Mapping Tree Species Deciduousness of Tropical Dry Forests Combining Reflectance, Spectral Unmixing, and Texture Data from High-Resolution Imagery

In tropical dry forests, deciduousness (i.e., leaf shedding during the dry season) is an important adaptation of plants to cope with water limitation, which helps trees adjust to seasonal drought. Deciduousness is also a critical factor determining the timing and duration of carbon fixation rates, and affecting energy, water, and carbon balance. Therefore, quantifying deciduousness is vital to understand important ecosystem processes in tropical dry forests. The aim of this study was to map tree species deciduousness in three types of tropical dry forests along a precipitation gradient in the Yucatan Peninsula using Sentinel-2 imagery. We propose an approach that combines reflectance of visible and near-infrared bands, normalized difference vegetation index (NDVI), spectral unmixing deciduous fraction, and several texture metrics to estimate the spatial distribution of tree species deciduousness. Deciduousness in the study area was highly variable and decreased along the precipitation gradient, while the spatial variation in deciduousness among sites followed an inverse pattern, ranging from 91.5 to 43.3% and from 3.4 to 9.4% respectively from the northwest to the southeast of the peninsula. Most of the variation in deciduousness was predicted jointly by spectral variables and texture metrics, but texture metrics had a higher exclusive contribution. Moreover, including texture metrics as independent variables increased the variance of deciduousness explained by the models from R2 = 0.56 to R2 = 0.60 and the root mean square error (RMSE) was reduced from 16.9% to 16.2%. We present the first spatially continuous deciduousness map of the three most important vegetation types in the Yucatan Peninsula using high-resolution imagery.Arts, Faculty ofGeography, Department ofReviewedFacultyResearche

Mapping Tree Species Deciduousness of Tropical Dry Forests Combining Reflectance, Spectral Unmixing, and Texture Data from High-Resolution Imagery

In tropical dry forests, deciduousness (i.e., leaf shedding during the dry season) is an important adaptation of plants to cope with water limitation, which helps trees adjust to seasonal drought. Deciduousness is also a critical factor determining the timing and duration of carbon fixation rates, and affecting energy, water, and carbon balance. Therefore, quantifying deciduousness is vital to understand important ecosystem processes in tropical dry forests. The aim of this study was to map tree species deciduousness in three types of tropical dry forests along a precipitation gradient in the Yucatan Peninsula using Sentinel-2 imagery. We propose an approach that combines reflectance of visible and near-infrared bands, normalized difference vegetation index (NDVI), spectral unmixing deciduous fraction, and several texture metrics to estimate the spatial distribution of tree species deciduousness. Deciduousness in the study area was highly variable and decreased along the precipitation gradient, while the spatial variation in deciduousness among sites followed an inverse pattern, ranging from 91.5 to 43.3% and from 3.4 to 9.4% respectively from the northwest to the southeast of the peninsula. Most of the variation in deciduousness was predicted jointly by spectral variables and texture metrics, but texture metrics had a higher exclusive contribution. Moreover, including texture metrics as independent variables increased the variance of deciduousness explained by the models from R2 = 0.56 to R2 = 0.60 and the root mean square error (RMSE) was reduced from 16.9% to 16.2%. We present the first spatially continuous deciduousness map of the three most important vegetation types in the Yucatan Peninsula using high-resolution imagery.</jats:p

Landscape-scale forest cover drives the predictability of forest regeneration across the Neotropics

Abandonment of agricultural lands promotes the global expansion of secondary forests, which are critical for preserving biodiversity and ecosystem functions and services. Such roles largely depend, however, on two essential successional attributes, trajectory and recovery rate, which are expected to depend on landscape-scale forest cover in nonlinear ways. Using a multi-scale approach and a large vegetation dataset (843 plots, 3511 tree species) from 22 secondary forest chronosequences distributed across the Neotropics, we show that successional trajectories of woody plant species richness, stem density and basal area are less predictable in landscapes (4 km radius) with intermediate (40–60%) forest cover than in landscapes with high (greater than 60%) forest cover. This supports theory suggesting that high spatial and environmental heterogeneity in intermediately deforested landscapes can increase the variation of key ecological factors for forest recovery (e.g. seed dispersal and seedling recruitment), increasing the uncertainty of successional trajectories. Regarding the recovery rate, only species richness is positively related to forest cover in relatively small (1 km radius) landscapes. These findings highlight the importance of using a spatially explicit landscape approach in restoration initiatives and suggest that these initiatives can be more effective in more forested landscapes, especially if implemented across spatial extents of 1–4 km radius.</jats:p

Landscape-scale forest cover drives the predictability of forest regeneration across the Neotropics

Abandonment of agricultural lands promotes the global expansion of secondary forests, which are critical for preserving biodiversity and ecosystem functions and services. Such roles largely depend, however, on two essential successional attributes, trajectory and recovery rate, which are expected to depend on landscape-scale forest cover in nonlinear ways. Using a multi-scale approach and a large vegetation dataset (843 plots, 3511 tree species) from 22 secondary forest chronosequences distributed across the Neotropics, we show that successional trajectories of woody plant species richness, stem density and basal area are less predictable in landscapes (4 km radius) with intermediate (40–60%) forest cover than in landscapes with high (greater than 60%) forest cover. This supports theory suggesting that high spatial and environmental heterogeneity in intermediately deforested landscapes can increase the variation of key ecological factors for forest recovery (e.g. seed dispersal and seedling recruitment), increasing the uncertainty of successional trajectories. Regarding the recovery rate, only species richness is positively related to forest cover in relatively small (1 km radius) landscapes. These findings highlight the importance of using a spatially explicit landscape approach in restoration initiatives and suggest that these initiatives can be more effective in more forested landscapes, especially if implemented across spatial extents of 1–4 km radius

Appendix S1 ;Table S1;Table S2;Table S3;Table S4 from Landscape-scale forest cover drives the predictability of forest regeneration across the Neotropics

Precipitation information ;Metadata of the 22 databases analyzed in our study. Stem inclusion criterion refers to the diameter at breast height (dbh), except for data from Vieira et al., who used height values. The data used in the analysis from Chazdon and Griffith et al. are for repeated measures over three years, given the small number of plots (n = 6). For these databases, the age range in the chronosequences corresponds to the variation in the last year sampled. S. G. Letcher database include some plots studied by R. L. Chazdon. ;Effect of precipitation on the predictability of successional trajectories. Predictability was measured as the goodness-of-fit (R²adj) of the relationship between each vegetation attribute and forest stand age. ;Effect of precipitation, minimum stem diameter, and plot size on the recovery rate of species richness, density of individuals and basal area along secondary succession. We estimated recovery rate through the equation: [(15 years predicted value – 20 years predicted value)/5], where 5 corresponds to the age interval in years. The predicted values for the fixed age of 15 and 20 years were obtained from the relationship between each vegetation attribute and forest stand age, using generalized additive models.;Effect of landscape forest cover on the predictability of successional trajectories considering differently sized concentric landscapes (i.e. buffers of 1 to 10-km radius, at 1 km intervals). Predictability was measured as the goodness-of-fit (R²adj) of the relationship between each vegetation attribute and forest stand age using generalized additive models. Because the deviance explained by the models was highest when considering landscapes of 4-km radius for all vegetation attributes (in bold), this landscape radius was selected as the scale of forest cover effect (Jackson & Fahrig 2015)

Ethnic and racial differences in children and young people with respiratory and neurological post-acute sequelae of SARS-CoV-2: an electronic health record-based cohort study from the RECOVER InitiativeResearch in context

Summary: Background: Children from racial and ethnic minority groups are at greater risk for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, but it is unclear whether they have increased risk for post-acute sequelae of SARS-CoV-2 (PASC). Our objectives were to assess whether the risk of respiratory and neurologic PASC differs by race/ethnicity and social drivers of health. Methods: We conducted a retrospective cohort study of individuals <21 years seeking care at 24 health systems across the U.S, using electronic health record (EHR) data. Our cohort included those with a positive SARS-CoV-2 molecular, serology or antigen test, or with a COVID-19, multisystem inflammatory disease in children, or PASC diagnosis from February 29, 2020 to August 1, 2022. We identified children/youth with at least 2 codes associated with respiratory and neurologic PASC. We measured associations between sociodemographic and clinical characteristics and respiratory and neurologic PASC using odds ratios and 95% confidence intervals estimated from multivariable logistic regression models adjusted for other sociodemographic characteristics, social vulnerability index or area deprivation index, time period of cohort entry, presence and complexity of chronic respiratory (respectively, neurologic) condition and healthcare utilization. Findings: Among 771,725 children in the cohort, 203,365 (26.3%) had SARS-CoV-2 infection. Among children with documented infection, 3217 children had respiratory PASC and 2009 children/youth had neurologic PASC. In logistic regression models, children <5 years (Odds Ratio [OR] 1.78, 95% CI 1.62–1.97), and of Hispanic White descent (OR 1.19, 95% CI 1.05–1.35) had higher odds of having respiratory PASC. Children/youth living in regions with higher area deprivation indices (OR 1.25, 95% CI 1.10–1.420 for 60–79th percentile) and with chronic complex respiratory conditions (OR 3.28, 95% CI 2.91–3.70) also had higher odds of respiratory PASC. In contrast, older (OR 1.57, 95% CI 1.40–1.77 for those aged 12–17 years), non-Hispanic White individuals and those with chronic pre-existing neurologic conditions (OR 2.04, 95% CI 1.78–2.35) were more likely to have a neurologic PASC diagnosis. Interpretation: Racial and ethnic differences in healthcare utilization for neurologic and respiratory PASC may reflect social drivers of health and inequities in access to care. Funding: National Institutes of Health