Human Population Density Research Articles

Gullying and landscape evolution: Lavaka in Lac Alaotra, Madagascar shed light on rates of change and non-anthropogenic controls.

Gully evolution remains poorly understood, largely because multidecadal analysis is lacking. Large gullies (called lavaka) that pepper Madagascar's highlands are generally attributed to human impact; but longitudinal data are few, and anthropogenic causation is inferred not verified. We focus on Lac Alaotra, Madagascar's largest lake and wetland, its major rice-growing region, and an ecological hotspot surrounded by fault-controlled steep hills with abundant lavaka. Analysis of historical aerial photographs and recent orthoimagery reveals that the proportion of highly active lavaka has decreased since mid-20th century. At the same time, human population, farming intensity, and livestock density have increased exponentially. This suggests that background factors, including seismicity, are primary drivers of lavaka formation. Although human activities may contribute to erosion overall, land management policies that overemphasize human causation of gullying and neglect background forcing factors (in Madagascar and elsewhere) are unlikely to be effective in erosion mitigation.

Population density and ranging behaviour of a generalist carnivore varies with human population.

Canid species are highly adaptable, including to urban and peri-urban areas, where they can come into close contact with people. Understanding the mechanisms of wild canid population persistence in these areas is key to managing any negative impacts. The resource dispersion hypothesis predicts that animal density increases and home range size decreases as resource concentration increases, and may help to explain how canids are distributed in environments with an urban-natural gradient. In Australia, dingoes have adapted to human presence, sometimes living in close proximity to towns. Using a targeted camera trap survey and spatial capture-recapture models, we estimated spatial variation in the population density and detection rates of dingoes on Worimi Country in the Great Lakes region of the NSW coast. We tested whether dingo home range and population densities varied across a gradient of human population density, in a mixed-use landscape including, urban, peri-urban, and National Park environs. We found human population density to be a strong driver of dingo density (ranging from 0.025 to 0.433 dingoes/km2 across the natural-urban gradient), and to have a negative effect on dingo home range size. The spatial scale parameter changed depending on survey period, being smaller in the peak tourism period, when human population increases in the area, than in adjacent survey periods, potentially indicating reduced home range size when additional resources are available. Our study highlights the potential value of managing anthropogenic resource availability to manage carnivore densities and potential risk of human-carnivore interactions.

The macroecology of butyrate-producing bacteria via metagenomic assessment of butyrate production capacity.

Butyrate-producing bacteria are found in many outdoor ecosystems and host organisms, including humans, and are vital to ecosystem functionality and human health. These bacteria ferment organic matter, producing the short-chain fatty acid butyrate. However, the macroecological influences on their biogeographical distribution remain poorly resolved. Here we aimed to characterise their global distribution together with key explanatory climatic, geographical and physicochemical variables. We developed new normalised butyrate production capacity (BPC) indices derived from global metagenomic (n = 13,078) and Australia-wide soil 16S rRNA (n = 1331) data, using Geographic Information System (GIS) and modelling techniques to detail their ecological and biogeographical associations. The highest median BPC scores were found in anoxic and fermentative environments, including the human (BPC = 2.99) and non-human animal gut (BPC = 2.91), and in some plant-soil systems (BPC = 2.33). Within plant-soil systems, roots (BPC = 2.50) and rhizospheres (BPC = 2.34) had the highest median BPC scores. Among soil samples, geographical and climatic variables had the strongest overall effects on BPC scores (variable importance score range = 0.30-0.03), with human population density also making a notable contribution (variable importance score = 0.20). Higher BPC scores were in soils from seasonally productive sandy rangelands, temperate rural residential areas and sites with moderate-to-high soil iron concentrations. Abundances of butyrate-producing bacteria in outdoor soils followed complex ecological patterns influenced by geography, climate, soil chemistry and hydrological fluctuations. These new macroecological insights further our understanding of the ecological patterns of outdoor butyrate-producing bacteria, with implications for emerging microbially focused ecological and human health policies.

The effects of human population density on trophic interactions are contingent upon latitude

AbstractAimGlobal‐scale studies are necessary to draw general conclusions on how trophic interactions vary with urbanization and to explore how the effects of urbanization change along latitudinal gradients. We predict that the intensity of trophic interactions decreases in response to urbanization (quantified by human population density). Since trophic interactions are more intense at lower latitudes, we also expect major impacts of urbanization at higher latitudes, where base levels are essentially lower.LocationGlobal (881 study sites).Time period2000–2021.Major taxa studiedBirds, arthropods and woody plants.MethodsWe compiled global data on insect herbivory and bird predation from studies that employed similar methods and fitted generalized linear mixed models to test how these trophic interactions vary with human population density, latitude and their interactions.ResultsThe intensity of herbivory and predation decreased with an increase in human population density at lower latitudes. Surprisingly, it remained unaffected at intermediate latitudes and even increased at higher latitudes.Main conclusionsThe observed patterns may be attributed to local climate changes in urban areas, such as the Urban Heat Island effect, which disrupts thermal stability in the tropics while increasing niche availability at polar latitudes.

Prediction of viral spillover risk based on the mass action principle

Infectious zoonotic disease emergence, through spillover events, is of global concern and has the potential to cause significant harm to society, as recently demonstrated by COVID-19. More than 70% of the 400 infectious diseases that emerged in the past five decades have a zoonotic origin, including all recent pandemics. There have been several approaches used to predict the risk of spillover through some of the known or suspected infectious disease emergence drivers, largely using correlative approaches. Here, we predict the spatial distribution of spillover risk by approximating general transmission through animal and human interactions. These mass action interactions are approximated through the multiplication of the spatial distribution of zoonotic virus diversity and human population density. Although our results indicate higher risk in regions along the equator and in Southeast Asia where both virus diversity and human population density are high, it should be noted that this is primarily a conceptual exercise. We compared our spillover risk map to key factors, including the model inputs of zoonotic virus diversity estimate map, human population density map, and the spatial distribution of species richness. Despite the limitations of this approach, this viral spillover map is a step towards developing a more comprehensive spillover risk prediction system to inform global monitoring.

Ensembling machine learning models to identify forest fire-susceptible zones in Northeast India

Forest fires pose significant challenges by disrupting ecological balance, impacting socio-economic harmony, and raising global concerns. North-East India (NEI) experiences high incidences of forest fires, making it crucial to implement suitable management measures considering the driving forces influencing fire likelihood. This study aims to identify forest fire susceptibility zones in NEI by using five machine-learning modeling approaches, Boosted Regression Tree (BRT), Random Forest (RF), Support Vector Machine (SVM), Classification and Regression Tree (CART), and Multivariate Adaptive Regression Splines (MARS), and an ensemble method. Forest fire data from the SNPP – VIIRS sensor (2018–2019) were rectified for spatial autocorrelation. Thirty-two responsive predictor variables related to topographic, climatic, biophysical, and anthropogenic factors were used as model inputs and multicollinearity analysis was performed to eliminate highly correlated predictors. Results indicate that the southern and southeastern regions of NEI, characterized by ample solar radiation, enhanced vegetation index, high human population density, and jhum cultivation, contribute significantly to higher susceptibility to forest fires. The Random Forest model performs best among the models employed, achieving an AUC value of 0.87. The ensemble susceptibility map, binarized based on AUC weighting, covers 29.54% of the total geographic area and 44.42% of the forested area of NEI. The vulnerability levels vary among states, with Mizoram showing the highest susceptibility at 89.27% and Sikkim exhibiting the lowest vulnerability at only 0.49% of their respective geographic areas. This map provides valuable insights for implementing effective forest fire management plans in the region. Moreover, the methodology utilized in this study, which incorporates satellite imagery, GIS techniques, and improved modeling techniques, can be replicated in any geographical region worldwide to facilitate effective forest fire management at a regional to large scale.

Exposure of Peregrine Falcons to Halogenated Flame Retardants: A 30 Year Retrospective Biomonitoring Study across North America.

Compared to aquatic ecosystem, terrestrial systems have been subjected to fewer investigations on the exposure to halogenated flame retardants (HFRs). Our study utilized peregrine falcon eggs collected from multiple habitats across North America to retrospectively explore both spatial distribution and temporal changes in legacy (e.g., polybrominated diphenyl ethers) and alternative HFRs over a 30 year period (1984-2016). The results reveal intensive HFR exposure in terrestrial ecosystems and chemical-specific spatiotemporal distribution patterns. The correlations between egg levels of the selected HFRs and human population density clearly illustrated a significant urban influence on the exposure of this wildlife species to these HFRs and subsequent maternal transfer to their eggs. Temporal analyses suggest that, unlike aquatic systems, terrestrial ecosystems may undergo continual exposure to consistently high levels of legacy HFRs for a long period of time. Our findings collectively highlight the effectiveness of using peregrine eggs to monitor terrestrial exposure to HFRs and other bioaccumulative chemicals and the need for continuous monitoring of HFRs in terrestrial ecosystems.

Diversity and conservation status of palms (Arecaceae) in two hotspots of biodiversity in Colombia and Ecuador

Societal Impact StatementPalms provide vital plant resources and ecosystem services to people across the tropics. To improve conservation guidance, a “health check” of palms in two highly threatened biodiversity hotspots in Colombia and Ecuador was undertaken. Palms are very diverse in these regions, but over one third are threatened with extinction now, especially among endemic species. Widespread and useful palms are also under intense human pressure and need to be prioritized in terms of sustainable management practices. Given the importance of palms for humans, inclusive conservation actions should be continued in both countries in order to safeguard this resource.Summary Palms provide central plant resources to societies in the tropics, especially in the Global South. The western Pacific and Andean regions of Colombia and Ecuador host two hotspots of biodiversity. To prioritize conservation policies towards palms, we undertook a conservation assessment of species in the region. We compiled a taxonomically verified database of specimens collected in both hotspots. We inferred preliminary conservation assessments using International Union for Conservation of Nature (IUCN) Criteria B. In addition, we evaluated the level of exposure of palms to human use and population density using the anthrome concept. We documented 144 native palm species in 33 genera occurring in both hotspots of Colombia and Ecuador. Of these, 55 are endemic to this region. We recorded 133 species for Colombia, 43 endemic, and 71 species for Ecuador, 9 endemic. A third of all palm species in the region are potentially threatened with extinction (50/144) and 12 as preliminary Critically Endangered. Aiphanes and Geonoma have the highest number of threatened species. In total, 60% of palm specimens were collected in the “low human impact” anthrome type. In contrast, 41% of specimens occur in high human density areas. The two hotspots of biodiversity in Colombia and Ecuador are very diverse in palms. However, we show that this diversity is under threat and is predominantly found in areas impacted by human activities. Extinction risk is highest in endemic species in both countries. Widespread and useful palm species also face threats linked to overexploitation or habitat loss. Inclusive conservation measures should be designed to conserve, together with communities, this plant resource.

Environmental and anthropogenic drivers of invasive plant diversity and distribution in the Himalaya

Invasive alien species (IAS) are currently considered as one of the major causes of global environmental change. To manage the IAS, it is crucial to identify the different environmental and associated anthropogenic drivers that contribute to invasion of alien species in non-native regions. Although multiple drivers of invasion have been identified at a global scale, the relative roles of these are known to vary considerably at regional scales. Here, we investigate the role of key environmental and anthropogenic drivers in determining the diversity and distribution of selected invasive alien plant species in Kashmir Himalaya. We generated an extensive distribution dataset of these species through field sampling across the region and supplemented it with novel herbarium records. We also extracted data on the relevant environmental (climatic, soil and topographic) and anthropogenic drivers for the study region. The random forest model was employed to quantify the relative contribution of these drivers to determine the two common diversity metrics (species richness and abundance) of selected invasive alien plants. We found that soil water content followed by distance to city, the maximum air temperature, soil pH, soil temperature and human population density exerted the greatest influence on species richness of the invasive plants. Species abundance was significantly influenced by the maximum air temperature followed by soil temperature, distance to city, slope, soil pH and human population density. Overall, our findings help in disentangling the individual and interactive roles of multiple drivers of plant invasions, with wide-ranging implications for management in this Himalayan region and similar landscapes elsewhere.

Does Inequality Shape Human Character? Cross-Cultural Associations between Character Strengths and the Gini Index in 68 Countries

Environmental factors are crucial in shaping individual characteristics. One key contextual factor is economic inequality, which is increasing in most OECD countries and negatively impacting individuals and societies, including personality traits. To date, no studies examined the relationship between economic inequality and positive personality traits such as character strengths. In a large cross-cultural study (N = 980,807, 68 countries) we investigated the relationship between country-level economic inequality and the level of the 24 character strengths. Across countries, we found consistent, robust evidence that economic inequality is positively linked to 22 character strengths, even after controlling for the Human Development Index, population density, urbanization, and climate of each country. On average, inequality explained 34% of the between-country variance in character strengths. Different alternative explanations for such unexpected effects, including increased competitiveness or resilience in the face of adversity, are discussed. Our research provides novel insights into the importance of environmental factors in shaping human character.

Absence of a water depth signal in archaeal tetraether lipids from surface lake sediments in areas with intensive human activity: A case study from Daihai Lake, Inner Mongolia

Indices based on isoprenoid glycerol dialkyl glycerol tetraethers (isoGDGTs) and hydroxylated GDGTs (OH-GDGTs), such as %Cren, Cren/Cren', and %OH-GDGTs, have been widely used as lake-level proxies in lake sediment cores. However, the interpretation of these indices is potentially complex and more research is needed to determine the contexts in which they can reliably be used for paleohydrological reconstruction. We analyzed the isoGDGTs and OH-GDGTs of multiple sample types (watershed surface soils, lake surface sediments, and sediments from a 70-cm sediment core) from Daihai Lake. Our results show that while the widely-used lake level proxies are not correlated with water depth for the surface sediments, this correlation exists in the samples from the sediment core over the past 60 years. We propose that archaeal GDGTs of modern surface sediments in Daihai Lake may be modified by intensive human activities, including eutrophication and potential terrestrial soil input, which prevents their use for lake-level reconstruction. Data from three other lakes (Qinghai, Lugu, and Yangzong) confirm that lake level proxies based on surface sediments are applicable for Qinghai Lake and Lugu Lake (both of which are in regions with relatively weak human activity), but not for Yangzong Lake (in a region with intensive human activity). Interestingly, the transition zone between Qinghai Lake/Lugu Lake and Daihai Lake/Yangzong Lake corresponds to the Hu Line, which divides China into two parts with contrasting human population densities. We propose that future research should consider these findings when using surface lake sediments for paleoenvironmental calibration in areas with intensive human activity, and that sediment cores may sometimes be more reliable for modern validation studies. Although more studies are needed to verify our conclusions, our findings contribute to the application of archaeal GDGTs to lake-level reconstruction, and they are potentially also significant for the validation of other proxies.

Evaluating the Path to the European Commission’s Organic Agriculture Goal: A Multivariate Analysis of Changes in EU Countries (2004–2021) and Socio-Economic Relationships

This study comprehensively analyzed the dynamic landscape of organic farming in the European Union (EU) from 2004 to 2021, investigating the shifts in dedicated agricultural areas influenced by evolving preferences and the priorities of farmers and consumers. Examining the impact of socio-economic factors, including gross domestic product (GDP) per capita, the human development index (HDI), and human population density, this study established multivariate relationships through country-level analyses based on correlations, principal component analysis, cluster analysis, and panel analysis. Despite a universal increase in the organic agriculture areas across all the EU countries during the study period, the production levels exhibited negative correlations with the human population density, GDP per capita, and HDI. Notably, the Baltic countries and Austria led in organic farming production, while Malta, the Netherlands, Belgium, Ireland, and Luxemburg formed a distinct group in the cluster analysis with less intensive organic agriculture per capita. These insights are crucial for supporting the resilience and sustainability of organic farming as it continues to evolve. Predictions of organic agriculture share for 2030 based on trends evaluated using linear regression in the years 2004–2021 estimated about 12% of utilized agricultural area, which was much lower than the target share of the European Commission at 25%. Predictions based on linear regression showed that achieving the European Green Deal target of a 25% share of organic agriculture in unlikely in most EU countries by 2030. The target is only highly probable to be obtained in Austria, Estonia, and Sweden. The EU countries varied significantly across various indices characterizing organic agriculture, including organic agriculture area share. It should be noted that the study was conducted on the data obtained prior to the outbreak of the war in Ukraine, which could potentially alter the previous trends in the development of organic agriculture in the EU.

Global reduction of snow cover in ski areas under climate change.

Ongoing climate change substantially alters snowfall patterns with severe but diverging consequences for global ski areas. A global assessment as well as the investigation of potential implications for mountain ecosystems is currently lacking. We quantify future trends in natural snow cover days under different climate change scenarios until 2100 in seven major global skiing regions and discuss implications for mountainous biodiversity by analysing how natural snow cover days relate to regional human population density. Within all major skiing regions, snow cover days are projected to decrease substantially under every assessed climate change scenario. Thirteen percent of all current ski areas are projected to completely lose natural annual snow cover and one fifth will experience a reduction of more than 50% by 2071-2100 relative to historic baselines. Future skiable areas will concentrate in less populated areas, towards continental regions and inner parts of the mountain ranges. As skiable areas will be located at greater distances to highly populated areas in the future, we expect an expansion of infrastructure and increasing intervening actions (i.e., artificial snowmaking, slope grooming) to prolong snow duration. Our results are concerning for both the recreational and economic value of skiing as well as for mountain biodiversity since vulnerable high-altitude species might be threatened by space reductions with ski area expansion.

Predicting severe wildlife vehicle crashes (WVCs) on New Hampshire roads using a hybrid generalized additive model

Across the United States, wildlife vehicle crashes (WVCs) are increasing and remain consistently deadly to drivers, despite a downward trend in fatal automobile accidents overall. That said, the factors related to severe WVCs are unclear. With this in mind, we pursued a statistical model to reveal factors associated with WVCs that result in severe injury or death to drivers. We hypothesize that there are statistically significant interactions and non-linear relationships between these factors and severity occurrence. We developed a generalized additive model (GAM) with linear terms, additive terms, and a binary response for severity. We surmise that our fitted model results will quantify the relationship between significant variables and severity occurrence, and ultimately help to develop countermeasures to mitigate serious injury. The model was fitted to WVC records occurring between 2002 and 2019 in the state of New Hampshire. Fitted linear terms revealed: 1) in inclement weather, there is about a 22% increase in the odds of severity for slick surface conditions compared to dry surface conditions; 2) for the warmer months (spring/summer), there is a 42% decrease in the odds of severity for straight roads compared to those with curvature/incline; 3) for highways, the odds of severity decreases by 48% for accidents occurring on NH’s two major intestates highways, and 4) for spring/summer (as compared to the fall/winter), there is more than a 3-fold increase in the odds of severity for two-way traffic. Fitted additive terms revealed: 1) the odds of severity increased in the early hours, between midnight and 6AM, and after 5PM; 2) speeds between 45 and 60 mph are associated with an increase in the odds of a severe accident, while both lower and higher speeds (those below 45 and above 60 mph) are associated with a decrease in the odds of a severe accident; and 3) low, mid-range, and high human population densities are associated with decreases, increases, and decreases in odds of severity, respectively. Cross validation and resulting ROC curves gave evidence that our model is well specified and an effective predictor. Results could be used to inform drivers of potentially dangerous roadways/conditions/times.

Prediction of the size and spatial distribution of free-roaming dog populations in urban areas of Nepal

A factor constraining the elimination of dog-mediated human rabies is limited information on the size and spatial distribution of free-roaming dog populations (FRDPs). The aim of this study was to develop a statistical model to predict the size of free-roaming dog populations and the spatial distribution of free-roaming dogs in urban areas of Nepal, based on real-world dog census data from the Himalayan Animal Rescue Trust (HART) and Animal Nepal. Candidate explanatory variables included proximity to roads, building density, specific building types, human population density and normalised difference vegetation index (NDVI). A multivariable Poisson point process model was developed to estimate dog population size in four study locations in urban Nepal, with building density and distance from nearest retail food establishment or lodgings as explanatory variables. The proposed model accurately predicted, within a 95 % confidence interval, the surveyed FRDP size and spatial distribution for all four study locations. This model is proposed for further testing and refinement in other locations as a decision-support tool alongside observational dog population size estimates, to inform dog health and public health initiatives including rabies elimination efforts to support the ‘zero by 30′ global mission.

Characterization of environmental drivers influencing the abundance of Anopheles maculipennis complex in Northern Italy

BackgroundIn Italy, malaria was endemic until the 1970s, when it was declared eradicated by WHO. Nowadays, with the persistence of competent mosquito populations, the effect of climate change, and increased possibility of importing malaria parasites from endemic counties due to growing migration, a malaria resurgence in Italy has become more likely. Hence, enhancing the understanding of the current distribution of the Anopheles maculipennis complex and the factors that influence the presence of this malaria vector is crucial, especially in Northern Italy, characterised by a high density of both human population and livestock.MethodsTo assess the presence and abundance of malaria vectors, a 4-year field survey in the plain areas of Lombardy and Emilia-Romagna region in Italy was conducted. Every sampling point was characterised in space by the land use in a 500-m radius and in time considering meteorological data collected in the short and long time periods before sampling. We combined the results of a linear regression model with a random forest analysis to understand the relative importance of the investigated niche dimensions in determining Anopheles mosquito presence and abundance.ResultsThe estimated normalised variable importance indicates that rice fields were the most important land use class explaining the presence of Anopheles, followed by transitional woodlands and shrubland. Farm buildings were the third variable in terms of importance, likely because of the presence of animal shelters, followed by urbanised land. The two most important meteorological variables influencing the abundance of Anopheles in our study area were mean temperature in the 24 h before the sampling date and the sum of degree-days with temperature between 18 °C and 30 °C in the 14 days before the sampling date.ConclusionsThe results obtained in this study could be helpful in predicting the risk of autochthonous malaria transmission, based on local information on land cover classes that might facilitate the presence of malaria vectors and presence of short- and medium-term meteorological conditions favourable to mosquito development and activity. The results can support the design of vector control measures through environmental management.Graphical

Capacity and establishment rules govern the number of nonnative species in communities of ground-dwelling invertebrates.

Nonnative species are a key agent of global change. However, nonnative invertebrates remain understudied at the community scales where they are most likely to drive local extirpations. We use the North American NEON pitfall trapping network to document the number of nonnative species from 51 invertebrate communities, testing four classes of drivers. We sequenced samples using the eDNA from the sample's storage ethanol. We used AICc informed regression to evaluate how native species richness, productivity, habitat, temperature, and human population density and vehicular traffic account for continent-wide variation in the number of nonnative species in a local community. The percentage of nonnatives varied 3-fold among habitat types and over 10-fold (0%-14%) overall. We found evidence for two types of constraints on nonnative diversity. Consistent with Capacity rules (i.e., how the number of niches and individuals reflect the number of species an ecosystem can support) nonnatives increased with existing native species richness and ecosystem productivity. Consistent with Establishment Rules (i.e., how the dispersal rate of nonnative propagules and the number of open sites limits nonnative species richness) nonnatives increased with automobile traffic-a measure of human-generated propagule pressure-and were twice as common in pastures than native grasslands. After accounting for drivers associated with a community's ability to support native species (native species richness and productivity), nonnatives are more common in communities that are regularly seasonally disturbed (pastures and, potentially deciduous forests) and those experiencing more vehicular traffic. These baseline values across the US North America will allow NEON's monitoring mission to document how anthropogenic change-from disturbance to propagule transport, from temperature to trends in local extinction-further shape biotic homogenization.

Environmental and sociodemographic factors associated with zoonotic pathogen occurrence in Norway rats (Rattus norvegicus) from Windsor, Ontario.

Rat-associated zoonotic pathogen transmission at the human-wildlife interface is a public health concern in urban environments where Norway rats (Rattus norvegicus) thrive on abundant anthropogenic resources and live in close contact with humans and other animal species. To identify potential factors influencing zoonotic pathogen occurrence in rats, we investigated associations between environmental and sociodemographic factors and Leptospira interrogans and Bartonella spp. infections in rats from Windsor, Ontario, Canada, while controlling for the potential confounding effects of animal characteristics (i.e., sexual maturity and body condition). Between November 2018 and June 2021, 252 rats were submitted by collaborating pest control professionals. Kidney and spleen samples were collected for L. interrogans and Bartonella spp. PCR and sequencing, respectively. Of the rats tested by PCR, 12.7% (32/252) were positive for L. interrogans and 16.3% (37/227) were positive for Bartonella species. Associations between infection status and environmental and sociodemographic variables of interest were assessed via mixed multivariable logistic regression models with a random intercept for social group and fixed effects to control for sexual maturity and body condition in each model. The odds of L. interrogans infection were significantly higher in rats from areas with high building density (odds ratio [OR]: 3.76; 95% CI: 1.31-10.79; p = 0.014), high human population density (OR: 3.31; 95% CI: 1.20-9.11; p = 0.021), high proportion of buildings built in 1960 or before (OR: 11.21; 95% CI: 2.06-60.89; p = 0.005), and a moderate number of reports of uncollected garbage compared to a low number of reports (OR: 4.88; 95% CI: 1.01-23.63; p = 0.049). A negative association was observed between median household income and Bartonella spp. infection in rats (OR: 0.26; 95% CI: 0.08-0.89; p = 0.031). Due to the complexity of the ecology of rat-associated zoonoses, consideration of environmental and sociodemographic factors is of critical importance to better understand the nuances of host-pathogen systems and inform how urban rat surveillance and intervention efforts should be distributed within cities.

Human population density and blue carbon stocks in mangroves soils

Mangrove soils provide many important ecosystem services such as carbon sequestration, yet they are vulnerable to the negative impacts brought on by anthropogenic activities. Research in recent decades has shown a progressive loss of blue carbon in mangrove forests as they are converted to aquaculture, agriculture, and urban development. We seek to study the relationship between human population density and soil carbon stocks in urban mangrove forests to quantify their role in the global carbon budget. To this end, we conducted a global analysis, collecting mangrove soil carbon data from previous studies and calculating population density for each study location utilizing a recent database from the European Commission. Results indicate population density has a negative association with mangrove soil carbon stocks. When human population density reaches 300 people km−2, which is defined as ‘urban domains’ in the European Commission database, mangrove soil carbon is estimated to be lower than isolated mangrove forests by 37%. Nonetheless, after accounting for climatic factors in the model, we see the negative relationship between population density and soil carbon is reduced and is even non-significant in mixed effects models. This suggests population density is not a good measure for the direct effects of humans on mangrove ecosystems and further implies mangrove ecosystems in close proximity to very high population density can still possess valuable carbon stocks. Our work provides a better understanding of how soil carbon stocks in existing mangrove forests correlate with different levels of population density, underscores the importance of protecting existing mangroves and especially those in areas with high human population density, and calls for further studies on the association between human activities and mangrove forest carbon stocks.

Biomonitoring of parabens in wild boars through hair samples analysis.

Parabens are compounds widely utilized in the industry as preservative additives to personal care products, cosmetics and food. They pollute the environment and penetrate to the living organisms through the digestive tract, respiratory system and skin. Till now the knowledge about exposure of terrestrial wild mammals to parabens is extremely scarce. Therefore, this study for the first time assessed the concentration levels of five parabens commonly used in industry (methylparaben-MeP, ethylparaben-EtP propylparaben-PrP, benzylparaben -BeP and butylparaben-BuP). Substances have been analyzed in hair samples collected from wild boars using liquid chromatography-mass spectrometry (LC-MS) method. The hair is a matrix, which allows to study long-term exposure of organisms to parabens. During this study MeP was noted in 96.3% of samples with mean 88.3±72.9 pg/mg, PrP in 87.0% of samples with mean 8.5±3.3 pg/mg, BeP in 44.4% of samples with mean 17.2±12.3 pg/mg and EtP in 11.1% of samples with mean 17.2±4.8 pg/mg. In turn BuP was noted only in 3.7% of samples with concentration levels below limit of quantification (2.6 pg/mg). Statistically significant intragender differences in parabens levels have not been noted. Only BeP concentration levels depended on industrialization and density of human population of area, where the animals lived. This study indicates that wild boars are exposed to parabens, especially to MeP and PrP, and analysis of the hair seems to be a useful tool of biomonitoring of parabens in wild mammals.