Abstract
Humans are unique among primates in altruism and sharing limited recourses towards non-kin. Our study revealed the differences in proportions of individuals ready to share limited resources ...with virtual friend compared to virtual stranger in children and adolescents from seven ethnic groups, represented by four traditional rural African societies from Tanzania with different types of economy and three societies from Russia. The study was conducted between 2015 and 2020, and the data on 2253 individuals (1104 males and 1149 females) were obtained. Six economic games with limited resource allocations were conducted: Prosocial, Envy, and Sharing games with imagined friends and stranger partners accordingly. All players were later classified according to their decisions in all six games into four behavioral types: egoistic, egalitarian, altruistic, and mixed. The effects of population origin, gender, age, and stranger/friend type of interaction on the behavior were estimated by multinomial logistic regression. It was demonstrated that more respondents prefer altruistic and egalitarian behavior than egoistic and mixed in the whole sample. However, significant parochial effect was found. The study revealed significant main effects of ethnicity, age, and the interaction effects of ethnicity and parochial tendencies, and ethnicity and age on the behavior of players.
Productive wetland systems at land–water interfaces that provide unique ecosystem services are challenging to study because of water dynamics, complex surface cover and constrained field access. We ...applied object-based image analysis and supervised classification to four 32-m Beijing-1 microsatellite images to examine broad-scale surface cover composition and its change during November 2007–March 2008 low water season at Poyang Lake, the largest freshwater lake-wetland system in China (>
4000
km
2). We proposed a novel method for semi-automated selection of training objects in this heterogeneous landscape using extreme values of spectral indices (SIs) estimated from satellite data. Dynamics of the major wetland cover types (Water, Mudflat, Vegetation and Sand) were investigated both as transitions among primary classes based on maximum membership value, and as changes in memberships to all classes even under no change in a primary class. Fuzzy classification accuracy was evaluated as match frequencies between classification outcome and a) the best reference candidate class (MAX function) and b) any acceptable reference class (RIGHT function). MAX-based accuracy was relatively high for Vegetation (≥
90%), Water (≥
82%), Mudflat (≥
76%) and the smallest-area Sand (≥
75%) in all scenes; these scores improved with the RIGHT function to 87–100%. Classification uncertainty assessed as the proportion of fuzzy object area within a class at a given fuzzy threshold value was the highest for all classes in November 2007, and consistently higher for Mudflat than for other classes in all scenes. Vegetation was the dominant class in all scenes, occupying 41.2–49.3% of the study area. Object memberships to Vegetation mostly declined from November 2007 to February 2008 and increased substantially only in February–March 2008, possibly reflecting growing season conditions and grazing. Spatial extent of Water both declined and increased during the study period, reflecting precipitation and hydrological events. The “fuzziest” Mudflat class was involved in major detected transitions among classes and declined in classification accuracy by March 2008, representing a key target for finer-scale research. Future work should introduce Vegetation sub-classes reflecting differences in phenology and alternative methods to discriminate Mudflat from other classes. Results can be used to guide field sampling and top-down landscape analyses in this wetland.
► We present wetland object-based analysis and change detection at Poyang Lake, China. ► We propose novel methods to select training objects and analyze class uncertainty. ► Important fuzzy membership changes were detected even with no primary class change. ► Changes in Vegetation class membership reflected grazing and temperature variation. ► The fuzziest Mudflat class was sensitive to flooding and needs finer-scale research.
Remote-sensing methods are being used to study a growing number of issues in the San Francisco Estuary, such as (1) detecting the optical properties of chlorophyll-a concentrations and dissolved ...organic matter to assess productivity and the nature of carbon inputs, (2) creating historical records of invasive aquatic vegetation expansion through space and time, (3) identifying origins and expansions of invasions, and (4) supporting models of greenhouse-gas sequestration by expanding restoration projects. Technological capabilities of remote sensing have likewise expanded to include a wide array of opportunities: from boat-mounted sensors, human-operated low-flying planes, and aerial drones, to freely accessible satellite imagery. Growing interest in coordinating these monitoring methods in the name of collaboration and cost-efficiency has led to the creation of diverse expert teams such as the Remote Imagery Collaborative, and monitoring frameworks such as the Interagency Ecological Program Aquatic Vegetation Monitoring Framework and Wetland Regional Monitoring Program. This paper explores the emerging technologies and applications of various methods for studying primary producers, with an emphasis on remote sensing.
Converting mangroves to other land cover types can induce large emissions of carbon dioxide, depending on the type of land use and land cover (LULC) change. However, mangroves may also recover their ...ecosystem carbon stocks rapidly following restoration, potentially offsetting carbon stock losses. While studies have quantified these tradeoffs at global scales using coarse metrics, fewer studies have quantified them at national scales at higher resolution. Here, we used high‐resolution data sets of LULC for mangroves in Thailand to quantify district‐level gross and net changes in mangrove carbon stocks from ~1960 to 2014. We found emissions based on gross gain and loss statistics (7.18 ± 0.24 million Mg C) to be greater than those associated with emissions based on net area change statistics (1.65 ± 0.26 million Mg C) by a factor of four. The difference in estimates arises from slower rates of carbon stock recovery following reforestation relative to carbon stock loss following LULC change. Overall, we found the greatest gains in mangrove carbon stocks to be from mangrove expansion in areas of accreting sediments, which were strongly correlated with district‐level extent of undisturbed mangroves. Our results show that net loss statistics may greatly underestimate emissions associated with LULC change in mangroves. Additionally, our findings suggest that gains in mangrove carbon stocks associated with natural establishment at the periphery of standing mangroves may offset substantial carbon stock losses at national scales.
Periodically inundated wetlands with high short-term surface variation require special approaches to assess their composition and long-term change. To circumvent high uncertainty in single-date ...analyses of such areas, we propose to characterize them as dynamic cover types (DCTs), or sequences of wetland states and transitions informed by physically and ecologically plausible surface processes. This study delineated DCTs for one 2007–2008 flood cycle at Poyang Lake, the largest freshwater wetland in China, using spatial and temporal orientation modes of extended principal components analysis (EPCA) and supervised object-based classification of multi-spectral and radar image series. Classification accuracy was compared among three sets of attributes selected by machine-learning optimization from object-level mean and standard deviations of: 1) image time series alone; 2) the most informative EPCA outputs alone and 3) image time series and EPCA results together. Classification uncertainty was additionally assessed as low values of object's maximum class membership (<0.5). The highest accuracy was achieved with a larger set of 33 attributes selected from combined time series and EPCA results (overall accuracy 95.0%, kappa 0.94); however, accuracies with smaller sets of variables from input image series or EPCA results alone were comparably high (93.1% and 94.7%, respectively). All three selected attribute sets included standard deviations of image and/or EPCA values, suggesting the utility of object texture in dynamic class discrimination. The highest classification uncertainty was observed primarily along the mapped class boundaries, in some cases indicating minor change trajectories for which prior reference data were not available. Results indicate that DCTs provide a reasonable classification framework for complex and variable Poyang Lake wetlands that can be facilitated by EPCA transformation of complementary remote sensing time series. Future work should test this approach over multiple change cycles and assess sensitivity of results to temporal frequency of input image series, alternative variable selection algorithms and other remote sensors.
•Static land cover classes provide limited information in rapidly varying landscapes.•Dynamic cover types (DCTs) are proposed to characterize landscape change types.•DCTs were analyzed for one flood cycle in a large freshwater wetland in China.•Extended principal components analysis facilitated DCT detection and classification.•Remotely sensed DCTs may facilitate studies and models of variable landscapes.
•NDCI can quantify algal bloom spatiotemporal heterogeneity in small reservoirs.•Google Earth Engine enables efficient algal bloom time series analysis.•Results provide baseline aquatic research for ...the largest dam removal in history.
Freshwater algal blooms have caused ecological damage and public health concerns throughout the world. Monitoring such blooms via in situ sampling is both costly and time-consuming, and satellite imagery provides a rapid and relatively inexpensive way to supplement these techniques. Sentinel-2 MultiSpectral Imager data have effectively detected chlorophyll-a, a proxy for algal biomass, in large bodies of water, but few studies have shown the applicability in small (<10 km2) reservoirs, which are critically important for aquatic species, drinking water, irrigation, cultural activities, and recreation. This study provides a test of the use of Sentinel-2 imagery in Google Earth Engine for algal bloom detection in two small freshwater reservoirs in northern California, USA, from October 2015 to December 2020. Google Earth Engine’s cloud computing allows for the analysis of extensive datasets and time series, expanding the capacity to analyze the spatial and temporal heterogeneity of floating algal blooms. Here we analyzed four spectral indices - Normalized Difference Vegetation Index (NDVI), Normalized Difference Chlorophyll Index (NDCI), B8AB4, and B3B2 - to retrieve chlorophyll-a data for algal bloom identification in two highly dynamic freshwater systems. We assessed the relationship between spectral indices and monthly in situ water samples that were collected at three sites within the reservoirs using cubic polynomial regression equations. NDCI, which leverages the red-edge wavelength, most accurately identified chlorophyll-a across all study sites (highest adjusted R2 = 0.84, lowest RMSE = 0.02 µg/l), followed by NDVI. We demonstrate that Sentinel-2 imagery can capture greater spatial and temporal heterogeneity of algal blooms than typical in situ sampling. This suggests that remote sensing may be an increasingly important tool in monitoring algal bloom dynamics in small reservoirs and other aquatic environments.
The electrochemical sensing of new psychoactive substances, synthetic cannabinoids (SCs), commonly marketed under the trade name “Spice” is explored for the first time. The electrooxidative ...transformations of 11 new indole and indazole SCs which are currently the predominant illicit smoking mixtures on the drug market is performed using cyclic and differential pulse voltammetry with various commercially available electrodes (Pt, GC, Bdd). It is found that SCs exhibit voltammetric responses that can be used for their detection in smoking mixtures and artificial saliva with limits of detection in the nanomolar range. The indole-based SCs exhibited an anodic peak at ∼1.5 V (vs Ag/Ag+) and ∼1.2 V (vs Ag/AgCl) in acetonitrile and artificial saliva, respectively, and the indazoles exhibited corresponding peaks at ∼1.7 V and ∼1.5 V. The voltammetric procedure was evaluated by prescreening of SCs in 12 confiscated street samples that were also independently analyzed by GC-MS and LC-MS techniques. A good agreement between the three analytical protocols was found. Voltammetry provides a tool for the prescreening of synthetic cannabinoid derivatives in seized materials and biological samples.
Plant diversity safeguards wetland ecosystem functions, stability, and resilience, but is threatened by habitat loss and degradation. Remote sensing could support the cost-effective management of ...biodiversity by providing consistent and frequent data at large scales. While identifying individual species from remote sensing datasets with low spatial and spectral resolution is challenging, studies can focus on factors known to correlate with or promote diversity. We tested the predictive potential of such factors — maximum annual greenness as an indicator of productivity, texture (i.e., spatial arrangement of grey tones) as a proxy for habitat heterogeneity, and spatial autocorrelation — across a dataset of 1115 wetlands in the conterminous United States surveyed by the EPA’s National Wetland Condition Assessment. We used multivariate linear regressions to test whether spectral and spatial metrics derived from two open-source datasets — NASA’s Landsat 5 TM and 7 ETM+ (30 m, 16-day revisit) and USDA’s National Agriculture Inventory Program (1 m, biennial) — can predict wetland plant diversity and richness. Individual texture metrics showed different sensitivity to vegetation evenness, growth form, and spatial distribution and could together predict 35–36% of site variation in richness and diversity. This highlights the impact of habitat heterogeneity on species diversity and spectral variability. While maximum annual greenness and texture metrics had similar predictive capacity, their interactions and combined effects improved the fit of linear models by 11–14%, demonstrating their complementarity. Best results were achieved when including distance-based Moran's Eigenvector Maps (dbMEMs) describing spatial relations among sites at multiple scales and reflecting the role of spatially structured factors (e.g., climate, topography, dispersal) on diversity. Together greenness, texture, and dbMEMs could predict 59% of plant richness and 50% of plant diversity across the entire dataset and up to 71% of the richness of least disturbed sites. These results show the potential of open-source remote sensing datasets to monitor biodiversity resources at a large scale and prioritize the protection and field monitoring of wetlands.
The androgen receptor (AR) gene polymorphism in humans is linked to aggression and may also be linked to reproduction. Here we report associations between AR gene polymorphism and aggression and ...reproduction in two small-scale societies in northern Tanzania (Africa)--the Hadza (monogamous foragers) and the Datoga (polygynous pastoralists). We secured self-reports of aggression and assessed genetic polymorphism of the number of CAG repeats for the AR gene for 210 Hadza men and 229 Datoga men (aged 17-70 years). We conducted structural equation modeling to identify links between AR gene polymorphism, aggression, and number of children born, and included age and ethnicity as covariates. Fewer AR CAG repeats predicted greater aggression, and Datoga men reported more aggression than did Hadza men. In addition, aggression mediated the identified negative relationship between CAG repeats and number of children born.
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