Percent Forest Research Articles

Spatio-temporal dynamic of forest cover distribution in Omo-Shasha-Oluwa Tropical Forest Complex, Nigeria, (2014 – 2022)

: Forest Reserves (FRs) constitute a significant portion of the forested lands within protected areas, and their conservation is essential for Nigeria to achieve the United Nations’ Sustainable Development Goals (SDGs). Our study conducted an analysis of the dynamics of forested lands in the study area to evaluate and rank management performance over the period of 2014-2022. The forest bloc in OSO witnessed an expansion by 1.39% (3,929.67 ha) from 27.73% to 29.12% forest cover in 2014 and 2022, respectively. Forest fragments declined from 16 to 10 units for the forest bloc (>201.1 ha) with a proportionate increase in the Largest Patch Index (LPI) from 5,815.53 ha to 39,234.15 ha, in the period 2014 -2022, respectively. Omo, Ago-Owu, Ife and Shasha FRs all witnessed negative changes in the percent forest cover representing a decline of about -0.43% (529.52 ha), -37.62% (9028.8 ha), -8.54% (1217.43 ha) and -0.29% (89.55 ha), respectively. Oluwa FR had an increase in the forest cover by 17.64% or 14,596.38 ha in the period 2014 - 2022. The forest cover retention rates in the period 2014 – 2022, ranks Oluwa FR ahead of other FRs in terms of Protected Area (PA) management efficiency. The results highlight the existence of a relatively better and effective forest management system in the reserve. Landsat data are capable of providing near real-time forest cover information that can be integrated into existing tracking tools for periodically gauging management efficiency and performances across the PAs of Nigeria.

Implications of human–wildlife conflict on the diet of an endangered avian top predator in the northern Andes

Conflicts between rural people and the Endangered Black-and-chestnut Eagle (Spizaetus isidori) are a prominent conservation concern in the northern Andes, as at least 60 eagles were poached between 2000 and 2022 in response to poultry predation. Here, we conducted direct observations to analyze the Black-and-chestnut Eagle diet and evaluated how forest cover affects the feeding habits of the species during nestling-rearing periods in 16 nests located in different human-transformed Andean landscapes of Ecuador and Colombia. We analyzed 853 prey items (46 species) delivered to nestlings. We used Generalized Linear Models to test whether the percent forest cover calculated within varying buffer distances around each nest and linear distances from the nest to the nearest settlement and pasture areas were predictors of diet diversity and biomass contribution of prey. Forest cover was not a factor that affected the consumption of poultry; however, the eagle regularly preyed on chickens (Gallus gallus) (i.e., domestic Galliformes) which were consumed by 15 of the 16 eagle pairs, with biomass contributions (14.57% ± 10.55) representing 0.6–37% of the total prey consumed. The Black-and-chestnut Eagle is an adaptable generalist able to switch from mammalian carnivores to guans (i.e., wild Galliformes) in human-dominated landscapes, and eagles nesting in sites with low forest cover had a less diverse diet than those in areas with more intact forests. Management actions for the conservation of this avian top predator require studies on the eagle’s diet in areas where human persecution is suspected or documented, but also maintaining forest cover for the wild prey of the species, development of socio-economic and psychological assessments on the drivers behind human-eagle conflicts, and the strengthening of technical capacities of rural communities, such as appropriate poultry management.

Species richness and ecological connectivity of the mammal communities in urban and peri-urban areas at Mexico City

Urban expansion has emerged as a pervasive driver of biodiversity loss in Mexican cities due to shifts in landscape composition and configuration. Hence, the preservation of forest cover, green spaces and isolated trees assume a critical role in conserving the biodiversity within urban areas. We compared mammal assemblages across 520 sites in Mexico City examining the impact of local vegetation and site characteristics (e.g., patch size, isolation distance) on diversity patterns. Then, we used a generalized linear model to evaluate the relationship between mammal assemblages (including both alpha and beta diversities) and the level of structural and functional connectivity across the landscape. We collected 5,063 records of 38 species from the sampled sites. Mammalian richness ranged from 1 to 9 spp./site, and the mean of taxonomic and functional (Functional Distance [MFD]) alpha diversities were 1.9 ± 1.3 spp./site and 0.41 ± 0.60 MFD/site, respectively. The average size of habitat fragments was 0.41 hectares, and the percent forest cover per site ranged from 0.5 to 100%. Both species and functional alpha diversities were different among the land-use and vegetation types (including protected vs. non-protected areas). There was a clear distinction (β value > 0.40) in species composition across the landscape, which was positively related to geographical distance and negatively related to connectivity between sites. We identified four main patches especially important to connectivity across the city and argue that conserving them is critical for restoring key components of biodiversity and ecosystem services in urban and peri-urban areas of Mexico City.

Eastern Black Kingsnake Lampropeltis nigra dorsal scale colouration primarily adapted for thermoregulation

The Eastern Black Kingsnake Lampropeltis nigra exhibits variation in colouration and pattern throughout its range. Drivers behind this colour variation remain largely unknown. To elucidate patterns in the percentage of light pigmentation in L. nigra dorsal scales (% light) we compared two primary hypotheses. Our first hypothesis posited that L. nigra colouration has been adapted for thermoregulation (thermoregulation). Alternatively, we hypothesised that L. nigra colouration has been adapted to avoid detection by predators and prey (crypsis). To test our hypotheses, we modeled L. nigra % light from 46 counties within Mississippi as a function of average temperature, average maximum temperature (thermoregulation) and soil value (darkness of soil colour; crypsis). We included percentage forest cover as a modifying variable in the thermoregulation hypothesis and an interacting variable in the crypsis hypothesis. The best competing model included average temperature and percent forest cover as explanatory variables (LOOIC = -291.3, weight = 0.84, Bayesian R2 = 0.37 [fixed] and 0.55 [total]). There was a positive relationship between % light and average temperature (β = 0.23, 95% CIs = 0.13, 0.34) and average maximum temperature (β = 0.11, 95% CIs = -0.00, 0.21), and a negative relationship with percent forest cover (β = -0.14, 95% CIs = -0.23, -0.04). These results support the thermoregulation hypothesis. The climate in Mississippi, as in most of the world, is expected to experience shifts over the next century. If ambient temperatures experienced through these changes are outside of L. nigra’s thermal optima, as based on colour patterns and relationships observed in our study, then behavioural adaptations may result in some individuals experiencing thermal advantages that influence this species’ distribution. The patterns observed in our study, and expected changes in L. nigra behaviour and distribution, are likely to occur among other ectothermic species with relatively static colouration. Keywords: Colour adaptation, homeostasis, Mississippi

How deforestation and forest use affect tree diversity in a conifer forest from central Mexico

Aim of study: To assess the association of deforestation and forest use with tree diversity and dominance of pine species. Area of study: La Malinche National Park, central Mexico. Material and methods: Three forest stands were selected on each of four slopes in La Malinche National Park, in which the percent forest cover was determined using a concave spherical densitometer. Additionally, we estimated logging, firewood, and ocoteo as indirect measures of forest use. In each forest stand, we set five 0.1-ha circular plots and we recorded tree species, diameter at breast height, and height of all individuals ≥1.5 m. We estimated diversity using Hill numbers (qD). We identified dominant species from rank-abundance curves, and explored the dissimilarity between forest stands for the three q orders. Using generalized linear mixed-effects models, we evaluated the relationships of deforestation and forest use with species diversity and pine dominance, and studied the effect of pine dominance on the abundance of other tree species through generalized linear models. Main results: We recorded a total of 16 tree species; a pine (Pinus montezumae) was the dominant species. The diversity of tree species was not associated with forest use or deforestation, but deforestation increased Pinus dominance, with a strong slope effect. Research highlights: Regulation and control of forest use should be prioritized to avoid further deforestation and the transformation of species-diverse forests into monospecific forests, further increasing the dominance of P. montezumae. We also suggest reforesting with native species and those scarcely represented to increase forest cover and maintain tree diversity.

The ecological functions of dung beetles are shaped by multiple dimensions of diversity

Abstract A mechanistic understanding of the relationship between multiple dimensions of species diversity and ecosystem functioning is needed to safeguard ecological processes critical to human well‐being. We evaluated how taxonomic, functional and phylogenetic diversities, as well as the assembly mechanisms of dung beetle communities, percent forest cover and landscape fragmentation, affect dung burial, soil excavation efficiency and dung fly larvae abundance in Los Tuxtlas Biosphere Reserve, a complex tropical landscape. Dung beetle functions were measured in 15 landscapes representing a gradient of forest cover loss (0%–100%). Phylogenetically clustered assemblages of large tunneler dung beetles showed the highest dung burial efficiency across the landscape. Fly larvae abundance was negatively correlated with the taxonomic diversity and functional evenness of dung beetles. Forest cover and landscape fragmentation did not significantly affect dung burial effectiveness, suggesting a high degree of functional redundancy. Dung burial efficacy is mainly driven by selection mechanisms, in which a single or a few phylogenetically close species disproportionately affect a given function. Fly larvae abundance was controlled by a complementary functional effect where each dung beetle species additively affected fly larvae survival. Hence, multiple functions are maintained by contrasting mechanisms and different diversity dimensions. High dung beetle species richness may foster pest fly control in tropical landscapes such as Los Tuxtlas Biosphere Reserve. Our findings highlight the need to assess multiple dimensions of diversity since each can operate concurrently and contrastingly on the different ecological functions of species assemblages.

Wildlife habitat association over a twelve-year period (2008–2020) in the Greater Mahale Ecosystem, western Tanzania

The Greater Mahale Ecosystem (GME) in western Tanzania hosts high faunal biodiversity, including charismatic species such as elephants and chimpanzees. Across the GME, habitat occurs in differently administered, unprotected and protected areas, including village land, forest reserves, and national parks. Areas of lower protective status are prone to deforestation and land cover changes. These land cover changes influence suitable habitat itself as well as the connectivity between habitat patches, but the impact on different mammal species in this region is unknown. In this study, we investigated changes in habitat availability by creating species distribution models for 11 medium-large mammal species based on species occurrence data collected from 2008 to 2020. We tested habitat associations for each species and evaluated the importance of the static predictor variables elevation, slope, aspect and terrain-ruggedness and the dynamic predictor variables distance to deforestation, percent forest, vegetation intensity, and vegetation variability, retrieved through remotely sensed data analysis. Our models suggest habitat declined for most, but not all species. Whilst elephants and buffalo lost more than 50 % of available habitat during this period, primates exhibited habitat stability. Habitat losses were pronounced in the southeast of the GME. Slope, elevation and median tasseled-cap wetness index (vegetation intensity) and distance to deforestation were the most important predictor variables. Our study presents a spatially and temporally explicit estimation of habitat changes in a critically important, biodiverse region undergoing drastic land cover changes. We call for consideration in land-use planning to foster connectivity and landscape integrity to protect and conserve wildlife across the ecosystem.

Generation and comprehensive validation of 30 m conterminous United States Landsat percent tree cover and forest cover loss annual products

This study describes the generation and comprehensive validation of 30 m Landsat-based annual percent tree cover and forest cover loss products for the conterminous United States (CONUS). The products define (i) forest status with respect to three thematic classes: stable forest, stable non-forest, forest cover loss, (ii) percent tree cover (PTC, 0–100%), (iii) percent tree cover decrease (ΔPTC), and (iv) the Landsat acquisition dates bounding mapped forest cover loss occurrence. Forest was defined, based on the U.S. federal government forested land definition, as 30 m pixels with mapped PTC >10%. Annual products were derived using temporally overlapping 9-year periods (mapping within each central 5-year period) of USGS Landsat Analysis Ready Data (ARD) with reconciliation of the results between periods. The products for 2013 are presented and were validated rigorously by comparison with 1910 30 m independent reference data interpreted from bi-temporal <1 m resolution aerial imagery selected using a Stage 3 CONUS stratified random sampling design. The stable forest, stable non-forest, and forest cover loss results were validated using standard accuracy metrics derived from the confusion matrix. The overall accuracy was high (0.92), and class-specific user's accuracy (UA) and producer's accuracy (PA) metrics were also high for the stable forest (UA = 0.94, PA = 0.84) and stable non-forest (UA = 0.90, PA = 0.97) classes. The forest cover loss class had similarly high UA (0.89) but significantly lower PA (0.61) indicating non-negligible omission errors. All standard errors were <5%. The total area of stable forest over CONUS for year 2013 was estimated as 3,049,380 ± 114,392 km2 and the total area of forest cover loss was estimated as 31,382 ± 4751 km2, with 95% confidence interval. The PTC and ΔPTC products were validated by linear regression with the reference data, indicating good PTC precision reflected by a high coefficient of determination (R2 = 0.79), and accuracy with a regression slope close to unity (0.86) and small intercept (3.48). The regression between mapped ΔPTC and the reference data had a high coefficient of determination (R2 = 0.74) but a regression slope further away from unity (0.78) and small intercept (1.68) consistent with the forest cover loss omission errors revealed by the confusion matrix. State-level comparison of the stable forest mapped area with forest land area statistics published by the U.S. federal government for the 48 CONUS states indicated reasonable correspondence (R2 = 0.97) but with a 1.15 regression line slope indicating relative over estimation of the mapped stable forest area, likely related to forest land reporting differences.

Neighborhood greenspace and cognition: The cardiovascular health study.

We examined whether greenspace measures (overall percent greenspace and forest, and number of greenspace types) were associated with clinically adjudicated dementia status. In a sample of non-demented older adults (n=2141, average age=75.3 years) from the Cardiovascular Health and Cognition Study, Cox proportional hazard and logistic regression analyses were used to estimate associations of baseline greenspace with risks of incident dementia and MCI, respectively, while adjusting for demographics, co-morbidities, and other neighborhood factors. We derived quartiles of percent greenness (greenspace), forest (percent tree canopy cover), and tertiles of greenspace diversity (number of greenspace types) for 5-km radial buffers around participant's residences at study entry (1989-1990) from the 1992 National Land Cover Dataset. Dementia status and mild cognitive impairment (MCI) over 10 years was clinically adjudicated. We observed no significant association between overall percent greenspace and risk of mild cognitive impairment or dementia and mostly null results for forest and greenspace diversity. Forest greenspace was associated with lower odds of MCI (OR quartile 4 versus 1: 0.54, 95% CI: 0.29-0.98) and greenspace diversity was associated with lower hazard of incident dementia (HR tertile 2 versus 1: 0.70, 95% CI=0.50-0.99). We found divergent results for different types of greenspace and mild cognitive impairment or dementia. Improved greenspace type and diversity measurement could better characterize the association between greenspace and cognition.

Global spatial distribution of vegetation and 20-years dynamic changes

Global spatial distribution of vegetation is determined jointly by climate, monsoon, ocean current and human activities. In recent decades, surface vegetation changes caused by global warming, extreme climate events and intensified human activities need a further understood. In this paper, we adopted available MODIS land cover dataset (MCD12Q1) to investigate the spatial distribution feature of global vegetation sand dynamics during the past 20 years. Results show that: (1) the spatial distribution pattern of global vegetation formed by the long-term ecosystem evolution exhibits stable with less variation in both latitude and vertical dimensions. Generally, the percent of forest, shrubland, grass and cropland respectively are 15.27%, 9.39%, 39.46% and 8.91% of surface covers, with a distinct land cover transition trend from rainforest at equator region to moss and lichen at the north and south pole. (2) In the past 20 years, the global forest land decreased by 3.0×105 km2, with an average annual decrease of 1.255%, open shrubs experienced slight fluctuations in 2001-2017 and increased sharply by 1.6×105 km2 in 2017-2018. And the grassland, croplands and cropland/natural vegetation mosaics had different degrees of growth rate, with an average annual dynamic increase of 0.021%, 0.019% and 0.502%, respectively. (3) There are about 4.2×106 km2 of forest changed into grassland in West Siberia and the Amazon, and 2.5×106 km2 of grassland into shrubs, as well as 1.9×106 km2 of grassland vegetation into farmland, and 9.4×104 km2 of forest into shrubs.

Explainable machine learning improves interpretability in the predictive modeling of biological stream conditions in the Chesapeake Bay Watershed, USA

Anthropogenic alterations have resulted in widespread degradation of stream conditions. To aid in stream restoration and management, baseline estimates of conditions and improved explanation of factors driving their degradation are needed. We used random forests to model biological conditions using a benthic macroinvertebrate index of biotic integrity for small, non-tidal streams (upstream area ≤200 km2) in the Chesapeake Bay watershed (CBW) of the mid-Atlantic coast of North America. We utilized several global and local model interpretation tools to improve average and site-specific model inferences, respectively. The model was used to predict condition for 95,867 individual catchments for eight periods (2001, 2004, 2006, 2008, 2011, 2013, 2016, 2019). Predicted conditions were classified as Poor, FairGood, or Uncertain to align with management needs and individual reach lengths and catchment areas were summed by condition class for the CBW for each period. Global permutation and local Shapley importance values indicated percent of forest, development, and agriculture in upstream catchments had strong impacts on predictions. Development and agriculture negatively influenced stream condition for model average (partial dependence [PD] and accumulated local effect [ALE] plots) and local (individual condition expectation and Shapley value plots) levels. Friedman's H-statistic indicated large overall interactions for these three land covers, and bivariate global plots (PD and ALE) supported interactions among agriculture and development. Total stream length and catchment area predicted in FairGood conditions decreased then increased over the 19-years (length/area: 66.6/65.4% in 2001, 66.3/65.2% in 2011, and 66.6/65.4% in 2019). Examination of individual catchment predictions between 2001 and 2019 showed those predicted to have the largest decreases in condition had large increases in development; whereas catchments predicted to exhibit the largest increases in condition showed moderate increases in forest cover. Use of global and local interpretative methods together with watershed-wide and individual catchment predictions support conservation practitioners that need to identify widespread and localized patterns, especially acknowledging that management actions typically take place at individual-reach scales.

Validity evaluation of a machine-learning model for chlorophyll a retrieval using Sentinel-2 from inland and coastal waters

The MultiSpectral Instrument (MSI) on-board Sentinel-2 provides satellite images at spatiotemporal resolutions suitable for chlorophyll a (Chla) retrieval from inland and coastal waters. Machine-learning (ML) algorithms including light gradient boosting machine (LGBM) were employed for Chl a retrieval from MSI. The study area encompasses 78 lakes and estuaries located across four major river watersheds in South Korea. Matchup data between MSI overpass and near-concurrent in situ Chl a measurements from December 2018 to April 2021 were included. The remote sensing reflectance (Rrs) values of six single spectral bands and four two-band ratios were used as the input features. Despite the difficulty in Chla estimation in optically complex waters, ML algorithms showed overall reasonable accuracy. Among the ML algorithms, LGBM exhibited the best performance (R2 = 0.75, bias = -0.15, slope = 0.73, RMSE = 15.15 mg·m-3, MAE = 9.49 mg·m-3) over a wide range of trophic states. Post-hoc interpretations of the best performing LGBM using Shapley additive explanations indicated that Rrs(704)/Rrs(665) was the most important feature, while Rrs(739)/Rrs(704) and Rrs(492)/Rrs(560) played auxiliary roles in Chl a retrieval through interaction with Rrs(704)/Rrs(665). Among-lake spatial variations of Chla were explained by percent forest and agricultural area within the buffer zone at multiple scales (buffer widths of 50 m and 500 m). The associations between the modeled Chla and buffer land cover types, that is, increase in Chla concentration with increase in percent forest and decrease in percent agricultural area, were consistent with the established ecological knowledge. Overall, the model interpretations and spatial variations in Chla within and among lakes confirmed the validity of LGBM for retrieving MSI-derived Chla from lakes and estuaries. Our study can serve as the reference for evaluating the validity of complex ML models for inland water remote sensing.

A comparison of remotely sensed environmental predictors for avian distributions

With greater accessibility and processing power from online platforms, summaries of remotely sensed data are increasingly used in species distribution models (SDMs). Comparisons of the predictive power of these environmental variables could inform SDMs moving forward. We evaluated the performance of freely available Landsat data as predictor sets for SDMs. Our objectives were to (1) compare the performance of single season SDMs built on mean values of raw spectral bands, Tasseled Cap transformations, and eight different indices, including NDVI, (2) evaluate the performance gain with the addition of standard deviation, textural metrics, and additional seasons, and (3) compare the performance of SDMs built on these continuous spectral predictor sets to SDMs built on classified land cover data (e.g., percent forest cover). We used statewide point counts to build multi-scale SDMs for 13 avian species across Oregon, USA. We compared the performance of SDMs built on each predictor set based on our objectives. Of the Landsat-derived predictor sets, SDMs built on raw spectral bands had the highest overall performance with nearly equivalent performance in Tasseled-Cap models. While performance gains from standard deviations, textural metrics, and additional seasons were minimal in raw-band and Tasseled-Cap models, gains were appreciable in single-index models. Classified land cover models performed equivalently to raw band models. When predictive performance is paramount, means of raw Landsat bands are strong predictors for avian SDMs. When parsimonious variables are essential, SDMs of single indices (e.g., NDVI) greatly benefit from additional information, such as standard deviation.

Predicted distribution of plains spotted skunk in Arkansas and Missouri

AbstractThe plains spotted skunk (Spilogale interrupta) is of conservation concern because of widespread population declines and is being considered for listing under the United States Endangered Species Act. Although the taxon is relatively rare and difficult to study, recent research and reports provided some information about spotted skunks in Arkansas and Missouri, USA. Using this information, we employed full and simplified multivariate species distribution models to evaluate cover type associations and identify regions of high predicted presence for plains spotted skunks. The simple model contained percent forest and percent development within 5 km, land cover category, and distance to water. Percent forest within 5 km was the most important variable based on permutation importance in both models, indicating that plains spotted skunk habitat may persist in contiguous forest at the landscape scale. Regions predicted to have high presence occurred in northern, western, and southern Arkansas and southern Missouri, totaling &gt;300,000 ha. The resulting plains spotted skunk distribution map can be used for research and management efforts in areas of high probability of occurrence, and future statewide survey efforts may validate our results.

Woodland restoration and forest structure affect nightjar abundance in the Ozark Highlands

AbstractChuck‐will's‐widow (Antrostomus carolinensis) and eastern whip‐poor‐will (Antrostomus vociferus) are nightjars in eastern North America that have declined 69% and 67%, respectively, in abundance since 1966, resulting in conservation concerns for these species. We investigated relationships between nightjar abundance and landscape composition, forest structure, and application of tree thinning and prescribed fire because of regional interest in woodland restoration and nightjar conservation. We conducted nocturnal nightjar surveys at 385 points in southern Missouri, USA, in 2014 and 2015 and related counts to pine (Pinusspp.) and hardwood basal area, canopy closure, percent forest cover, and percent of area thinned or burned within 500 m of survey points. We modeled abundance of chuck‐will's‐widow and eastern whip‐poor‐will using time‐removal models that included a detection process and an abundance process within a hierarchical Bayesian framework. We detected 534 eastern whip‐poor‐will and 186 chuck‐will's‐widow during surveys. Our data supported global models that included all 6 vegetation and management variables for both species. Chuck‐will's‐widow abundance was negatively related to hardwood basal area and peaked at intermediate values of percent area burned and percent forest cover. Eastern whip‐poor‐will abundance was negatively related to hardwood basal area and canopy cover, positively related to percent forest cover and percent of area burned, and peaked at low to moderate levels of percent of area thinned. Relationships to forest structure and management activities generally supported the conclusion that woodland restoration benefits nightjars and that chuck‐will's‐widow select landscapes with less forest cover than eastern whip‐poor‐will.

Eurasian lynx density and habitat use in one of Europe’s strongholds, the Romanian Carpathians

Abstract The Eurasian lynx (Lynx lynx) faces population declines in the western part of its range, and its ecological requirements are poorly understood in the eastern part of its range. The Romanian Carpathians harbor an intact large carnivore community, in which lynx co-occur with bears (Ursus arctos), wolves (Canis lupus), and humans (Homo sapiens), with which they potentially compete for ungulate prey. We provide a science-based estimate of lynx density and habitat use, combining non-invasive monitoring techniques (camera trapping) with spatially explicit capture-recapture models (SECR) in the Southern Carpathians of Romania. We sampled 59 and 76 trap stations during two monitoring sessions (winter and autumn), identified at least 30 individuals, from which we reconstructed encounter histories for 23 individuals. SECR modeling resulted in similar density estimates between winter and autumn (1.6 ± 0.39 SE and 1.7 ± 0.38 SE lynx/100 km2, respectively), but the cumulative number of lynx detected reached the asymptote faster during autumn, suggesting that monitoring prior to the mating season is preferable. Density varied within and across sessions with topography (slope), percent forest cover, and landscape heterogeneity (i.e., agricultural mosaic). Density hotspots shifted between low-altitude agricultural mosaic during winter and more rugged, mid-altitude forest stands during autumn. Estimated densities of lynx in the Romanian Carpathians are higher than those reported in the Alps or Slovak Carpathians, highlighting the importance of this population as a source both for natural recolonization and recent reintroduction programs. When used in an SECR framework, camera trapping is an efficient method for assessing spatial and temporal variation in lynx population density in the remote Romanian Carpathians. We recommend this methodology for improving lynx population estimates and to monitor lynx population trends nationwide.

Forest-Associated Fishes of the Conterminous United States

Freshwaters are important, interconnected, and imperiled. Aquatic ecosystems, including freshwater fishes, are closely tied to the terrestrial ecosystems they are embedded within, yet available spatially explicit datasets have been underutilized to determine associations between freshwater fishes and forested areas. Here, we determined the spatial co-occurrence between freshwater fish distributions and forests within 2129 watersheds of the conterminous United States. We identified 21% of freshwater fishes as associated with forested areas, and 2% as strictly present only in highly forested areas (75–100% forested). The northern coasts and southeast regions, both heavily forested, showed the largest numbers of forest-associated fishes in highly forested areas and fish species richness. Fish associated with low-forested areas occurred in the southwest and central plains. Imperiled fishes were relatively evenly distributed among percent forest categories, which was distinctly different from patterns for all fishes. The identification of forest-associated fishes provides insights regarding species-specific landscape contexts. Determining these large-scale patterns of freshwater biodiversity is necessary for conservation planning at regional levels, especially in highly impacted freshwater ecosystems.

The association between forest cover and depressive symptoms in Pennsylvania adolescents

BACKGROUND AND AIM: Exposure to green space, especially dense tree cover, can be beneficial for mental health, but most studies have been conducted with adults. We investigated associations between percent forest cover and depressive symptoms in adolescents in Pennsylvania. METHODS: We assessed depressive symptoms via Public Health Questionnaire-9 scores in 24,985 Geisinger subjects aged 11-18 years (52% females; 93% white; 44% on Medical Assistance) with electronic health records in 2014-16. Percent forest was calculated within a circular residential buffer of radius 463-m (which was converted to a 1250 x 1250 m2 square buffer using “Feature Envelope to Polygon” in ArcGIS)using the most recent release of data (2011) from the U.S. National Land Cover Database with land classes for deciduous, evergreen, and mixed forest and modeled in quartiles. We assessed associations with negative binomial regression models using generalized estimating equations to account for clustering of subjects within communities, adjusted for individual- and community-level covariates. We also used multinomial logistic regression models using generalized estimating equations to evaluate the association between percent forest and clinical depressive score categories of the PHQ-9. RESULTS:The highest quartile percent forest (vs. lowest) was associated with decreased depression symptoms (adjusted exponentiated coefficient = 0.89, 95% confidence interval: 0.83 – 0.96). In categorized models, the highest quartile of percent forest (vs. lowest) was associated with an adjusted odds ratio of 0.80 (95% confidence interval: 0.64, 0.98) for moderately severe to severe depressive symptoms vs. no to minimal depressive symptoms. CONCLUSIONS:Surrounding forest was associated with lower depressive symptoms in a large sample of adolescents. Forest cover could reduce depressive symptoms through promotion of physical activity and reduction of stress. These findings suggest communities should preserve and increase tree cover, a goal that is also consistent with, for example, climate change adaptation for cooling effects and heavy precipitation mitigation. KEYWORDS: mental health outcomes, children's environmental health, health co-benefits

Estimates of Abundance and Harvest Rates of Female Black Bears Across a Large Spatial Extent

ABSTRACTAmerican black bears (Ursus americanus) are an iconic wildlife species in the southern Appalachian highlands of the eastern United States and have increased in number and range since the early 1980s. Given an increasing number of human‐bear conflicts in the region, many management agencies have liberalized harvest regulations to reduce bear populations to socially acceptable levels. Wildlife managers need reliable population data for assessing the effects of management actions for this high‐profile species. Our goal was to use DNA extracted from hair collected at barbed‐wire enclosures (i.e., hair traps) to identify individual bears and then use spatially explicit capture‐recapture methods to estimate female black bear density, abundance, and harvest rate. We established 888 hair traps across 66,678 km2 of the southern Appalachian highlands in Georgia, North Carolina, South Carolina, and Tennessee, USA, in 2017 and 2018, arranged in 174 clusters of 2–9 traps/cluster. We collected 9,113 hair samples from those sites over 6 weeks of sampling, of which 1,954 were successfully genotyped to 462 individual female bears. Our spatially explicit estimator included a percent forest covariate to explain inhomogeneous bear density across the region. Densities ranged up to 0.410 female bears/km2 and regional abundance was 5,950 (95% CI = 4,988–7,098) female bears. Based on hunter kill data from 2016 to 2018, mean annual harvest rates for females were 12.7% in Georgia, 17.6% in North Carolina, 17.6% in South Carolina, and 22.8% in Tennessee. Our estimated harvest rates for most states approached or exceeded theoretical maximum sustainable levels, and population trend data (i.e., bait‐station indices) indicated decreasing growth rates since about 2009. These data suggest that the increased harvest goals and poor hard mast production over a series of prior years reduced bear population abundance in many states. We were able to obtain reasonable population abundance and density estimates because of spatially explicit capture‐recapture methods, cluster sampling, and a large spatial extent. Continued monitoring of bear populations (e.g., annual bait‐station surveys and periodic population estimation using spatially explicit methods) by state jurisdictions would help to ensure that population trajectories are consistent with management goals. © 2021 The Wildlife Society.

Using landscape metrics to characterize towns along an urban-rural gradient

ContextUrban-rural gradients are useful tools when examining the influence of human disturbances on ecological, social and coupled systems, yet the most commonly used gradient definitions are based on single broad measures such as housing density or percent forest cover that fail to capture landscape patterns important for conservation.ObjectivesWe present an approach to defining urban–rural gradients that integrates multiple landscape pattern metrics related to ecosystem processes important for natural resources and wildlife sustainability.MethodsWe develop a set of land cover composition and configuration metrics and then use them as inputs to a cluster analysis process that, in addition to grouping towns with similar attributes, identifies exemplar towns for each group. We compare the outcome of the cluster-based urban-rural gradient typology to outcomes for four commonly-used rule-based typologies and discuss implications for resource management and conservation.ResultsThe resulting cluster-based typology defines five town types (urban, suburban, exurban, rural, and agricultural) and notably identifies a bifurcation along the gradient distinguishing among rural forested and agricultural towns. Landscape patterns (e.g., core and islet forests) influence where individual towns fall along the gradient. Designations of town type differ substantially among the five different typologies, particularly along the middle of the gradient.ConclusionsUnderstanding where a town occurs along the urban-rural gradient could aid local decision-makers in prioritizing and balancing between development and conservation scenarios. Variations in outcomes among the different urban-rural gradient typologies raise concerns that broad-measure classifications do not adequately account for important landscape patterns. We suggest future urban-rural gradient studies utilize more robust classification approaches.