Orginal Article: Land-Use Changes in Southern Appalachian Landscapes: Spatial Analysis and Forecast Evaluation

Land-use dynamics and climatic gradients have large effects on many terrestrial systems. Exurban development, one of the fastest growing forms of land use in the United States, may affect wildlife through habitat fragmentation and building presence may alter habitat quality. We studied the effects of residential development and temperature gradients on bird species occurrence at 140 study sites in the southern Appalachian Mountains (North Carolina, U.S.A.) that varied with respect to building density and elevation. We used occupancy models to determine 36 bird species' associations with building density, forest canopy cover, average daily mean temperature, and an interaction between building density and mean temperature. Responses varied with habitat requirement, breeding range, and migration distance. Building density and mean temperature were both included in the top occupancy models for 19 of 36 species and a building density by temperature interaction was included in models for 8 bird species. As exurban development expands in the southern Appalachians, interior forest species and Neotropical migrants are likely to decline, but shrubland or edge species are not likely to benefit. Overall, effects of building density were greater than those of forest canopy cover. Exurban development had a greater effect on birds at high elevations due to a greater abundance of sensitive forest-interior species and Neotropical migrants. A warming climate may exacerbate these negative effects.

Surveying for flying squirrels by using traditional techniques produces extremely low detection rates compared with ultrasonic acoustics. Within Pennsylvania, the northern flying squirrel subspecies Glaucomys sabrinus macrotis is state listed as endangered due to habitat loss and parasite-mediated competition by and hybridization with the southern flying squirrel Glaucomys volans. This subspecies is isolated from adjacent populations in West Virginia and New York and has experienced drastic population declines. The discovery and characterization of ultrasonic vocalizations of G. s. macrotis and G. volans, as well as successful field surveys with ultrasonic acoustic detectors in the southern Appalachian Mountains, highlight the potential use of this technique for determining the presence of G. s. macrotis. To confirm the feasibility of using this technique on declining populations of G. s. macrotis sympatric with G. volans, we conducted 108 nights of passive ultrasonic acoustic surveys for G. s. macrotis at six survey sites by using two detectors per survey site (N = 12 detectors) in June 2017. We considered sites high quality (“high”) or low quality (“low”) based on the number of physical capture records during the past 2 decades and the dominance of boreo-montane conifer tree species in the overstory. We detected G. s. macrotis at four study sites and G. volans at all six study sites. We found higher average probability of detection for G. s. macrotis in high vs. low sites (0.28 ± 0.06 [mean ± SE] and 0.09 ± 0.07, respectively), whereas probability of detection was similar for G. volans between high and low sites (0.13 ± 0.05 and 0.17 ± 0.05, respectively). We also found G. s. macrotis had lower latency of detection at high vs. low sites (2.7 ± 0.8 and 7.83 ± 1.5 nights, respectively) but G. volans did not vary in latency of detection between sites (5 ± 1.6 and 3.8 ± 1.5 nights, respectively). Our study shows acoustics can be successfully used to efficiently survey G. s. macrotis in Pennsylvania, where populations are small and monitoring these populations more effectively is critical to determining changes in persistence due to climate- and disease-induced factors.

We used drift fences with pitfall traps to compare soricid abundance, richness, and demographic parameters among intact multiple-tree windthrow gaps, salvaged gaps, and mature forest in a xeric southern Appalachian forest type during 1997–1999. We also tested whether capture rates were correlated with rainfall, and whether similar-sized species did not co-occur as predicted by multi-species assemblage rules. We captured six species: Blarina brevicauda (Say) (northern short-tailed shrew), Cryptotis parva (Say) (least shrew), Sorex cinereus Kerr (masked shrew), S. fumeus G.M. Miller (smoky shrew), S. hoyi Baird (pygmy shrew), and S. longirostris Bachman (southeastern shrew). Tree basal area, forest structural features, and arthropod abundance differed among treatments, but species richness and abundance of most shrews did not. Captures during June–October were higher in 1998 than in 1997, although 1998 summer rainfall was low. Rainfall was correlated with shrew activity, but explained little of the variability in capture rates (r2 = 0.05). The sex ratio within each species was similar among gap treatments and controls, but differed from 1:1 for S. hoyi, S. longirostris, and S. fumeus. The soricid assemblage did not conform to multi-species assemblage rules; three small-bodied species co-occurred in similar numbers at all study sites. Our results suggest that forest management that mimics conditions created by multiple windthrows in xeric forest of the southern Appalachians is unlikely to affect shrew communities adversely, at least in the short-term.

The coronavirus disease 2019 (COVID-19) epidemic has had a crucial influence on people’s lives and socio-economic development. An understanding of the spatiotemporal patterns and influencing factors of the COVID-19 epidemic on multiple scales could benefit the control of the outbreak. Therefore, we used spatial autocorrelation and Spearman’s rank correlation methods to investigate these two topics, respectively. The COVID-19 epidemic data reported publicly and relevant open data in Hubei province were analyzed. The results showed that (1) at both prefecture and county levels, the global spatial autocorrelation was extremely significant for the cumulative confirmed COVID-19 cases (CCC) in Hubei province from 30 January to 18 February 2020. Further, (2) at both levels, the significant hotspots and cluster/outlier areas were observed solely in Wuhan city and most of its districts/sub-cities from 30 January to 18 February 2020. (3) At the prefecture level in Hubei province, the number of CCC had a positive and extremely significant correlation (p < 0.01) with the registered population (RGP), resident population (RSP), Baidu migration index (BMI), regional gross domestic production (GDP), and total retail sales of consumer goods (TRS), respectively, from 29 January to 18 February 2020 and had a negative and significant correlation (p < 0.05) with minimum elevation (MINE) from 2 February to 18 February 2020, but no association with the land area (LA), population density (PD), maximum elevation (MAXE), mean elevation (MNE), and range of elevation (RAE) from 23 January to 18 February 2020. (4) At the county level, the number of CCC in Hubei province had a positive and extremely significant correlation (p < 0.01) with PD, RGP, RSP, GDP, and TRS, respectively, from 27 January to 18 February 2020, and was negatively associated with MINE, MAXE, MNE, and RAE, respectively, from 26 January to 18 February 2020, and negatively associated with LA from 30 January to 18 February 2020. It suggested that (1) the COVID-19 epidemics at both levels in Hubei province had evident characteristics of significant global spatial autocorrelations and significant centralized high-risk outbreaks. (2) The COVID-19 epidemics were significantly associated with the natural factors, such as LA, MAXE, MNE, and RAE, -only at the county level, not at the prefecture level, from 2 February to 18 February 2020. (3) The COVID-19 epidemics were significantly related to the socioeconomic factors, such as RGP, RSP, TRS, and GDP, at both levels from 26 January to 18 February 2020. It is desired that this study enrich our understanding of the spatiotemporal patterns and influencing factors of the COVID-19 epidemic and benefit classified prevention and control of the COVID-19 epidemic for policymakers.

Thermal comfort differences between urban villages and formal settlements in Chinese developing cities: A case study in Shenzhen

Raw remotely sensed images contain geometric distortions and cannot be used directly for map-based applications, accurate locational information extraction or geospatial data integration. A geometric correction process must be conducted to minimize the errors related to distortions and achieve the desired location accuracy before further analysis. A considerable number of images might be needed when working over large areas or in temporal domains in which manual geometric correction requires more labor and time. To overcome these problems, new algorithms have been developed to make the geometric correction process autonomous. The Scale Invariant Feature Transform (SIFT) algorithm is an image matching algorithm used in remote sensing applications that has received attention in recent years. In this study, the effects of the incidence angle, surface topography and land cover (LC) characteristics on SIFT-based automated orthorectification were investigated at three different study sites with different topographic conditions and LC characteristics using Pleiades very high resolution (VHR) images acquired at different incidence angles. The results showed that the location accuracy of the orthorectified images increased with lower incidence angle images. More importantly, the topographic characteristics had no observable impacts on the location accuracy of SIFT-based automated orthorectification, and the results showed that Ground Control Points (GCPs) are mainly concentrated in the “Forest” and “Semi Natural Area” LC classes. A multi-thread code was designed to reduce the automated processing time, and the results showed that the process performed 7 to 16 times faster using an automated approach. Analyses performed on various spectral modes of multispectral data showed that the arithmetic data derived from pan-sharpened multispectral images can be used in automated SIFT-based RPC orthorectification.

The aetiology of childhood acute lymphoblastic leukaemia has been linked with spatially heterogeneous environmental exposures. The presence of spatial clustering would be consistent with geographically localized environmental exposures over long periods of time. The present study is the first to examine spatial clustering amongst children aged 0-4years using population-based data from Hungary. The data set consisted of 134 children diagnosed with acute lymphoblastic leukaemia who were resident in part of Hungary during the period 1981-2000. Two levels of spatial aggregation were examined: counties and settlements. The Potthoff-Whittinghill and Moran I autocorrelation methods were used to test for spatial clustering. Additionally, an evaluation of the environmental changes during the study period was considered. Specifically analyses were carried out on sub-periods to investigate a possible effect of the Chernobyl catastrophe. There was statistically significant spatial clustering both at the county (estimate of extra-Poisson variation [Formula: see text], P = 0.04) and settlement levels (estimate of extra-Poisson variation [Formula: see text], P = 0.0003). At county level, the finding was attributable to clustering amongst female cases, but at settlement level, the finding was limited to male cases. There was significant spatial autocorrelation in the sub-periods immediately following the accident (1986-1990 & 1991-1995), but not before 1986, nor after 1995. A significant autocorrelation was observed during the 5year period immediately following the accident (1986-1990, global Moran I = 0.1334, p = 0.005). The centre of significant excesses of ALL cases was located in the county of Baranya. Our study is consistent with an environmental aetiology for acute lymphoblastic leukaemia in children associated with constant exposure to an, as yet unknown, environmental factor in small geographical areas. Although a possible effect of the Chernobyl accident was found in the autocorrelation analysis, the role of chance cannot be excluded.

A supersaturated design is a factorial design in which the number of effects to be estimated is greater than the number of runs. It is used in many experiments, for screening purpose, i.e., for studying a large number of factors and identifying the active ones. In this paper, we propose a method for screening out the important factors from a large set of potentially active variables through the symmetrical uncertainty measure combined with the information gain measure. We develop an information theoretical analysis method by using Shannon and some other entropy measures such as Renyi entropy, Havrda–Charvat entropy, and Tsallis entropy, on data and assuming generalized linear models for a Bernoulli response. This method is quite advantageous as it enables us to use supersaturated designs for analyzing data on generalized linear models. Empirical study demonstrates that this method performs well giving low Type I and Type II error rates for any entropy measure we use. Moreover, the proposed method is more efficient when compared to the existing ROC methodology of identifying the significant factors for a dichotomous response in terms of error rates.

Coupling relationships between urban form and performance of outdoor environment at the pedestrian level

Food webs based on flows of organic matter were developed for two small streams to examine food web response to a large reduction in detrital inputs. At the study site, Coweeta Hydrologic Laboratory in the southern Appalachians, leaf litter inputs and associated microbial assemblages are the main energy source for food webs in headwater streams. We eliminated leaf litter inputs to one stream using a net placed over the first 180 m of stream from its origin. Food webs based on flow of organic matter were developed for a reference stream and the litter-excluded stream for two months, July and December of year 1 of the litter exclusion, to examine effects of leaf litter exclusion on the trophic base of the food web, size distribution of flows, predator–prey interactions, and trophic structure. Flows (mg AFDM·m−2·d−1; AFDM = ash-free dry mass) were estimated using gut content analyses for detritus and prey items, coupled with secondary production estimates. We used a whole-stream 13C tracer method to estimate assimilation of bacteria by invertebrates. The food webs encompassed most (84–91%) of invertebrate secondary production, but <30% of the estimated total links. The primary sources of organic matter for the food web in the reference stream were leaf tissue, bacterial carbon, and animal prey, with ∼25–30% of total secondary production derived from each. In-stream primary production led to <1% of invertebrate secondary production. A higher fraction of food web production in the litter-excluded stream was derived from wood. Magnitudes of detrital flows were lower in the litter-excluded stream, and some taxa were missing compared to the reference stream. The fraction of predator ingestion approached 100% of total secondary production for both streams, but this predation was distributed diffusely among several taxa. Flows to predators were fewer and smaller in the litter-excluded stream, yet these flows had higher per-biomass consumption coefficients, suggesting stronger interactions among the remaining common taxa. These food webs enabled us to examine interactions among taxa in the streams; hence, we found responses of the stream ecosystem to litter-exclusion that we would not have considered had we only measured changes in invertebrate population sizes or system-level changes in organic matter flow.

Southern Appalachian hillslope erosion rates measured by soil and detrital radiocarbon in hollows

Stream capture and the geomorphic evolution of the Linville Gorge in the southern Appalachians, USA

BackgroundLyme disease (LD) is a tick-borne zoonotic illness caused by the bacterium Borrelia burgdorferi. Texas is considered a non-endemic state for LD and the spatial distribution of the state’s reported LD cases is unknown.MethodsWe analyzed human LD cases reported to the Texas Department of State Health Services (TX-DSHS) between 2000 and 2011 using exploratory spatial analysis with the objective to investigate the spatial patterns of LD in Texas. Case data were aggregated at the county level, and census data were used as the population at risk. Empirical Bayesian smoothing was performed to stabilize the variance. Global Moran’s I was calculated to assess the presence and type of spatial autocorrelation. Local Indicator of Spatial Association (LISA) was used to determine the location of spatial clusters and outliers.Results and DiscussionThere was significant positive spatial autocorrelation of LD incidence in Texas with Moran’s I of 0.41 (p = 0.001). LISA revealed significant variation in the spatial distribution of human LD in Texas. First, we identified a high-risk cluster in Central Texas, in a region that is thought to be beyond the geographical range of the main vector, Ixodes scapularis. Second, the eastern part of Texas, which is thought to provide the most suitable habitat for I. scapularis, did not appear to be a high-risk area. Third, LD cases were reported from several counties in western Texas, a region considered unsuitable for the survival of Ixodes ticks.ConclusionsThese results emphasize the need for follow-up investigations to determine whether the identified spatial pattern is due to: clustering of misdiagnosed cases, clustering of patients with an out-of state travel history, or presence of a clustered unknown enzootic cycle of B. burgdorferi in Texas. This would enable an improved surveillance and reporting of LD in Texas.Electronic supplementary materialThe online version of this article (doi:10.1186/s12889-015-2286-0) contains supplementary material, which is available to authorized users.

BackgroundAn improved understanding in transmission variation of kala-azar is fundamental to conduct surveillance and implementing disease prevention strategies. This study investigated the spatio-temporal patterns and hotspot detection for reporting kala-azar cases in Vaishali district based on spatial statistical analysis.MethodsEpidemiological data from the study area during 2007–2011 was used to examine the dynamic space-time pattern of kala-azar outbreaks, and all cases were geocoded at a village level. Spatial smoothing was applied to reduce random noise in the data. Inverse distance weighting (IDW) is used to interpolate and predict the pattern of VL cases distribution across the district. Moran’s I Index (Moran’s I) statistics was used to evaluate autocorrelation in kala-azar spatial distribution and test how villages were clustered or dispersed in space. Getis-Ord Gi*(d) was used to identify the hotspot and cold spot areas within the study site.ResultsMapping kala-azar cases or incidences reflects the spatial heterogeneity in the incidence rate of kala-azar affected villages in Vaishali district. Kala-azar incidence rate map showed most of the highest endemic villages were located in southern, eastern and northwestern part of the district; in the middle part of the district generally show the medium occurrence of VL. There was a significant positive spatial autocorrelation of kala-azar incidences for five consecutive years, with Moran’s I statistic ranging from 0.04-0.17 (P <0.01). The results revealed spatially clustered patterns with significant differences by village. The hotspots showed the spatial trend of kala-azar diffusion (P < 0.01).ConclusionsThe results pointed to the usefulness of spatial statistical approach to improve our understanding the spatio-temporal dynamics and control of kala-azar. The study also showed the north-western and southern part of Vaishali district is most likely endemic cluster region. To employ exact and geographically suitable risk-reduction programmes, apply of such spatial analysis tools should suit a vital constituent in epidemiology research and risk evaluation of kala-azar.

Geomorphic response to historical agriculture at Monument Hill in the Blue Ridge Foothills of Central Virginia