 0.86; %RMSE: 11.0) and total phenolics (R2: 0.86; %RMSE: 11.0). We also assessed how well our models estimated biochemistry on novel species and new dates using a cross-validation analysis. Chlorophyll and total phenolics were well estimated across withheld dates and species, whereas calcium was estimated poorly on both withheld species (R2: 0.08) and dates (R2: 0.07). Our canopy-level maps of macronutrients (N, P and K) showed general trends of decreasing concentration over the course of the year, reflecting dilution by carbon-rich compounds during the growing season and resorption during senescence. Conversely, recalcitrant compounds including lignin and calcium increased until late summer, after which they stabilized. These results demonstrate the potential of current and proposed spaceborne imaging spectroscopy missions for mapping seasonal patterns in foliar biochemistry at a global scale. • Seasonal dynamics of eight biochemicals were mapped in a temperate deciduous forest. • Biochemicals with well-defined absorption features were best estimated. • Seasonal patterns varied between foliar biochemicals and across species. [ABSTRACT FROM AUTHOR]

Protecting the future of forests relies on our ability to observe changes in forest health. Thus, developing tools for sensing diseases in a timely fashion is critical for managing threats at broad scales. Oak wilt —a disease caused by a pathogenic fungus (Bretziella fagacearum)— is threatening oaks, killing thousands yearly while negatively impacting the ecosystem services they provide. Here we propose a novel workflow for mapping oak wilt by targeting temporal disease progression through symptoms using land surface phenology (LSP) from spaceborne observations. By doing so, we hypothesize that phenological changes in pigments and photosynthetic activity of trees affected by oak wilt can be tracked using LSP metrics derived from the Chlorophyll/Carotenoid Index (CCI). We used dense time-series observations from Sentinel-2 to create Analysis Ready Data across Minnesota and Wisconsin and to derive three LSP metrics: the value of CCI at the start and end of the growing season, and the coefficient of variation of the CCI during the growing season. We integrate high-resolution airborne imagery in multiple locations to select pixels (n = 3872) from the most common oak tree health conditions: healthy, symptomatic for oak wilt, and dead. These pixels were used to train an iterative Partial Least Square Discriminant (PLSD) model and derive the probability of an oak tree (i.e., pixel) in one of these conditions and the associated uncertainty. We assessed these models spatially and temporally on testing datasets revealing that it is feasible to discriminate among the three health conditions with overall accuracy between 80 and 82%. Within conditions, our models suggest that spatial variations among three CCI-derived LSP metrics can identify healthy (Area Under the Curve (AUC) = 0.98), symptomatic (AUC = 0.89), and dead (AUC = 0.94) oak trees with low false positive rates. The model performance was robust across different years as well. The predictive maps were used to guide local stakeholders to locate disease hotspots for ground verification and subsequent decision-making for treatment. Our results highlight the capabilities of LSP metrics from dense spaceborne observations to map diseases and to monitor large-scale change in biodiversity. • Oak wilt is a lethal disease that is impacting oak forests of the eastern USA. • We assessed disease detection targeting phenological metrics derived from satellites. • Predictive models were developed and assessed spatially and temporally. • Our results demonstrate that it is feasible to map oak wilt from space. • Our predicted maps can guide local stakeholders in locating hotspots of the disease. [ABSTRACT FROM AUTHOR]

Maps of urban tree canopy are essential to estimate the magnitude and spatial distribution of ecosystem services, and to determine who benefits from them. Our objective was to develop a repeatable method to map urban tree canopy cover for the state of Wisconsin. We compared two types of imagery (pan-sharpened 1.5 m SPOT-6 and 1 m NAIP aerial photography), three classification algorithms, seven segment sizes ranging from single pixels to 105 m2, three levels of compactness for each segment size, and sixteen subsets of features. NAIP outperformed SPOT. On average across classification algorithms there were no significant differences in map agreement. Pixel-based maps with convolution features performed as well as the best segment-based maps. The best segment size tested was 60 m2 on average, but there was also a local maximum around 15 m2, which suggests a large range of possible segment sizes must be tested to find the best. The model chosen for application to the whole state was a pixel-level Random Forest classifier with 160 features, and the final state-wide map has five classes: tree/woody vegetation, grass/herbaceous vegetation, impervious surfaces/bare soil, water, and non-forested wetland. Overall accuracy for our state-wide map at the pixel-scale was 79.3% (95% CI: 77.5–81%). Errors occurred due to meter-scale heterogeneity in the urban environment which increases the errors due to spatial misregistration and the number of difficult-to-classify mixed and edge pixels. At larger management level scales, mean absolute error (MAE) decreases to about 10% at the Landsat pixel scale and about 6% at the scale of a city block. Our work is comparable to past efforts to map urban tree canopy, and the open source software and use of imagery that is freely available for all cities in the contiguous US make it broadly applicable. • We mapped urban tree canopy cover for all urban areas in Wisconsin. • Our method is transferable to much of the contiguous US. • NAIP aerial imagery creates better maps than pan-sharpened SPOT-6 imagery. • Pixel-based maps with convolved features perform as well as segment-based maps. • We assess our map at multiple management scales. [ABSTRACT FROM AUTHOR]

Abstract: Efforts to predict water quality often rely heavily on static landuse/land-cover (LULC) classifications derived from remote sensing imagery. However, LULC classifications are infrequently updated, and the development of regular (annual) land cover maps that accurately capture intra- and inter-annual change may be expensive in terms of data and labor costs. In addition, existing land cover products may not include classes relevant to the assessment of water quality. Conversely, data from the Moderate Resolution Imaging Spectroradiometer (MODIS) instruments are freely available, preprocessed, frequently updated, and available in a range of useful data products. Our goal was to predict water quality for streams in Wisconsin, USA using a model constructed exclusively from MODIS products. We used streamwater nitrate and dissolved phosphorus data from 2001 to 2004 from two previous studies (Robertson et al., 2006; Stanley & Maxted, 2008) to evaluate the potential for using MODIS in an empirical model to predict water quality, thereby reducing reliance on possibly dated land cover maps. Using predictors derived exclusively from MODIS data products, we successfully predicted 80% of the variation in measured nitrate concentrations. Remote sensing based models explained 51% of the variation in dissolved phosphorus concentrations. Predictions of water quality were developed on both a per-pixel and a watershed basis. The remote sensing approach offers the potential for spatially continuous estimates of stream nutrient concentrations, and complements the suite of approaches currently used to assess water quality; including periodic stream surveys models, and point-source data. [Copyright &y& Elsevier]

Wildlife managers need reliable information on species distributions (i.e. patterns of occurrence and abundance) to make effective decisions. Historically, managers have relied on harvest records (collected at broad spatial extents but coarse resolution) to monitor wildlife populations. However, emerging citizen-science datastreams can potentially supplement harvest-based monitoring by providing fine-resolution data that permit identification of species-environment relationships needed to predict occurrence and abundance. We combined harvest records and citizen-science camera-trap data in integrated species distribution models (iSDMs) to estimate species-environment relationships and distribution patterns of six wildlife species in Wisconsin, USA. We expected that iSDMs would more precisely estimate species-environment relationships and predict spatial abundance patterns intermediate between camera- and harvest-only SDMs. We also conducted simulations to explore the consequences of incomplete knowledge of harvest effort for estimates of abundance and species-environment relationships. Integrated models produced more precise species-environment relationships than camera-only models in 53% of the relationships we tested; all harvest-only models failed to converge. Moreover, integrated and camera-only models showed low agreement (mean: 19.67%) in identifying abundance "hotspots" but considerably higher agreement (mean: 45.17%) in identifying abundance "cold spots". Our simulations showed that abundance patterns estimated by iSDMs may suffer from imprecision if harvest effort is poorly measured. We recommend that harvest records be collected at finer spatial resolutions and be paired with in-depth effort reporting. Our work demonstrates the potential for integrating an existing datastream (harvest records) with an emerging one (citizen-science camera-trap monitoring) for modeling species distributions and providing support for wildlife management decisions. [ABSTRACT FROM AUTHOR]

• PXRF spectra predict key soil properties within the profiles at depths. • Combination of EMI, Sentinel-2, and DEM maps soil texture, N, and soil depth. • Proximal and remote sensing delineate management zones for decision-making. • Proximal sensing data improve delineation of subsoil characteristics. The potential of a stepwise fusion of proximally sensed portable X-ray fluorescence (pXRF) spectra and electromagnetic induction (EMI) with remote Sentinel-2 bands and a digital elevation model (DEM) was investigated for predicting soil physicochemical properties in pedons and across a heterogeneous 80-ha crop field in Wisconsin, USA. We found that pXRF spectra with partial least squares regression (PLSR) models can predict sand, total nitrogen (TN), organic carbon (OC), silt contents, and clay with validation R2 of 0.81, 0.74, 0.73, 0.68, and 0.64 at the pedon scale but performed less well for soil pH (R2 = 0.51). A combination of EMI, Sentinel-2, and DEM data showed promise in mapping sand, silt contents, and TN at two depths and Ap horizon thickness and soil depth across the field. A clustering analysis using combinations of mapped soil properties or proximal and remote sensing data suggested that data fusion improved the characterization of field-scale variability of soil properties. The cost-benefit analysis showed that the most accurate management zones (MZs) for topsoil can be generated only using estimated soil property maps while it was the most costly as compared to other data sources. For an intermediate-high (for topsoil) and high (subsoil) accuracy and a moderate economic budget, the combination of sensors (proximal + remote sensing + DEM) might be a better approach for effective MZs generation than collecting soil samples for laboratory analysis while the latter produced the most accurate maps for topsoil. It can be concluded that pXRF spectra can be useful for predicting key soil properties (e.g., sand, TN, OC, silt, clay) at different soil depths, and a combination of proximal and remote sensing provides an effective way to delineate soil MZs that are useful for decision-making. [ABSTRACT FROM AUTHOR]

Biological data collection is entering a new era. Community science, satellite remote sensing (SRS), and local forms of remote sensing (e.g., camera traps and acoustic recordings) have enabled biological data to be collected at unprecedented spatial and temporal scales and resolution. There is growing interest in developing observation networks to collect and synthesize data to improve broad-scale ecological monitoring, but no examples of such networks have emerged to inform decision-making by agencies. Here, we present the implementation of one such jurisdictional observation network (JON), Snapshot Wisconsin, which links synoptic environmental data derived from SRS to biodiversity observations collected continuously from a trail camera network to support management decision-making. We use several examples to illustrate that Snapshot Wisconsin improves the spatial, temporal, and biological resolution and extent of information available to support management, filling gaps associated with traditional monitoring and enabling consideration of new management strategies. JONs like Snapshot Wisconsin further strengthen monitoring inference by contributing novel lines of evidence useful for corroboration or integration. SRS provides environmental context that facilitates inference, prediction, and forecasting, and ultimately helps managers formulate, test, and refine conceptual models for the monitored systems. Although these approaches pose challenges, Snapshot Wisconsin demonstrates that expansive observation networks can be tractably managed by agencies to support decision making, providing a powerful new tool for agencies to better achieve their missions and reshape the nature of environmental decision-making., (© 2021 The Authors. Ecological Applications published by Wiley Periodicals LLC on behalf of Ecological Society of America.)