This report presents considerations of potential hazards and mitigation measures associated with conducting field research in the context of a pathogenic epidemic or pandemic situation. We use an example of a specific risk assessment developed for advising decisions on initiating or continuing field activities (in this case, markresight and passive acoustic monitoring) associated with ongoing research of northern spotted owls (Strix occidentalis caurina) in the Pacific Northwest region of the United States under conditions imposed by the COVID-19 (severe acute respiratory syndrome coronavirus 2 or SARS-CoV-2) global pandemic. We review the structure of a risk assessment procedure that follows USDA Forest Service policy in general and has specifically been applied to owl research during the current pandemic. The risk assessment framework we used included listing job objectives, job tasks, and potential hazards associated with each task. For each task, we evaluated the severity of the hazard (negligible, moderate, critical, or catastrophic) and the probability of a mishap if the hazard was present (rare, unlikely, possible, likely, or almost certain) and assigned a risk assessment code that identified risks as low, moderate, high, or extremely high. We then described mitigation and abatement measures that we posited would reduce the risk severity or probability, and then scored the residual (decreased) severity, probability, and risk level. We briefly review other potential considerations for a job hazard risk assessment under conditions of pathogenic outbreaks, including considerations for additional costs and administrative duties, working in proximity and unexpected encounters in field situations, and changes in behavior of wildlife.
Background: Tree species in the genus Cedrela P. Browne are threatened by timber overexploitation across the Neotropics. Genetic identification of processed timber can be used to supplement wood anatomy to assist in the taxonomic and source validation of protected species and populations of Cedrela. However, few genetic resources exist that enable both species and source identification of Cedrela timber products. We developed several ‘omic resources including a leaf transcriptome, organelle genome (cpDNA), and diagnostic single nucleotide polymorphisms (SNPs) that may assist the classification of Cedrela specimens to species and geographic origin and enable future research on this widespread Neotropical tree genus.
Results: We designed hybridization capture probes to enrich for thousands of genes from both freshly preserved leaf tissue and from herbarium specimens across eight Meliaceae species. We first assembled a draft de novo transcriptome for C. odorata, and then identified putatively low-copy genes. Hybridization probes for 10,001 transcript models successfully enriched 9795 (98%) of these targets, and analysis of target capture efficiency showed that probes worked effectively for five Cedrela species, with each species showing similar mean on-target sequence yield and depth. The probes showed greater enrichment efficiency for Cedrela species relative to the other three distantly related Meliaceae species. We provide a set of candidate SNPs for species identification of four of the Cedrela species included in this analysis, and present draft chloroplast genomes for multiple individuals of eight species from four genera in the Meliaceae.
Conclusions: Deforestation and illegal logging threaten forest biodiversity globally, and wood screening tools offer enforcement agencies new approaches to identify illegally harvested timber. The genomic resources described here provide the foundation required to develop genetic screening methods for Cedrela species identification and source validation. Due to their transferability across the genus and family as well as demonstrated applicability for both fresh leaves and herbarium specimens, the genomic resources described here provide additional tools for studies examining the ecology and evolutionary history of Cedrela and related species in the Meliaceae.
In heterogeneous landscapes, large herbivores employ plastic behavioral strategies to buffer themselves against negative effects of environmental variation on fitness. Yet, the mechanisms by which individual responses to such variation scale up to influence population performance remain uncertain. Analyses of space-use behaviors exemplify this knowledge gap, because such behaviors are often assumed, but rarely demonstrated, to have direct fitness consequences. We combined fine-scale data on forage biomass and quality with movement data and measures of somatic energy reserves to determine whether variation in use (the quantity of resource units, e.g., pixels on a landscape, that receive some level of investment by an animal during a specific sampling period) or selection (use of a resource unit relative to its availability to the animal during the same sampling period) of the nutritional landscape predicted early winter body condition of mule deer (Odocoileus hemionus). At the population level, mule deer exhibited stronger selection for high forage biomass at the landscape scale than at the home-range scale, and during summer than during spring. Use of the nutritional landscape varied among individual deer and had important consequences for early winter condition (an important determinant of survival and reproduction in capital-breeding ungulates). Females that consistently used vegetation communities that provided high biomass of preferred forage plants throughout spring and summer entered winter in better condition than females that used those vegetation communities less frequently. In contrast, selection (i.e., use relative to availability) of the nutritional landscape by individual deer was not significantly related to early winter condition at either the landscape or home-range scales. Our results highlight the value of using mechanistic, nutritional approaches to understand the potential fitness consequences of individual variation in behavior. In addition, our study suggests that patterns of forage use by ungulates may sometimes correlate more strongly with fitness than patterns of forage selection, which are scale-dependent and more vulnerable to biases stemming from the need to accurately quantify availability.
Passive acoustic monitoring using autonomous recording units (ARUs) is a fast-growing area of wildlife research especially for rare, cryptic species that vocalize. Northern Spotted Owl (Strix occidentalis caurina) populations have been monitored since the mid-1980s using mark–recapture methods. To evaluate an alternative survey method, we used ARUs to detect calls of Northern Spotted Owls and Barred Owls (S. varia), a congener that has expanded its range into the Pacific Northwest and threatens Northern Spotted Owl persistence. We set ARUs at 30 500-ha hexagons (150 ARU stations) with recent Northern Spotted Owl activity and high Barred Owl density within Northern Spotted Owl demographic study areas in Oregon and Washington, and set ARUs to record continuously each night from March to July, 2017. We reviewed spectrograms (visual representations of sound) and tagged target vocalizations to extract calls from ~160,000 hr of recordings. Even in a study area with low occupancy rates on historical territories (Washington’s Olympic Peninsula), the probability of detecting a Northern Spotted Owl when it was present in a hexagon exceeded 0.95 after 3 weeks of recording. Environmental noise, mainly from rain, wind, and streams, decreased detection probabilities for both species over all study areas. Using demographic information about known Northern Spotted Owls, we found that weekly detection probabilities of Northern Spotted Owls were higher when ARUs were closer to known nests and activity centers and when owls were paired, suggesting passive acoustic data alone could help locate Northern Spotted Owl pairs on the landscape. These results demonstrate that ARUs can effectively detect Northern Spotted Owls when they are present, even in a landscape with high Barred Owl density, thereby facilitating the use of passive, occupancy-based study designs to monitor Northern Spotted Owl populations.
Modeling landscape use (i.e., estimating the probability or relative probability of use, occurrence, or selection in a given area and time) by ungulates is an increasingly common and important practice in research and management. Models of occupancy, distribution, movement, habitat use, and resource selection are formal approaches by which landscape use has been characterized and results published for a myriad of ungulate species. Understanding landscape use has benefited from a growing volume of data on animal locations and model covariates, and the ease of modeling with automated software. These models are particularly noteworthy in their potential to estimate use at multiple scales, characterize individual and population distributions, and predict spatiotemporal responses to environmental change. Despite these advantages, ecological processes can be secondary or forgotten. Models without a strong ecological foundation may perform well in case studies but fail to advance our understanding of a species’ habitat requirements and response to habitat change across a broad inference space. In response, we describe criteria, synthesized from the ecological literature, of direct relevance to modeling landscape use for advancing the ecological understanding and effective management of ungulates. Criteria include (1) a knowledge coproduction framework for scientist-manager collaborations; (2) an explicit inference space with supporting replication for broad inference; (3) process covariates and their ecological scaling to address habitat requirements; (4) ecologically plausible sets of competing models; (5) model evaluation to address objectives and hypotheses of ecological importance; (6) assessment of relationships with animal and population performance; and (7) reliable interpretations for ecological understanding and management uses. Contemporary modeling of landscape use has been challenged by large, disparate data sources and an emphasis on statistical methods. However, advances in knowledge and conservation of ungulates based on tenets of ecology, management, and inference are achievable with careful consideration of these criteria.
Variation in climate, disturbance regime, and forest management strongly influence terrestrial carbon sources and sinks. Spatially distributed, process-based, carbon cycle simulation models provide a means to integrate information on these various influences to estimate carbon pools and flux over large domains. Here we apply the Biome-BGC model over the four-state Northwest US region for the interval from 1986 to 2010. Landsat data were used to characterize disturbances, and forest inventory data were used to parameterize the model. The overall disturbance rate on forest land across the region was 0.8 % year-1, with 49 % as harvests, 28 % as fire, and 23 % as pest/pathogen. Net ecosystem production (NEP) for the 2006–2010 interval on forestland was predominantly positive (a carbon sink) throughout the region, with maximum values in the Coast Range, intermediate values in the Cascade Mountains, and relatively low values in the Inland Rocky Mountain ecoregions. Localized negative NEPs were mostly associated with recent disturbances. There was large interannual variation in regional NEP, with notably low values across the region in 2003, which was also the warmest year in the interval. The recent (2006–2010) net ecosystem carbon balance (NECB) was positive for the region (14.4 TgC year-1). Despite a lower area-weighted mean NECB, public forestland contributed a larger proportion to the total NECB because of its larger area. Aggregated forest inventory data and inversion modeling are beginning to provide opportunities for evaluating model-simulated regional carbon stocks and fluxes.
The guidelines proposed in Urban Tree Monitoring: A Resource Guide (hereafter referred to as the Resource Guide) were developed and refined over many years to address the need for standardized urban tree monitoring protocols. The Resource Guide provides in-depth guidance for urban forest managers and researchers who want to design and implement a tree monitoring project. This Resource Guide is a companion to Urban Tree Monitoring: A Field Guide; however, the Resource Guide can also be used on its own. The Resource Guide is divided into three parts. In Part I, we discuss (1) the varied goals of monitoring projects and how to match data collection to those goals, (2) the development of these urban tree monitoring standards, (3) types of monitoring projects, and (4) connections to other protocols for urban tree data collection. We offer guidance on methods for recording tree location, developing tree record identifiers, organizing spreadsheets and databases, choosing data collection systems, fostering research-practice partnerships, training crews, and managing fieldwork. In Part II, we present five monitoring data sets: Minimum Data Set, Tree Data Set, Site Data Set, Young Tree Management Data Set, and Community Data Set. We list study goals that could be addressed with each data set and descriptions of relevant variables. We also provide guidance regarding which variables are best suited for beginner and advanced crews. Lastly, in Part III we include appendices with additional resources for designing and implementing tree monitoring projects.
The relative contribution of private and public forest to the conservation of species in mixed-ownership landscapes has often been contentious because management goals vary among owners. This tension can be exacerbated by a lack of understanding about how wildlife use habitats managed by different landowners and the relative value of habitats in having different structures, configurations, and management histories. To address this knowledge gap and enhance science-based conservation planning among different ownerships, we analyzed habitat selection by 53 GPS-tagged California spotted owls across multiple temporal scales within mixed-ownership landscapes in the Sierra Nevada. At a fine temporal scale, step-selection function analysis of hourly locations collected by GPS tags suggested that foraging spotted owls selected closed-canopy, larger-tree forest (Quadratic Mean Diameter [QMD] ≥ 33 cm, canopy cover ≥ 60%). Point selection function (PSF) analysis based on single nightly locations suggested that spotted owls selected a broader range of forest conditions including selection of forests having intermediate sized trees and intermediate canopy cover (QMD 28–33 cm, canopy cover ≥ 50%), and the strength of selection for these forest conditions increased in the less frequently used areas of home ranges. The PSF also suggested that spotted owls selected areas with relatively high cover type heterogeneity that included a mix of seral stages, except in the core of their home range where they selected relatively spatially homogenous forests characterized by large trees and closed canopy. Spotted owl home ranges increased in size with increasing elevation and cover type heterogeneity, and decreased in size with forest characterized by intermediate-sized trees. Collectively, these results indicate that landscapes having forest patches characterized by either intermediate or large-sized trees, both with high canopy cover, likely constitute the important foraging habitat for California spotted owls in Sierra Nevada mixed conifer forests. However, selection for any one particular cover type was not sufficiently strong for us to infer selection of individual landownership types, in spite of differences in forest conditions among ownerships. Collectively, our findings suggest that privately-owned lands used in our study may harbor more suitable spotted owl foraging habitat than previously recognized. Finally, given the importance of understanding the relationship between landowner management priorities and the resultant pattern of vegetation on lands with different ownerships, the development of forest management strategies relevant for broad-scale conservation of the Sierra Nevada forest will benefit from effective collaboration between forest managers, landowners, and research organizations.