Ecology

Commensal rodents serve as important reservoirs of rodent-borne pathogens. Efforts to reduce the risk of pathogen transmission include decimating rodent populations, altering access pathways, upholding good waste management practices and denying easy access to food sources. In our study, we examined the incidence of rodent activity in waste collection premises in public residential estates in Singapore and examined the factors associated with rodent activity to inform the priority of rodent control measures of resource limited municipal estate managers.Of the three types of waste collection premises, rodent activity had the highest incidence in refuse bin centres followed by CRCs and IRC bin chambers. Refuse bin centres are prone to refuse spillage because refuse is manually transferred from refuse collection carts into bulk bins and refuse compactors located within the centres. Bin centres tend to be larger than CRCs and IRC bin chambers and the storage of bulky waste that provide additional areas of rodent harbourage are a common sight in Singapore. IRC chambers and refuse bin centres in combination far outnumber CRCs, and the former two are a distinct characteristic of older public housing estates in Singapore. This suggests that older public housing estates have a higher propensity for rodent infestation compared to newer ones. Aging infrastructure can also provide a greater number of harbourage areas and alternate access pathways for rodent travel that increase their ability to obtain food sources. Our study findings were in support of previous studies which found that older infrastructure was associated with a greater likelihood of rodent activity22,23.We also found that the number of IRC bin chutes was positively associated with rodent infestation. Fluids from food waste in IRC bin chambers are drained directly into a sanitary line that is common to all other bin chambers within the same building. A possible explanation therefore is that rodents which find their way into the sanitary line can easily access all bin chambers in the same building. This suggests that preventing individual bin chamber access may reduce food availability to rodents which traverse the sanitary line in search of food sources.In the present study, we observed that rodent sightings were relatively higher in some months in the first half of the calendar year compared to the second half. Even though our estimates were positive, those for some months were not statistically significant. In Singapore, end-December, January to February are usually associated with increased food production due to the year-end (Christmas and New Year celebrations) and early-year (Chinese New Year) festivities. A proportionate increase in food waste over that period could improve survivability of rodents that leads to increased mating and reproduction. We therefore postulate that the higher seasonal rodent activity is plausible but recommend that future studies be conducted with sufficient longitude to examine the differences in the seasonal pattern across the three categories of premises more closely. A previous study in Harbin, China27 reported a seasonal pattern in the age composition of R. norvegicus while an ecological study on R. norvegicus in Salvador Brazil did not find any difference in the number of rats trapped between the dry and rainy seasons28. The inconsistent seasonal findings between studies could be due to the differences in the climate, degree of urbanization and environmental conditions of study locations.The relative rise in rodent activity in the first half of the year coupled with older estates being at greater risk of rodent activity suggest that municipal town councils which prioritize regular infrastructural repairs and improvements in older estates and complete them in the second half of each calendar year would help mitigate the anticipated rise in the first half of the new calendar year.In our study, we examined the relations between visual cues and rodent activity to help estate managers prioritise their control efforts. We found that rodent droppings were a common positive predictor of rodent activity across all three categories of waste collection premises. In particular, the odds of droppings in IRC bin chambers were the highest among the three categories of premises. We hypothesize that the probability of rodent dropping sightings was in part related to the accessibility of food waste and thus time spent by rodents within the respective waste collection premises. Each IRC chamber contains an open top bin that receives waste that is disposed down the IRC chamber chute. Food waste in IRC bin chambers are thus more easily accessed by rodents compared to in CRCs where waste is stored in a compactor and in bin centres where bulk bins are covered until the waste is compacted or collected.In Salvador, Brazil, the presence of Rattus norvegicus droppings were independently associated with an increased risk of Leptospira infection in humans29. Further research on site-specific Leptospira infection risks in Singapore are required to affirm the utility of droppings as an indicator for Leptospira infection risk. In addition, rub marks and gnaw marks were also positive predictors of rodent activity in CRCs and IRC bin chambers. A study in Chile reported that gnaw marks and holes, as well as grease or rub marks left behind by rodent travel were indicators of rodent activity30. A previous study carried out in an urban city in Taiwan reported that rodent droppings and rub marks were well correlated with rodent infestation31. Our findings, which were in support of these previous studies, suggest that estate managers can maximise the cost effectiveness of their resources by focusing their control efforts based on visual cues without relying solely on trapping activities for surveillance.We found a positive relationship between the number of rodent burrows and rodent activity in all three waste collection premises, though this was only significant for refuse bin centres. That the direction of effect for burrows was consistent these three premises, was a reassuring observation. It is possible that we did not have enough study power to establish the observed positive relations in CRCs and IRC bin chambers. Therefore future studies should seek to confirm our findings. R. norvegicus excavate extensive burrow systems that are able to house a large number of rats32. They exhibit a strong preference for creating burrows in loose soil and on sloping terrain33 and construct shallow burrows in close proximity to water bodies and food sources34. As rodent burrows are primarily used for nesting, food storage and harbourage purposes35, burrows can provide important information about the extent of rodent activity in an area and may be used as an indicator for estate managers to focus their investigations.A previous study in New York, United States found that the presence of numerous restaurants, or having older infrastructure were associated with increased levels of R. norvegicus22. Unexpectedly, we did not find any evidence that the number of dining establishments was associated with rodent activity. However, instances of rodent activity have been reported in food establishments in Singapore36,37,38. We hypothesize that rodent movement is restricted to the surrounding area of the food establishments due to the plethora of food available, with little reason for rodents to venture into waste collection premises. Future studies examining the relationship between rodent activity in food establishments and waste collection premises are required to confirm this.In our study, the presence of gnaw marks (aOR: 5.61), rub marks (aOR: 5.04) in CRCs and rodent droppings in CRCs (aOR: 6.20), IRC bin chambers (aOR: 90.84) and bin centres (aOR: 3.61) had the largest strengths of association with rodent activity. Comparatively, in a study in Johannesburg, South Africa, predictors such as dampness (aOR: 2.54) and cracks (aOR: 1.92) in homes had relatively smaller effects on rodent activity20, while a study in Salvador, Brazil found relatively larger effects of homes with dilapidated fences and walls (aOR: 8.95) and those built on earthen slopes (aOR: 4.95)21. This suggests that rodent activity can be strongly influenced by site- and setting-specific factors, and supports the body of evidence on the strong adaptability of rodents in our urban environment”.Urban environments have the capacity to alter the biology of the pathogens, hosts and vectors, which can influence disease transmission39. The proximate setting of dense urban environments allows for close contact between humans and synanthropic rodents, thereby increasing the transmission risk of zoonotic diseases4. In addition to causing diseases in human populations, urban rats are also known to compromise food safety, damage infrastructure and cause mental health distress25,40. The responsibility of rodent control in residential estates is important but may be one among many other competing public health and estate management responsibilities that municipal town councils have to undertake. Consequently, estate managers have to prioritize their limited resources in order to maximise the cost effectiveness of their resource allocation choices. Based on our study findings, we recommend that estate managers adopt a risk-based approach in vector control resource allocation in waste collection premises according to infrastructural age and visual cues for rodent activity.IRC bin chambers which are a distinct feature of the oldest residential buildings, were observed with a substantially higher odds of rodent activity compared to the other categories of waste collection premises. This suggests that rodent control resource allocation should be prioritized in older residential estates. The clear seasonal pattern of rodent activity in CRCs suggests that estate managers can increase their rodent control activities thereat in the first half of the year. Finally, easy access to food waste directly increases the probability of survival and consequently the rodent population size. Future research should examine the quality of municipal solid waste management and the waste processing flow in residential estates to determine how rodent access to food waste can be further minimized to reduce the population of rodents.Study strengths and limitationsWe analysed data from all public residential estates in Singapore; our findings are thus generalizable at the national level. The use of outcomes and independent measures from individual waste collection premises over multiple cycles of inspection provided stronger evidence for causal inferences. We analysed data over 12 months to account for within-year variations that could influence the outcome measure. Rodents were visually identified without molecular speciation because no trapping was carried out. Though the majority of rodents were observed to be Rattus norvegicus, which is the most common species of rodents in public housing estates in Singapore, we could not rule out misclassification of rodents. However, our findings remain relevant for municipal authorities seeking to prioritize resources for vector control in waste collection premises under their care. More

These authors contributed equally: Arjun Srivathsa, Divya Vasudev, Tanaya Nair, Jagdish Krishnaswamy, Uma Ramakrishnan.National Centre for Biological Science, TIFR, Bengaluru, IndiaArjun Srivathsa, Tanaya Nair, Mahesh Sankaran & Uma RamakrishnanWildlife Conservation Society-India, Bengaluru, IndiaArjun SrivathsaConservation Initiatives, Guwahati, IndiaDivya Vasudev & Varun R. GoswamiDivision of Biosciences, University College London, London, UKTanaya NairDepartments of Biology and Environmental Studies, Macalester College, Saint Paul, MN, USAStotra ChakrabartiWorld Wildlife Fund, Delhi, IndiaPranav Chanchani, Arpit Deomurari, Dipankar Ghose & Prachi ThatteDepartment of Ecology, Evolution and Environmental Biology, Columbia University, New York, NY, USARuth DeFriesAmity Institute of Forestry and Wildlife, Amity University, Noida, IndiaArpit DeomurariWildlife Institute of India, Dehradun, IndiaSutirtha DuttaFoundation for Ecological Research, Advocacy and Learning, Bengaluru, IndiaRajat Nayak & Srinivas VaidyanathanNetwork for Conserving Central India, Gurgaon, IndiaAmrita NeelakantanWorld Resources Institute, New Delhi, IndiaMadhu VermaSchool of Environment and Sustainability, Indian Institute for Human Settlements, Bengaluru, IndiaJagdish KrishnaswamyAshoka Trust for Research in Ecology and the Environment, Bengaluru, IndiaJagdish KrishnaswamyBiodiversity Collaborative, Bengaluru, IndiaJagdish Krishnaswamy, Mahesh Sankaran & Uma Ramakrishnan More

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Study area and field dataThe dataset was generated over NYC, located in the north-eastern United States (40.713° N, 74.006° W). NYC has a total area of 778.2 km2, which is composed of five boroughs, i.e. Brooklyn, Queens, Manhattan, Bronx, and Staten Island (Fig. 1). There were 296 field plots randomly sampled (Fig. 1a) and measured in the summer of 2013 over NYC following the i-Tree Eco protocols9 developed by the United State Department of Agriculture Forest Service (USFS). Each plot occupied a circular area of 404.7 m2. All the trees with a Diameter at Breast Height (DBH) larger than 2.54 cm were surveyed to record their tree height, species, DBH, and other structural attributes. Within all the 296 plots across NYC, there were 1,075 trees in 139 species surveyed. The species types with the top ten largest sample size were Acer platanoides (65 samples), Cedrus species (59 samples), Ailanthus altissima (58 samples), Sassafras albidum (56 samples), Quercus alba (51 samples), Betula lenta (42 samples), Robinia pseudoacacia (39 samples), Acer rubrum (38 samples), and Hardwood species (37 samples). Because the exact coordinates of individual trees were not collected, we mainly used the plot-level tree attributes (i.e. tree number, mean tree height, and maximum tree height) to validate LiDAR-derived products. Due to confidential requirements, the exact coordinates of field plots were not allowed to be released.Fig. 1The distribution of field plots across five regions in New York City (NYC) borough (a). The land cover map over the entire NYC with seven land cover types (b). The summary of identified trees from remotely sensed datasets across five regions (c), and the tree density map over each block group in NYC (d).Full size imageAerial image and land cover mapsA fine-resolution land cover dataset (0.91 m spatial resolution) provided via NYC OpenData (https://opendata.cityofnewyork.us/) was used to mask out non-vegetation areas. This land cover dataset was generated using an object-based image classification method5 from LiDAR data collected in 2010 and NAIP aerial imageries in 2009. This final land cover map includes seven classes, i.e., tree canopy, grass/shrub, bare earth, water, buildings, roads, and other paved surfaces (Fig. 1b). We regrouped the land cover map into vegetation (tree canopy and grass/shrub) and non-vegetation groups, and resampled the map into 1 m resolution to match with NAIP and LiDAR datasets. We also collected NAIP imagery in the summer of 2013 for tree structural estimation from the Google Earth Engine platform36. The NAIP image had a resolution of 1 m with four spectral bands (Red, Green, Blue, and Near Infrared). We further calculated the NDVI from the Red and Near Infrared bands of NAIP images for tree structural estimation.LiDAR data and processingThe LiDAR data were collected using a Leica ALS70 LiDAR system from two flight missions (https://noaa-nos-coastal-lidar-pds.s3.amazonaws.com/laz/geoid18/4920/index.html). The first LiDAR flight was taken on August 5th, 2013 at 2,286 m above ground level with an average side lap of 30%. The LiDAR data from this flight had a nominal pulse spacing of 0.91 m. The second flight was taken between March and April, 2014 at 2,286 m above ground level with an averaged side lap of 25% and a nominal pulse spacing of 0.7 m. According to the ground control survey, the LiDAR scan had a root mean square error accuracy of 9.25 cm. With up to 7 returns per pulse, the final LiDAR dataset has a point density of 5.9 points/m2.The tree structural information was mainly generated from LiDAR-derived CHM. The CHM was the difference between Digital Surface Model (DSM) and Digital Terrain Model (DTM) generated from LiDAR point clouds using the Kriging interpolation method37. All the raster layers (CHM, DSM, DTM) were generated at 1 m resolution using the LiDAR360 software (GreenValley International). We generated a Tree Canopy Cover (TCC) map by masking out non-vegetation land cover types from areas with CHM values larger than 2 m. The TCC was a binary map with the value of one indicating tree cover and zero indicating non-tree cover at 1 m resolution. The non-vegetation areas were derived from the land cover map (Fig. 1b). The 2 m tree canopy height threshold was chosen by referencing a commonly accepted canopy height threshold38.Individual tree segmentation and feature estimationIndividual tree crowns were segmented from LiDAR-derived CHM using the Marker-controlled Watershed Segmentation algorithm. This algorithm was widely adopted for LiDAR-based tree crown segmentation25,26,29 because it takes the advantages of both region-growing and edge-detection methods39. Due to the relatively low LiDAR point density, the CHM contained abnormal pits even after masking out non-tree-canopy pixels. We applied a Gaussian filter with two standard deviations to smooth the CHM and fill these pits in CHM. Then the segmentation was applied with a 3 × 3 moving window. Both smoothing and segmentation were conducted using the System for Automated Geoscientific Analyses software40. To refine the segmentation results, we deleted small segments with an area smaller than 1 m2 (one CHM pixel), which was most likely to be noise in CHM. We also visually examined and manually re-segmented extremely large segments by assuming most tree crowns should not exceed an area of 200 m2. The final tree crown dataset only contains segments with a maximum CHM value no less than 5 m because vegetation with lower height was mostly likely to be non-tree. All the post-segmentation operations were conducted in ESRI ArcMap 10.8.We estimated five tree structural features for each individual trees, which include tree top height, tree mean height, crown area, tree volume, and carbon storage. Tree top height (m) characterizes the height from ground to tree top, estimated as the maximum CHM value within each tree crown segment. Tree mean height (m) indicates the average height of the tree crown surface, calculated as the mean CHM values within each tree segment. Tree crown area (m2) is the total area of each tree crown segment. Tree volume (m3) is the volume of 3D space occupied by the tree crown25, which was calculated as the volume difference between crown surface (defined by CHM) and crown base (Eq. 1). Because the tree crown base height was difficult to estimate for individual trees due to the relatively low LiDAR point density, we used the 2 m to approximate the averaged crown base height according to Ma et al.25. The sensitivities of crown volume to the selection of crown base height from 1 m to 5 m was presented in the Technical Validation section.$$Volume={sum }_{i=1}^{n}left(CHMi-crown;base;heightright)times 1{m}^{2}$$
(1)
Where CHMi is the CHM values of the ith pixels within a tree segment, n is the total number of pixels within a tree segment. 1m2 is the area of each CHM pixel.The carbon storage (ton) was defined as the total carbon stock in both above- and below-ground biomass of each tree. The carbon storage was estimated in two steps: (1) calculating tree biomass from field measurements using allometric equations41; (2) running a regression between field measured carbon storage and LiDAR-derived tree structural features42 and applying the regression model to individual trees. In step (1), we applied species-specific allometric equations from i-Tree Eco database. There are more than 50 species-specific equations in i-Tree Eco, which can be summarised into four main equation forms with different coefficient values (Eqs. 2–5).$$Biomass=exp left({beta }_{1}+{beta }_{2}ast LNleft(DBHright)+frac{{sigma }^{2}}{2}right)$$
(2)
$$Biomass=exp left({beta }_{1}+{beta }_{2}ast LNleft({{rm{DBH}}}^{2}ast {rm{H}}right)+frac{{sigma }^{2}}{2}right)$$
(3)
$$Biomass={beta }_{1}ast left(DB{H}^{{beta }_{2}}right)$$
(4)
$$Biomass={beta }_{1}ast left({left({{rm{DBH}}}^{2}ast {rm{H}}right)}^{{beta }_{2}}right)$$
(5)
Where β1 and β2 are species-specific coefficients, DBH is diameter at breast height, H is tree top height, σ2 is the variance of model errors, which is applied to correct the potential underestimations when back-transforming predictions from logarithmic scale to original scale. For other species that were not included in the i-Tree Eco database, the averaged results from the four equations were applied. These allometric equations (Eqs. 2–5) estimate the entire tree biomass including both above- and below-ground biomass, and the final carbon storage for each field plot was converted to carbon by a factor of 0.541.In step 2), we compared different regression models to simulate carbon storage at plot scale using LiDAR data and NAIP imagery. First, we compared the single variable regression for carbon storage from NAIP-derived NDVI, LiDAR-derived TCC, LiDAR-derived CHMmean and CHMmax, respectively. The four metrics were calculated at 1 m resolution, masked out non-vegetation areas, and aggregated over each field plot. TCC was calculated as the percentage area with tree cover (CHM >2 m). CHMmean and CHMmax were calculated as the mean and maximum of all CHM values within each plot. We compared different regression algorithms, including linear, exponential, and quadratic regressions. We also compared the modelling efficiency and accuracy between using single and multiple variables by combing all the attributes together using the Random Forest regression model. Using the optimal regression model, we generated a carbon density map at 20 m spatial resolution (each pixel size is similar to the plot size of 404.7 m2) by dividing the total carbon storage by the pixel size (400 m2) in the unit of ton/ha (Eq. 6). More details of carbon density estimation can be found in our previous publication42. The carbon storage for each tree was calculated as the product of crown area and crown density (Eq. 7).$$Carbon;densityleft(ton/haright)=0.5ast Biomass(ton)/left(400left({m}^{2}right)ast 0.0001left(ha/{m}^{2}right)right)$$
(6)
$$Carbon;storageleft(tonright)=Crown;arealeft(haright)ast Carbon;density(ton/ha)$$
(7)
Where Biomass is the total biomass for each pixel, which was 400 m2. ha is short for hectare, which is 10000 m2.We further quantified the uncertainty range in carbon storage estimation by propagating the potential error in carbon density regression to tree level carbon storage estimation. We first calculated the 95% confidence interval of the best carbon density regression model, and applied the confidence interval to carbon storage estimation for individual trees. The predicted the upper and lower values for individual tree carbon storage were given in the final dataset and summarized in Table 1.Table 1 A summary of the individual tree carbon storage prediction (Carbon) and their lower (Carbon_lower) and upper (Carbon_upper) values. The minimum (min), maximum (max), mean, standard deviation (std), first quartile (q25), median, and third quartile (q75) values of individual tree carbon storage are presented.Full size tableBlock group level tree structure distribution mappingThree sets of tree structural parameters were mapped at block group level, including tree density (the number of trees in each hectare), tree height (m), and carbon density (ton/ha). The mean values of tree density, tree top height, and carbon density within each block group of NYC. The block group boundary was downloaded from https://www.census.gov/geographies/mapping-files/time-series/geo/tiger-line-file.2014.html, which includes a total of 6392 block groups.We also estimated the potential tree height and carbon density at the census block group level. We assumed the 95% of the tree height and carbon storage values within each block group at mapping time were their potential values, which most trees can achieve during their life time. Then, we calculated the difference in tree height and carbon density between potential values (95%) and mapping time values (mean) over each block group, and used them as the extra carbon storage that trees could achieve during their life time. It is to be noticed that in this study we did not consider the carbon loss by tree degradation or removal, or extra carbon gain through the tree planting and management. More

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