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opencc-by-4.0Dec 2020View details →

[Database] Urban Water Consumption at Multiple Spatial and Temporal Scales. A Review of Existing Datasets

This file contains the complete catalog of datasets and publications reviewed in: Di Mauro A., Cominola A., Castelletti A., Di Nardo A.. Urban Water Consumption at Multiple Spatial and Temporal Scales. A Review of Existing Datasets. Water 2021.The complete catalog contains: <ul> <li>92 state-of-the-art water demand datasets identified at the district, household, and end use scales;</li> <li>120 related peer-reviewed publications;</li> <li>57 additional datasets with electricity demand data at the end use and household scales.</li> </ul> The following metadata are reported, for each dataset: <ul> <li>Authors</li> <li>Year</li> <li>Location</li> <li>Dataset Size</li> <li>Time Series Length</li> <li>Time Sampling Resolution</li> <li>Access Policy.</li> </ul> The following metadata are reported, for each publication: <ul> <li>Authors</li> <li>Year</li> <li>Journal</li> <li>Title</li> <li>Spatial Scale</li> <li>Type of Study: Survey (S) / Dataset (D)</li> <li>Domain: Water (W)/Electricity (E)</li> <li>Time Sampling Resolution</li> <li>Access Policy</li> <li>Dataset Size</li> <li>Time Series Length</li> <li>Location</li> </ul> Authors: Anna Di Mauro - Department of Engineering | Università degli studi della Campania Luigi Vanvitelli (Italy) |  <a href="mailto:anna.dimauro@unicampania.it">anna.dimauro@unicampania.it</a>; Andrea Cominola - Chair of Smart Water Networks | Technische Universität Berlin - Einstein Center Digital Future (Germany) |  <a href="mailto:andrea.cominola@tu-berlin.de">andrea.cominola@tu-berlin.de</a>;  Andrea Castelletti - Department of Electronics, Information and Bioengineering | Politecnico di Milano (Italy) | <a href="mailto:andrea.castelletti@polimi.it">andrea.castelletti@polimi.it</a> Armando Di Nardo -Department of Engineering | Università degli studi della Campania Luigi Vanvitelli (Italy) |   <a href="mailto:armando.dinardo@unicampania.it">armando.dinardo@unicampania.it</a> Citation and reference: If you use this database, please consider citing <a href="https://www.mdpi.com/2073-4441/13/1/36">our paper</a>  Di Mauro, A., Cominola, A., Castelletti, A., & Di Nardo, A. (2021). Urban Water Consumption at Multiple Spatial and Temporal Scales. A Review of Existing Datasets. Water, 13(1), 36, https://doi.org/10.3390/w13010036 Updates and Contributions: The catalogue stored in this public repository can be collaboratively updated as more datasets become available. The authors will periodically update it to a new version.  New requests can be submitted to the authors, so that the dataset collection can be improved by different contributors. Contributors will be cited, step by step, in the updated versions of the dataset catalogue. Updates history: <ol> <li>March 1st, 2021 - Pacheco, C.J.B., Horsburgh, J.S., Tracy, J.R. (Utah State University, Logan, UT - USA) --- The dataset associated with paper <a href="https://doi.org/10.3390/s20133655">Bastidas Pacheco, C.J.; Horsburgh, J.S.; Tracy, R.J.. A Low-Cost, Open Source Monitoring System for Collecting High Temporal Resolution Water Use Data on Magnetically Driven Residential Water Meters. Sensors 2020, 20, 3655.</a> is published in the HydroShare repository, where it is available as an OPEN dataset. Data can be found here: <a href="https://doi.org/10.4211/hs.4de42db6485f47b290bd9e17b017bb51">https://doi.org/10.4211/hs.4de42db6485f47b290bd9e17b017bb51</a></li> </ol>

opencc-by-4.0Sep 2016View details →

Fig. 3 in Influence of environmental variables on stream fish fauna at multiple spatial scales

Fig. 3. Venn diagrams representing the results of the variance partitioning with partial CCA (canonical correspondence analysis): percentage of variation in fish abundance (a) and incidence (b) explained by land use and land cover, site, and spatial variables, as well as that shared between the three sets of variables in the Upper Araguari River basin, Minas Gerais. See Table 4 for a list of all explanatory variables.

opencc-by-4.0Sep 2016View details →

Fig. 1 in Influence of environmental variables on stream fish fauna at multiple spatial scales

Fig. 1. Locations of the 38 randomly selected sites sampled in the Upper Araguari River basin, State of Minas Gerais, Brazil.

opencc-by-4.0Sep 2016View details →

Fig. 2 in Influence of environmental variables on stream fish fauna at multiple spatial scales

Fig. 2. Detrended correspondence analysis (DCA) of fish abundance (a) and incidence (b) along the sampling sites. The species are shown in triangle and sampling sites in X-mark.

FIGURE 4 in The influence of landscape at multiple spatial scales of the river basins at the Eastern Amazon fish assemblage

FIGURE 4 | Venn's diagram showing the exclusive and shared explanation of environmental variables (local, land use, and macroscale) and space in structuring fish assemblages in four catchments of the Eastern Amazon. Local = variation explained by the local matrix (variables of instream habitat); land use = variation explained by the land-use matrix (agriculture, pasture, and urbanization of drainagenetwork buffers and catchment); space = variation explained by the space matrix (environmental filters); macroscale = variation explained by the matrix of macroscale variables (soil characteristics and geographic factor); and residue = variation not explained. * p <0.05.

FIGURE 3 in The influence of landscape at multiple spatial scales of the river basins at the Eastern Amazon fish assemblage

FIGURE 3 | Graphic model showing the environmental and spatial variables that composed the four matrices used in the RDAp.

FIGURE 5 in The influence of landscape at multiple spatial scales of the river basins at the Eastern Amazon fish assemblage

FIGURE 5 | The dbRDA graph displays the distribution of fish data according to the most explanatory (local, macroscale, and land use) variables selected in the distLM model. Environmental variables were chosen by Forward and best model by AIC. Species code: Brachyhypopomus brevirostris (Pbre), Pyrrhulina capim (Pcap), Nannostomus trifasciatus (Ntri), Gymnorhamphichthys rondoni (Gron), Iguanodectes rachovii (Irac), Denticetopsis epa (Dent). Abbreviations: Local variables: EM: Average distance from excavated margins, MA: Average margin angle, SIN: Stretch sinuosity, SF: Smooth flow, LB: Substrate leaf bank, OM: Substrate organic matter, pH, RF: Rapids flow; TD: Average of thalweg depth; SSA: Stream sand. Macroscale variables: CLY: Catchment clay average, CSL: Catchment slope average, SDY: Catchment soil density, CSA: Catchment substrate sand; Land use: UR_S: Urbanization in 30m buffer, PAS_L: Pasture in 60m buffer, AGR_T: agriculture on catchment, PAS_S: Pasture in 30m buffer.

FIGURE 2 in The influence of landscape at multiple spatial scales of the river basins at the Eastern Amazon fish assemblage

FIGURE 2 | Scheme showing different spatial scales from which land use types were quantified in four catchments of the Eastern Amazon.

FIGURE 1 in The influence of landscape at multiple spatial scales of the river basins at the Eastern Amazon fish assemblage

FIGURE 1 | Study area containing 76 streams distributed in four Eastern Amazon basins and land use distribution. Land use: represents all uses (e.g., pasture, agriculture, and urbanization).

opencc-zeroSep 2021View details →

Data and codes to replicate the analysis in: The spatial ecology of conflicts: Unravelling patterns of wildlife damage at multiple scales

Human encroachment into natural habitats is typically followed by conflicts derived from wildlife damages to agriculture and livestock. Spatial risk modelling is a useful tool to gain understanding of wildlife damage and mitigate conflicts. Although resource selection is a hierarchical process operating at multiple scales, risk models usually fail to address more than one scale, which can result in the misidentification of the underlying processes. Here, we addressed the multi-scale nature of wildlife damage occurrence by considering ecological and management correlates interacting from household to landscape scales. We studied brown bear (Ursus arctos) damage to apiaries in the North-eastern Carpathians as our model system. Using generalized additive models, we found that brown bear tendency to avoid humans and the habitat preferences of bears and beekeepers determine the risk of bear damage at multiple scales. Damage risk at fine scales increased when the broad landscape context also favoured damages. Furthermore, integrated-scale risk maps resulted in more accurate predictions than single-scale models. Our results suggest that principles of resource selection by animals can be used to understand the occurrence of damages and help mitigate conflicts in a proactive and preventive manner.

opencc-zeroDec 2022View details →

Behavioral responses of a large, heat-sensitive mammal to climatic variation at multiple spatial scales

1. Climate warming creates energetic challenges for endothermic species by increasing metabolic and hydric costs of thermoregulation. Although endotherms can invoke an array of behavioral and physiological strategies for maintaining homeostasis, the relative effectiveness of those strategies in a climate that is becoming both warmer and drier is not well understood. 2. In accordance with the heat dissipation limit theory, which suggests that allocation of energy to growth and reproduction by endotherms is constrained by the ability to dissipate heat, we expected that patterns of habitat use by large, heat-sensitive mammals across multiple scales are critical for behavioral thermoregulation during periods of potential heat stress and that they must invest a large portion of time to maintain heat balance. 3. To test our predictions, we evaluated mechanisms underpinning the effectiveness of bed sites for ameliorating daytime heat loads and potential heat stress across the landscape while accounting for other factors known to affect behavior. We integrated detailed data on microclimate and animal attributes of moose Alces alces, into a biophysical model to quantify costs of thermoregulation at fine and coarse spatial scales. 4. During summer, moose spent an average of 67.8% of daylight hours bedded, and selected bed sites and home ranges that reduced risk of experiencing heat stress. For most of the day, shade could effectively mitigate the risk of experiencing heat stress up to 10°C, but at warmer temperatures (up to 20℃) wet soil was necessary to maintain homeostasis via conductive heat loss. Consistent selection across spatial scales for locations that reduced heat load underscores the importance of the thermal environment as a driver of behavior in this heat-sensitive mammal. 5. Moose in North America have long been characterized as riparian-obligate species because of their dependence on woody plant species for food. Nevertheless, the importance of dissipating endogenous heat loads conductively through wet soil suggests riparian habitats also are critical thermal refuges for moose. Such refuges may be especially important in the face of a warming climate in which both high environmental temperatures and drier conditions will likely exacerbate limits to heat dissipation, especially for large, heat-sensitive animals.

opencc-zeroJul 2023View details →

Data from: Philopatry influences the genetic population structure of the blacktip shark (Carcharhinus limbatus) at multiple spatial scales

Understanding how interactions among microevolutionary forces generate genetic population structure of exploited species is vital to the implementation of management policies that facilitate population persistence. Philopatry displayed by many coastal shark species can impact gene flow and facilitate selection, and thus has direct implications for the spatial scales of management plans. Here, genetic structure of the blacktip shark (Carcharhinus limbatus) was examined using a mixed-marker approach based on mitochondrial control region sequences and 4,339 SNP-containing loci generated using ddRAD-Seq. Genetic variation was assessed among young-of-the-year sampled in 11 sites in waters of the United States in the western North Atlantic Ocean, including the Gulf of Mexico. Spatial and environmental analyses detected 68 nuclear loci putatively under selection, enabling separate assessments of neutral and adaptive genetic structure. Both mitochondrial and neutral SNP data indicated three genetically distinct units – the Atlantic, eastern Gulf, and western Gulf – that align with regional stocks and suggest regional philopatry by males and females. Heterogeneity at loci putatively under selection, associated with temperature and salinity, was observed among sites within Gulf units, suggesting local adaptation. Furthermore, five pairs of siblings were identified in the same site across timescales corresponding with female reproductive cycles. This indicates that females re-used a site for parturition, which has the potential to facilitate the sorting of adaptive variation among neighboring sites. The results demonstrate differential impacts of microevolutionary forces at varying spatial scales and highlight the importance of conserving essential habitats to maintain sources of adaptive variation that may buffer species against environmental change.

opencc-zeroSep 2023View details →

Early life telomeres are influenced by environments acting at multiple temporal and spatial scales

An individual's telomere length early in life may reflect or contribute to key life history processes sensitive to environmental variation. Yet, the relative importance of genetic and environmental factors in shaping early life telomere length is not well understood as it requires samples collected from multiple generations with known developmental histories. We used a confirmed pedigree and conducted an animal model analysis of telomere lengths obtained from nestling house sparrows (Passer domesticus) sampled over a span of 22 years. We found significant additive genetic variation for early life telomere length, but it comprised a relatively small proportion (9%) of the total biological variation. Three sources of environmental variation were important: among cohorts, among breeding attempts within years and families, and among nestmates. The magnitude of variation among breeding attempts and among nestmates also differed by cohort, suggesting that interactive effects of environmental factors across time or spatial scales were important, yet we were unable to identify the specific causes of these interactions. The mean amount of precipitation during the breeding season positively predicted telomere length, but neither weather during a given breeding attempt nor date in the breeding season contributed to an offspring's telomere length. At the residual level, level of individual nestlings, offspring sex, size, and mass at 10 days of age also did not predict telomere length. Environmental effects appear especially important in shaping early life telomere length in some species, and an array of complex more focus on how environmental factors that interactions across scales may help to explain some of the variation observed among studies.