Broad Spatial Extents Research Articles

High resolution thermal remote sensing and the limits of species' tolerance.

Extinction risks for many insect species, particularly across very broad spatial extents, have been linked to the growing frequency and severity of temperatures that exceed the boundaries of their realized niches. Measurement and mitigation of such impacts is hindered by the availability of high-resolution measurements of species-specific severity of extreme weather, especially temperature. While techniques enabling interpolation of broad-scale remote sensing metrics are vital for such efforts, direct remote sensing measurements of thermal conditions could improve habitat management by providing detailed insights that interpolative approaches cannot. Advances in unmanned aerial vehicle (UAV) technology have created opportunities to better evaluate the role of microclimates in local species extinctions. Here, we develop a method to create high-resolution maps of microclimates using UAV and thermal imaging technology that use species’ realized niche boundaries to assess potential effects of severity of extreme temperatures. We generated air temperature maps (5 cm resolution) and canopy height maps (1 cm resolution) for 15 sites in a rare alvar ecosystem in eastern Ontario. We validated these remote sensing observations against independent, in situ temperature observations using iButtons. Temperature observations were accurate and related to physical heterogeneity in alvar habitats. We converted temperature measures into estimates of proximity of thermal niche boundaries for three butterfly species found during field surveys. This is the first time that this method has been applied to high resolution remote sensing observations and offers potential to assess the availability and adequacy of microclimates within habitats at resolutions relevant for conservation management.

Laserfarm – A high-throughput workflow for generating geospatial data products of ecosystem structure from airborne laser scanning point clouds

Quantifying ecosystem structure is of key importance for ecology, conservation, restoration, and biodiversity monitoring because the diversity, geographic distribution and abundance of animals, plants and other organisms is tightly linked to the physical structure of vegetation and associated microclimates. Light Detection And Ranging (LiDAR) — an active remote sensing technique — can provide detailed and high resolution information on ecosystem structure because the laser pulse emitted from the sensor and its subsequent return signal from the vegetation (leaves, branches, stems) delivers three-dimensional point clouds from which metrics of vegetation structure (e.g. ecosystem height, cover, and structural complexity) can be derived. However, processing 3D LiDAR point clouds into geospatial data products of ecosystem structure remains challenging across broad spatial extents due to the large volume of national or regional point cloud datasets (typically multiple terabytes consisting of hundreds of billions of points). Here, we present a high-throughput workflow called ‘Laserfarm’ enabling the efficient, scalable and distributed processing of multi-terabyte LiDAR point clouds from national and regional airborne laser scanning (ALS) surveys into geospatial data products of ecosystem structure. Laserfarm is a free and open-source, end-to-end workflow which contains modular pipelines for the re-tiling, normalization, feature extraction and rasterization of point cloud information from ALS and other LiDAR surveys. The workflow is designed with horizontal scalability and can be deployed with distributed computing on different infrastructures, e.g. a cluster of virtual machines. We demonstrate the Laserfarm workflow by processing a country-wide multi-terabyte ALS dataset of the Netherlands (covering ∼34,000 km2 with ∼700 billion points and ∼ 16 TB uncompressed LiDAR point clouds) into 25 raster layers at 10 m resolution capturing ecosystem height, cover and structural complexity at a national extent. The Laserfarm workflow, implemented in Python and available as Jupyter Notebooks, is applicable to other LiDAR datasets and enables users to execute automated pipelines for generating consistent and reproducible geospatial data products of ecosystems structure from massive amounts of LiDAR point clouds on distributed computing infrastructures, including cloud computing environments. We provide information on workflow performance (including total CPU times, total wall-time estimates and average CPU times for single files and LiDAR metrics) and discuss how the Laserfarm workflow can be scaled to other LiDAR datasets and computing environments, including remote cloud infrastructures. The Laserfarm workflow allows a broad user community to process massive amounts of LiDAR point clouds for mapping vegetation structure, e.g. for applications in ecology, biodiversity monitoring and ecosystem restoration.

Investigating chemical and microbial functional indicators of nutrient retention capacity in greenhouse stormwater retention ponds in southwestern Ontario, Canada

The tributaries flowing through Leamington, Ontario are unique in the Canadian Lake Erie watershed due to the broad spatial extent of greenhouse operations, which more than doubled in size and density from 2011 to 2022. These greenhouse operations are considered to be potential nutrient point sources with respect to observed nutrient concentrations in tributaries adjacent to greenhouse stormwater retention ponds (GSWPs). Identifying causal factors of nutrient release, whether this be chemical or biological, within these ponds may be critical for mitigating their impact on the watershed and ultimately the receiving waters of Lake Erie. Specifically, phosphorus and nitrogen accumulation in freshwater ponds can contribute to environmental damage proximal to adjacent streams, serving as a potential catalyst for algal blooms and eutrophication. This study compared correlations between the water column N:P stoichiometry, sediment nutrient retention capacity, and drivers of microbial metabolism within GSWP sediments. Correlations between water column TN:TP ratios and sediment nutrient retention capacity were observed, suggesting an interplay between N and P in terms of nutrient limitation. Further, clear shifts were observed in the bacterial metabolic pathways analyzed through metatranscriptomics. Specifically, genes related to nitrogen fixation, nitrification and denitrification, and other metabolic processes involving sulfur and methane showed differential expression depending on the condition of the respective pond (i.e., naturalized wetland vs. dredged, eutrophic pond). Collectively, this research serves to highlight the interconnected role of chemical-biological processes particularly as they relate to significant ecosystem processes such as nutrient loading and retention dynamics in impaired freshwater systems.

Consistent trait-temperature interactions drive butterfly phenology in both incidental and survey data

Data availability limits phenological research at broad temporal and spatial extents. Butterflies are among the few taxa with broad-scale occurrence data, from both incidental reports and formal surveys. Incidental reports have biases that are challenging to address, but structured surveys are often limited seasonally and may not span full flight phenologies. Thus, how these data source compare in phenological analyses is unclear. We modeled butterfly phenology in relation to traits and climate using parallel analyses of incidental and survey data, to explore their shared utility and potential for analytical integration. One workflow aggregated “Pollard” surveys, where sites are visited multiple times per year; the other aggregated incidental data from online portals: iNaturalist and eButterfly. For 40 species, we estimated early (10%) and mid (50%) flight period metrics, and compared the spatiotemporal patterns and drivers of phenology across species and between datasets. For both datasets, inter-annual variability was best explained by temperature, and seasonal emergence was earlier for resident species overwintering at more advanced stages. Other traits related to habitat, feeding, dispersal, and voltinism had mixed or no impacts. Our results suggest that data integration can improve phenological research, and leveraging traits may predict phenology in poorly studied species.

Factors Influencing the Use of Geospatial Technology with LiDAR for Road Design: Case of Malaysia

This study seeks a better understanding of the acceptance of geospatial technology with Light Detention and Ranging (LIDAR) in road design in a developing country, Malaysia. Existing surveying measurement methods to provide quick, accurate, and reliable information are unsuccessful in producing an expected result, especially in large areas. In addition, topographic data cannot be observed well with the conventional total station method in areas under thick canopies, which is challenging to identify road areas at risk to the environment, such as slope failure. Geospatial surveying technology by LiDAR helps in measuring fields over a wide area and provides a broader spatial extent. At the same time, the laser capability of airborne LiDAR, which penetrates the canopy, helps give accurate readings on the terrain. However, the use of LiDAR geospatial technology for use in road design is still insufficient to date. Thus, this study is developed to identify the factors that influence the use of LiDAR in road design among engineers. Factors identified are barriers, motivation, and strategy. Barrier factors consist of lack of knowledge, risk, cost, and human aspects that slow down the development of LiDAR use. On the other hand, motivational factors consist of encouraging engineers to obtain knowledge about LiDAR and to use it more widely. Meanwhile, a strategy factor form increases LiDAR measurement methods through activities or work procedures. The finding shows that barriers and strategy factors are the significant factors that affect the acceptance of LiDAR among engineers. However, motivational factors have no significant effect to engineers in accepting the use of LiDAR. The advantages of this study and its limitations are also discussed. Finally, this study also provides compilation of few suggestions pertaining this topic to improve future research.

Comparison of methods for estimating density and population trends for low-density Asian bears

Populations of bears in Asia are vulnerable to extinction and effective monitoring is critical to measure and direct conservation efforts. Population abundance (local density) or growth (λ) are the most sensitive metrics to change. We discuss the value in implementing spatially explicit capture-recapture (SCR), the current gold standard for density estimation, and open population SCR (OPSCR) to monitor changes in density over time. We provide guidance for designing studies to provide estimates with sufficient power to detect changes. Because of the wide availability of camera traps and interest in their use, we consider six density estimation methods and their extensions developed for use with camera traps, with specific consideration of assumptions and applications for monitoring Asian bears. We conducted a power analysis to calculate the precision in estimates needed to detect changes in populations with reference to IUCN Red List criteria. We performed a systematic review of empirical studies implementing camera trap abundance estimation methods and considered sample sizes, effort, and model assumptions required to achieve adequate precision for population monitoring. We found SCR and OPSCR, reliant on “marked” individuals, are currently the only methods with enough power to reliably detect even moderate to major (20–80%) declines. Camera trap methods with unmarked individuals rarely achieved precision sufficient to detect even large declines (80–90%), although with some exceptions (e.g., situations with moderate population densities, large number of sampling sites, or inclusion of ancillary local telemetry data. We describe additional estimation options including line transects, direct observations, monitoring age-specific survival and reproductive rates, and hybrid/integrated methodologies that may have potential to work for some Asian bear populations. We conclude monitoring changes in abundance or density is possible for most Asian bear populations but will require collaboration among researchers over broad spatial extents and extensive financial investment to overcome biological and logistical constraints. We strongly encourage practitioners to consider study design and sampling effort required to meet objectives by conducting simulations, power analyses, and assumption checks prior to implementing monitoring efforts, and reporting standardized dispersion measures such as coefficients of variation to allow for assessment of precision. Our guidance is relevant to other low-density and wide-ranging species.

Notebook‐as‐a‐VRE (NaaVRE): From private notebooks to a collaborative cloud virtual research environment

AbstractVirtual research environments (VREs) provide user‐centric support in the lifecycle of research activities, for example, discovering and accessing research assets or composing and executing application workflows. A typical VRE is often implemented as an integrated environment, including a catalog of research assets, a workflow management system, a data management framework, and tools for enabling user collaboration. In contrast, notebook environments like Jupyter allow researchers to rapidly prototype scientific code and share their experiments as online accessible notebooks. Jupyter can support several popular languages used by data scientists, such as Python, R, and Julia. However, such notebook environments do not have seamless support for running heavy computations on remote infrastructure or finding and accessing collaborative software code inside notebooks. This article investigates the gap between a notebook environment and a VRE and proposes an embedded VRE solution for the Jupyter environment called Notebook‐as‐a‐VRE (NaaVRE). The NaaVRE solution provides functional components via a component marketplace and allows users to create a customized VRE on top of the Jupyter environment. From the VRE, a user can search research assets (data, software, and algorithms), compose workflows, manage the lifecycle of an experiment, and share the results among users in the community. We demonstrate how such a solution can enhance a legacy workflow that uses Light Detection and Ranging (LiDAR) data from country‐wide airborne laser scanning surveys for deriving geospatial data products of ecosystem structure at high resolution over broad spatial extents. This enables users to scale out the processing of multi‐terabyte LiDAR point clouds for ecological applications to more data sources in a distributed cloud environment. Similar applications could be developed for workflows producing other essential biodiversity variables.

On the efficiency of indicator species for broad-scale monitoring of bird diversity across climate conditions

Multiple constraints (e.g., time, funding, expertise) can impede the broad-scale monitoring of human-induced loss of biodiversity. Non-random species co-occurrence provides an opportunity to estimate local species richness by tracking only a few species that are indicators of more global assemblages. Despite promising fine-scale studies, it remains unclear whether such indicator species remain effective over broad spatial extents. We assessed the value of indicator species to consistently predict species richness despite variation in short-term (5–9 years) climate conditions. Our study involves 22,041 point-count stations distributed over 1.75 M km2 of Canadian boreal forest, where 197,000 birds from 216 species were detected. Using null model analysis of species co-occurrence followed by regression analysis, we identified the minimum set of indicator species that can predict 70% of spatial variation in local bird richness in each of eight short-term climate regions. We found that indicators were non-random subsets of the species pool, as they were species whose presence explained a relatively high percentage of variations in species richness within the short-term climate region, and those sharing a relatively high number of significant pairwise associations. Although only 11 to 26 indicator species were needed to predict species richness at the regional scale, 57 of the 216 species pool (26%) were necessary to make predictions over the entire study area. This large number reflects regional variations in the best indicator species, and those that remained indicators in several regions were representative of different species assemblages. Our observations thus cast doubts on the use of indicator species as an effective and efficient tool for biodiversity monitoring under changing climate conditions. Broad-scale (spatial or temporal) use of indicators thus comes with the colossal challenge of having to determine under which new conditions a given set of indicators must be replaced by another, and by which one.

Evaluating ecological uniqueness over broad spatial extents using species distribution modelling

Local contributions to beta diversity (LCBD) can be used to identify sites with high ecological uniqueness and exceptional species composition within a region of interest. Yet, these indices are typically used on local or regional scales with relatively few sites, as they require information on complete community compositions difficult to acquire on larger scales. Here, we investigated how LCBD indices can be predicted over broad spatial extents using species distribution modelling and examined the effect of scale changes on beta diversity quantification. We used Bayesian additive regression trees (BARTs) to predict warbler species distributions in North America based on observations recorded in the eBird database. We then calculated LCBD indices for observed and predicted data and compared the site‐wise difference using direct comparison, a spatial association test and generalized linear regression. We also examined the relationship between LCBD values and species richness in different regions and at various spatial extents. Our results showed that species distribution models provided uniqueness estimates highly correlated with observed data. The form and variance of the LCBD–richness relationship varied according to the region and the total extent size. The relationship was also affected by the proportion of rare species in the communities. Therefore, sites identified as unique over broad spatial extents may vary according to regional characteristics. These results show that species distribution modelling can be used to predict ecological uniqueness over broad spatial extents, which could help identify beta diversity hotspots and important targets for conservation purposes in unsampled locations.

Analyzing the effects of nature exposure on perceived satisfaction with running routes: An activity path-based measure approach

Studies on the linkages between nature exposure and physical activities often focus simply on the immediate vicinity of home locations, but path-based exercises, such as running and cycling, are continuous activities and cover a broad spatial extent. Thus, the traditional home buffer approach fails to acknowledge the settings where road running actually occurs. This study employed an activity path-based measure approach using public participation GIS (PPGIS) to investigate the associations between running satisfaction and nature exposure. The mapped routes (N=545) that included an assessment of satisfaction level were collected from 249 runners resided in the Helsinki Metropolitan Area, Finland. Logistic regression analyses revealed a positive association between running satisfaction and nature exposure, including eye-level greenness, top-down greenness and blue space density. Top-down greenness was assessed by Normalized Difference Vegetation Index (NDVI) and the eye-level greenness by Green View Index (GVI), the latter one of which uses a deep learning algorithm. Running environment was more satisfying in those routes with more public transport nodes. Other traffic-related factors breaking the momentum of runners such as traffic light density were inversely related to running satisfaction. Demographic characteristics such as education background also played a significant role in the perceived satisfaction with running routes. The positive impacts of nature exposure on running satisfaction further verify the linkages between landscape and public health.

Population Genetics of Brook Trout in the Southern Appalachian Mountains

AbstractBroad‐scale patterns of genetic diversity for Brook Trout Salvelinus fontinalis remain poorly understood across their endemic range in the eastern United States. We characterized variation at 12 microsatellite loci in 22,020 Brook Trout among 836 populations from Georgia, USA, to Quebec, Canada, to the western Great Lakes region. Within‐population diversity was typically lower in the southern Appalachian Mountains relative to the mid‐Atlantic and northeastern regions. Effective population sizes in the southern Appalachians were often very small, with many estimates less than 30 individuals. The population genetics of Brook Trout in the southern Appalachians are far more complex than a conventionally held simple “northern” versus “southern” dichotomy would suggest. Contemporary population genetic variation was consistent with geographic expansion of Brook Trout from Mississippian, mid‐Atlantic, and Acadian glacial refugia as well as differentiation among drainages within these broader clades. Genetic variation was pronounced among drainages (57.4% of overall variation occurred among 10‐digit hydrologic unit code [HUC10] units or larger units) but was considerable even at fine spatial scales (13% of variation occurred among collections within HUC12 drainage units). Remarkably, 87.2% of individuals were correctly assigned to their collection of origin. While comparisons with fish from existing major hatcheries showed impacts of stocking in some populations, genetic introgression did not overwhelm the signal of broad‐scale patterns of population genetic structure. Although our results reveal deep genetic structure in Brook Trout over broad spatial extents, fine‐scale population structuring is prevalent across the southern Appalachians. Our findings highlight the distinctiveness and vulnerability of many Brook Trout populations in the southern Appalachians and have important implications for wild Brook Trout management. To facilitate application of our findings by conservation practitioners, we provide an interactive online visualization tool to allow our results to be explored at management‐relevant scales.

Lake-wide mapping of littoral habitat using underwater videography

Littoral zones − referring to benthic areas above the light compensation depth − provide numerous ecosystem functions, including mediating light, temperature, and nutrient dynamics, and supporting important foraging and refuge areas for macroinvertebrates, fishes and water birds. Habitat assessments of littoral zones remain fundamental to lake and fisheries management, however traditional field surveys are time-intensive and limited in their spatial extent, whereas desktop evaluations using remote sensing and aerial imagery are cost prohibitive and require considerable data processing expertise. In light of these challenges, this study demonstrated the ability to use simple, cost-effective underwater videography to conduct lake-wide spatially-continuous assessments of littoral habitat. For lakes across a gradient of shoreline and riparian development in northwestern United States, we map the areal coverage of macrophytes, coarse woody habitat, bottom substrates, and artificial structures in littoral zones. Underwater videography represents a relevant tool for environmental monitoring because it allows for the estimation of littoral habitats at fine spatial grains across broad spatial extents. Data can also be obtained rapidly and at relatively low cost, providing a permanent record of habitat conditions that can used to monitor trends over time.

Can fisheries bioenergetics modelling refine spatially explicit assessments of climate change vulnerability?

Rising water temperature under climate change is affecting the physiology, population dynamics and geographic distribution of freshwater taxa. We propose a novel application of individual-based bioenergetics modelling (BEM) to assess the physiological impacts of warming on freshwater fishes across broad spatial extents. We test this approach using the Guadalupe bass (Micropterus treculii), a species of conservation and recreational significance that is endemic to central TX, USA. We projected historical-to-future changes (middle 20th century to end of 21st century) in daily bioenergetics of individual fish across 7872 stream reaches and compared this output to changes in reach occupancy derived from traditional species distribution modelling (SDM). SDMs project an 8.7% to 52.1% decrease in reach occupancy, depending on model parameterizations and climate change scenarios. Persistence is projected in the central Edwards Plateau region, whereas extirpations are projected for the warmer southeastern region. BEM projected a median 79.3% and 143.2% increase in somatic growth of age-1 Guadalupe bass across historically occupied reaches under moderate and severe climate change scenarios, respectively. Higher end-of-year body size under future climate was caused by a longer growing season. Future scenarios exploring suppressed or enhanced prey consumption suggest that small changes in prey availability will have relatively greater effects on growth than forecasted changes in temperature. Projected growth was geographically discordant with SDM-based habitat suitability, suggesting that SDMs do not accurately reflect fundamental thermal niche dimensions. Our assessment suggests that for locations where the species persists, Guadalupe bass may benefit from warming, although realized consumption gains will depend on seasonal, spatially varying changes in prey availability and other biotic and abiotic factors. More generally, we demonstrate that uniting species-specific BEM with spatially explicit climate change projections can elucidate the physiological impacts of climate change-including seasonal variation-on freshwater fishes across broad geographic extents to complement traditional SDM.

A food web approach reveals the vulnerability of biocontrol services by birds and bats to landscape modification at regional scale

Pest control services provided by naturally occurring species (the so-called biocontrol services) are widely recognized to provide key incentives for biodiversity conservation. This is particularly relevant for vertebrate-mediated biocontrol services as many vertebrate species are of conservation concern, with most of their decline associated to landscape modification for agricultural purposes. Yet, we still lack rigorous approaches evaluating landscape-level correlates of biocontrol potential by vertebrates over broad spatial extents to better inform land-use and management decisions. We performed a spatially-explicit interaction-based assessment of potential biocontrol services in Portugal, using 1853 pairwise trophic interactions between 78 flying vertebrate species (birds and bats) and 53 insect pests associated to two widespread and economically valuable crops in the Euro-Mediterranean region, olive groves (Olea europaea subsp. europaea) and vineyards (Vitis vinifera subsp. vinifera). The study area was framed using 1004 square cells, each 10 × 10 km in size. Potential biocontrol services were determined at all those 10 × 10 km grid-cells in which each crop was present as the proportion of the realized out of all potential pairwise interactions between vertebrates and pests. Landscape correlates of biocontrol potential were also explored. Our work suggests that both birds and bats can effectively provide biocontrol services in olive groves and vineyards as they prey many insect pest species associated to both crops. Moreover, it demonstrates that these potential services are impacted by landscape-scale features and that this impact is consistent when evaluated over broad spatial extents. Thus, biocontrol potential by vertebrates significantly increases with increasing amount of natural area, while decreases with increasing area devoted to target crops, particularly olive groves. Overall, our study highlights the suitability of our interaction-based approach to perform spatially-explicit assessments of potential biocontrol services by vertebrates at local spatial scales and suggest its utility for integrating biodiversity and ecosystem services in conservation planning over broad spatial extents.

Using Hyperspectral Imagery to Characterize Rangeland Vegetation Composition at Process-Relevant Scales

Rangelands are composed of patchy, highly dynamic herbaceous plant communities that are difficult to quantify across broad spatial extents at resolutions relevant to their characteristic spatial scales. Furthermore, differentiation of these plant communities using remotely sensed observations is complicated by their similar spectral absorption profiles. To better quantify the impacts of land management and weather variability on rangeland vegetation change, we analyzed high resolution hyperspectral data produced by the National Ecological Observatory Network (NEON) at a 6500-ha experimental station (Central Plains Experimental Range) to map vegetation composition and change over a 5-year timescale. The spatial resolution (1 m) of the data was able to resolve the plant community type at a suitable scale and the information-rich spectral resolution (426 bands) was able to differentiate closely related plant community classes. The resulting plant community class map showed strong accuracy results from both formal quantitative measurements (F1 75% and Kappa 0.83) and informal qualitative assessments. Over a 5-year period, we found that plant community composition was impacted more strongly by weather than by the rangeland management regime. Our work displays the potential to map plant community classes across extensive areas of herbaceous vegetation and use resultant maps to inform rangeland ecology and management. Critical to the success of the research was the development of computational methods that allowed us to implement efficient and flexible analyses on the large and complex data.

Statistical inference for trends in spatiotemporal data

Global change analyses are facilitated by the growing number of remote-sensing datasets that have both broad spatial extent and repeated observations over decades. These datasets provide unprecedented power to detect patterns of time trends involving information from all pixels on a map. However, rigorously testing for time trends requires a solid statistical foundation to identify underlying patterns and test hypotheses. Appropriate statistical analyses are challenging because environmental data often have temporal and spatial autocorrelation, which can either obscure underlying patterns in the data or suggest false associations between patterns in the data and independent values used to explain them. Existing statistical methods that account for temporal and spatial autocorrelation are not practical for remote-sensing datasets that often contain millions of pixels. Here, we first analyze simulated data to show the need to account for both spatial and temporal autocorrelation in time-trend analyses. Second, we present a new statistical approach, PARTS (Partitioned Autoregressive Time Series), to identify underlying patterns and test hypotheses about time trends using all pixels in large remote-sensing datasets. PARTS is flexible and can include, for example, the effects of multiple independent variables, such as land-cover or latitude, on time trends. Third, we use PARTS to analyze global trends in NDVI, focusing on trends in pixels that have not experienced land-cover change. We found that despite the appearance of overall increases in NDVI in all continents, there is little statistical support for these trends except for Asia and Europe, and only in some land-cover classes. Furthermore, we found no overall latitudinal trend in greening for any continent, but some latitude by land-cover class interactions, implying that latitudinal patterns differed among land-cover classes. PARTS makes it possible to identify patterns and test hypotheses that involve the aggregate information from many pixels on a map, thereby increasing the value of existing remote-sensing datasets.

Does Reproductive Status Influence Habitat Selection by Female Greater Sage-grouse in a Sagebrush-Juniper Landscape?

Greater sage-grouse (Centrocercus urophasianus, hereafter, sage-grouse) have experienced habitat loss from expansion of juniper (Juniperus spp.) woodlands into sagebrush steppe. Sage-grouse avoid high levels of juniper cover (> 10% cover) but can vary in response to cover > 0–10%. Selection patterns by hens may be affected by differences in resource needs or predation risk between brooding (i.e., hens with chicks) and nonbrooding hens (i.e., hens without chicks). We investigated whether reproductive status influenced habitat selection at multiple spatial scales among hens in a landscape undergoing juniper expansion. We conducted our study during the 2017–2018 breeding seasons in southwestern Idaho. We collected habitat data at broad spatial extents (hereafter, macroscales) using remotely sensed layers and at a fine spatial extent (hereafter, microscale) with field-based surveys. We collected data at used and available locations for 11 brooding and 19 nonbrooding hens at macroscales (n = 2 059 locations) and microscales (n = 181 locations). At the macroscales, both reproductive groups avoided cover class II (> 10–20% juniper cover) and III (> 20% juniper cover) but nonbrooding hens were 2.8 × more likely to select cover class I (> 0–10% juniper cover) than brooding hens. In addition, nonbrooding hens selected wetlands dominated by woody vegetation (e.g., willows), whereas brooding hens selected wetlands dominated by herbaceous vegetation. At the microscale, brooding hens were 22.1 × more likely to select taller nonsagebrush shrubs than nonbrooding hens. Our results support our prediction that nonbrooding hens were more likely to select cover class I juniper than brooding hens. Our results help inform more targeted treatment whereby removal of juniper around wetlands and mesic habitats with taller nonsagebrush shrubs may be the most beneficial because increasing the availability of these habitats could positively influence survival of chicks and adults.

The Rapid Rise of Next-Generation Natural History

Many ecologists have lamented the demise of natural history and have attributed this decline to a misguided view that natural history is outdated and unscientific. Although there is a perception that the focus in ecology and conservation have shifted away from descriptive natural history research and training toward hypothetico-deductive research, we argue that natural history has entered a new phase that we call “next-generation natural history.” This renaissance of natural history is characterized by technological and statistical advances that aid in collecting detailed observations systematically over broad spatial and temporal extents. The technological advances that have increased exponentially in the last decade include electronic sensors such as camera-traps and acoustic recorders, aircraft- and satellite-based remote sensing, animal-borne biologgers, genetics and genomics methods, and community science programs. Advances in statistics and computation have aided in analyzing a growing quantity of observations to reveal patterns in nature. These robust next-generation natural history datasets have transformed the anecdotal perception of natural history observations into systematically collected observations that collectively constitute the foundation for hypothetico-deductive research and can be leveraged and applied to conservation and management. These advances are encouraging scientists to conduct and embrace detailed descriptions of nature that remain a critically important component of the scientific endeavor. Finally, these next-generation natural history observations are engaging scientists and non-scientists alike with new documentations of the wonders of nature. Thus, we celebrate next-generation natural history for encouraging people to experience nature directly.

Multivariate Bayesian clustering using covariate-informed components with application to boreal vegetation sensitivity.

Climate change is impacting both the distribution and abundance of vegetation, especially in far northern latitudes. The effects of climate change are different for every plant assemblage and vary heterogeneously in both space and time. Small changes in climate could result in large vegetation responses in sensitive assemblages but weak responses in robust assemblages. But, patterns and mechanisms of sensitivity and robustness are not yet well understood, largely due to a lack of long-term measurements of climate and vegetation. Fortunately, observations are sometimes available across a broad spatial extent. We develop a novel statistical model for a multivariate response based on unknown cluster-specific effects and covariances, where cluster labels correspond to sensitivity and robustness. Our approach utilizes a prototype model for cluster membership that offers flexibility while enforcing smoothness in cluster probabilities across sites with similar characteristics. We demonstrate our approach with an application to vegetation abundance in Alaska, USA, in which we leverage the broad spatial extent of the study area as a proxy for unrecorded historical observations. In the context of the application, our approach yields interpretable site-level cluster labels associated with assemblage-level sensitivity and robustness without requiring strong a priori assumptions about the drivers of climatesensitivity.

Lodgepole pine and interior spruce radial growth response to climate and topography in the Canadian Rocky Mountains, Alberta

Climate change may have spatially variable impacts on growth of trees in topographically diverse environments, making generalizing across broad spatial and temporal extents inappropriate. Therefore, topography must be considered when analyzing growth response to climate. We address these topo-climatic relationships in the Canadian Rocky Mountains, focusing on lodgepole pine (Pinus contorta Douglas ex Louden) and interior spruce (Picea glauca (Moench) Voss × Picea engelmannii hybrid Parry) growth response to climate, Palmer drought severity index (PDSI), aspect, and slope angle. Climate variables correlate with older lodgepole pine growth on south- and west-facing slopes, including previous August temperature, winter and spring precipitation, and previous late-summer and current spring PDSI, but younger lodgepole pine were generally less sensitive to climate. Climate variables correlate with interior spruce growth on all slope aspects, with winter temperature and PDSI important for young and old individuals. Numerous monthly growth–climate correlations are not temporally stable, with shifts over the past century, and response differs by slope aspect and angle. Both species are likely to be negatively affected by moisture stress in the future in some, but not all, topographic environments. Results suggest species-specific and site-specific spatiotemporally diverse climate–growth responses, indicating that climate change is likely to have spatially variable impacts on radial growth response in mountainous environments.