Plecoptera (Insecta) Diversity in Indiana: A Watershed-Based Analysis

Plecoptera, an environmentally sensitive order of aquatic insects commonly used in water quality monitoring is experiencing decline across the globe. This study addresses the landscape factors that impact the species richness of stoneflies using the US Geological Survey Hierarchical Unit Code 8 drainage scale (HUC8) in the state of Indiana. Over 6300 specimen records from regional museums, literature, and recent efforts were assigned to HUC8 drainages. A total of 93 species were recorded from the state. The three richest of 38 HUC8s were the Lower East Fork White (66 species), the Blue-Sinking (58), and the Lower White (51) drainages, all concentrated in the southern unglaciated part of the state. Richness was predicted using nine variables, reduced from 116 and subjected to AICc importance and hierarchical partitioning. AICc importance revealed four variables associated with Plecoptera species richness, topographic wetness index, HUC8 area, % soil hydrolgroup C/D, and % historic wetland ecosystem. Hierarchical partitioning indicated topographic wetness index, HUC8 area, and % cherty red clay surface geology as significantly important to predicting species richness. This analysis highlights the importance of hydrology and glacial history in species richness of Plecoptera. The accumulated data are primed to be used for monograph production, niche modeling, and conservation status assessment for an entire assemblage in a large geographic area.

Spatio-Temporal Characteristics of Dry-Wet Conditions and Boundaries in Five Provinces of Northwest China from 1960 to 2020

The study of dry-wet climate boundaries in the context of climate warming is of great practical significance for improving the environment of ecologically fragile zones and promoting economic and natural sustainable development. In this study, based on the daily meteorological data of 110 stations, using the wetness index, empirical orthogonal function decomposition, regime shift detection test, Fourier power spectrum, and Kriging interpolation, the researchers analyzed the spatiotemporal characteristics of dry-wet conditions and boundaries in five provinces of Northwest China from 1960 to 2020. The results showed that the overall wetness index increased in the past 61 years, but with significant internal differences, among which the western and central climate tended to be warm and wet, and the eastern tended to be warm and dry. The annual wetness index changed abruptly in 1986 with cycles of 3.61 a, 7.11 a and 8.83 a. The mutations occurred correspondingly in spring, summer, autumn, and winter in 1972, 1976, 1983, and 1988, with periods of 3.88 a and 4.92 a, 2.18 a and 2.81 a, 2.15 a, and 2.10 a, respectively. The dry-wet climate boundary has fluctuated markedly since 1960. The extreme arid and arid regions boundary shifted southward and shrank in size until the extreme arid region disappeared in the 2010s. The arid along with semi-arid regions and semi-arid in addition to semi-humid regions boundaries both have two boundary lines, and show the shift of the northwestern boundary to the southeast and the southeastern boundary to the northwest, with the area of the arid together with semi-arid regions shrinking significantly by 5.64%, simultaneously, the area of the semi-humid region area expanding significantly by 84.11%. The boundary of semi-humid and relatively humid regions, and the boundary of relatively humid and humid regions all shifted to the southeast, moreover, the area of the relatively humid region and humid region shrank significantly by 12.08%. The expansion of semi-humid region and the contraction of other climate regions are characteristics of the dry-wet climate variability in five provinces of Northwest China. The area of the three arid climate zones dwindled by 9.61%, and the area of the three humid zones extended by 39.01%. Obviously, the climate inclined to be warm and humid in general.

The spatial distribution of the woodland communities and their associated environmental drivers in the Golden Gate Highlands National Park, South Africa

The extreme variability in the topography, altitude and climatic conditions in the temperate Grassland Mountains of Southern Africa is associated with the complex mosaic of grassland communities with pockets of woodland patches. Understanding the relationships between plant communities and environmental parameters is essential in biodiversity conservation, especially for current and future climate change predictions. This article focused on the spatial distribution of woodland communities and their associated environmental drivers in the Golden Gate Highlands (GGHNP) National Park in South Africa. A generalized linear model (GLM) assuming a binomial distribution, was used to determine the optimal environmental variables influencing the spatial distribution of the woodland communities. The Coefficient of Variation (CV) was relatively higher for the topographic ruggedness index (68.78%), topographic roughness index (68.03), aspect (60.04%), coarse fragments (37.46%) and the topographic wetness index (31.33) whereas soil pH, bulk density, sandy and clay contents had relatively less variation (2.39%, 3.23%, 7.56% and 8.46% respectively). In determining the optimal number of environmental variables influencing the spatial distribution of woodland communities, roughness index, topographic wetness index, soil coarse fragments, soil organic carbon, soil cation exchange capacity and remote-sensing based vegetation condition index were significant (p 0.05) and positively correlated with the woodland communities. Soil nitrogen, clay content, soil pH, fire and elevation were also significant but negatively correlated with the woodland communities. The area under the curve (AUC) of the receiver operating characteristics (ROC) was 0.81. This was indicative of a Parsimonious Model with explanatory predictive power for determination of optimal environmental variables in vegetation ecology.Conservation implications: The isolated woodland communities are sources of floristic diversity and important biogeographical links between larger forest areas in the wider Drakensberg region. They provide suitable habitats for a larger number of forest species and harbour some of the endemic tree species of South Africa. They also provide watershed protection and other important ecosystem services. Understanding the drivers influencing the spatial distribution and persistence of these woodland communities is therefore key to conservation planning in the area.

Analisis Potensi Daerah Rawan Longsor di Kecamatan Bawen dan Tuntang dengan Metode Simple Additive Weighting (SAW)

Intensitas curah hujan yang tinggi pada penghujung tahun 2020 dan awal tahun 2021 tidak hanya menyebabkan beberapa daerah di Indonesia terendam banjir, namun juga tanah longsor. Tanah Longsor dapat terjadi karena adanya pergerakan tanah di musim penghujan serta dipengaruhi kondisi tektonik di Indonesia yang selalu berubah-ubah. Kecamatan Bawen dan Tuntang adalah dua Kecamatan yang berada di Kabupaten Semarang, Jawa Tengah. Kedua daerah tersebut dilanda bencana banjir dan tanah longsor pada April 2020 silam. Kerugian materiil dan moril dari penduduk setempat tentunya tidak dapat dihindari. Untuk melakukan pencegahan sedini mungkin agar dapat menekan kerugian di masa mendatang, perlu dilakukan penelitian mengenai potensi daerah-daerah yang rawan longsor di Kecamatan Bawen dan Tuntang. Analisis yang dilakukan menggunakan metode Simple Additive Weighting (SAW) untuk menghitung persentase suatu daerah terjadi longsor. Adapun parameter yang digunakan antara lain tutupan lahan, kemiringan kelerengan, curah hujan, Soil Adjusted Vegetation Index (SAVI), Normalized Difference Vegetation Index (NDVI), dan Normalized Difference Wetness Index (NDWI). Hasil dari penelitian ini menunjukkan bahwa tingkat kerawanan bencana tanah longsor di Kecamatan Bawen dan Tuntang tergolong “kurang rawan”. Hasil dari penelitian ini diharapkan dapat menjadi dokumen perencanaan tata ruang berbasis mitigasi bencana tanah longsor di Kabupaten Semarang, khususnya pada Kecamatan Bawen dan Tuntang.

Teasing Apart Silvopasture System Components Using Machine Learning for Optimization

Silvopasture systems combine tree and livestock production to minimize market risk and enhance ecological services. Our objective was to explore and develop a method for identifying driving factors linked to productivity in a silvopastoral system using machine learning. A multi-variable approach was used to detect factors that affect system-level output (i.e., plant production (tree and forage), soil factors, and animal response based on grazing preference). Variables from a three-year (2017–2019) grazing study, including forage, tree, soil, and terrain attribute parameters, were analyzed. Hierarchical variable clustering and random forest model selected 10 important variables for each of four major clusters. A stepwise multiple linear regression and regression tree approach was used to predict cattle grazing hours per animal unit (h ha−1 AU−1) using 40 variables (10 per cluster) selected from 130 total variables. Overall, the variable ranking method selected more weighted variables for systems-level analysis. The regression tree performed better than stepwise linear regression for interpreting factor-level effects on animal grazing preference. Cattle were more likely to graze forage on soils with Cd levels <0.04 mg kg−1 (126% greater grazing hours per AU), soil Cr <0.098 mg kg−1 (108%), and a SAGA wetness index of <2.7 (57%). Cattle also preferred grazing (88%) native grasses compared to orchardgrass (Dactylis glomerata L.). The result shows water flow within the landscape position (wetness index), and associated metals distribution may be used as an indicator of animal grazing preference. Overall, soil nutrient distribution patterns drove grazing response, although animal grazing preference was also influenced by aboveground (forage and tree), soil, and landscape attributes. Machine learning approaches helped explain pasture use and overall drivers of grazing preference in a multifunctional system.

Introducing SlideforMap; a probabilistic finite slope approach for modelling shallow landslide probability in forested situations

Worldwide, shallow landslides repeatedly pose a risk to infrastructure and residential areas. To analyse and predict the risk posed by shallow landslides, a wide range of scientific methods and tools to model shallow landslide probability exist for both local and regional scale However, most of these tools do not take the protective effect of vegetation into account. Therefore, we developed SlideforMap (SfM), which is a probabilistic model that allows for a regional assessment of shallow landslide probability while considering the effect of different scenarios of forest cover, forest management and rainfall intensity. SfM uses a probabilistic approach by distributing hypothetical landslides to uniformly randomized coordinates in a 2D space. The surface areas for these hypothetical landslides are derived from a distribution function calibrated from observed events. For each randomly generated landslide, SfM calculates a factor of safety using the limit equilibrium approach. Relevant soil parameters, i.e. angle of internal friction, soil cohesion and soil depth, are assigned to the generated landslides from normal distributions based on mean and standard deviation values representative for the study area. The computation of the degree of soil saturation is implemented using a stationary flow approach and the topographic wetness index. The root reinforcement is computed based on root proximity and root strength derived from single tree detection data. Ultimately, the fraction of unstable landslides to the number of generated landslides, per raster cell, is calculated and used as an index for landslide probability. Inputs for the model are a digital elevation model, a topographic wetness index and a file containing positions and dimensions of trees. We performed a calibration of SfM for three test areas in Switzerland with a reliable landslide inventory, by randomly generating 1000 combinations of model parameters and then maximising the Area Under the Curve (AUC) of the receiver operation curve (ROC). These test areas are located in mountainous areas ranging from 0.5–7.5 km2, with varying mean slope gradients (18–28°). The density of inventoried historical landslides varied from 5–59 slides/km2. AUC values between 0.67 and 0.92 indicated a good model performance. A qualitative sensitivity analysis indicated that the most relevant parameters for accurate modeling of shallow landslide probability are the soil depth, soil cohesion and the root reinforcement. Further, the use of single tree detection in the computation of root reinforcement significantly improved model accuracy compared to the assumption of a single constant value of root reinforcement within a forest stand. In conclusion, our study showed that the approach used in SfM can reproduce observed shallow landslide occurrence at a catchment scale.

The impacts of environmental and socioeconomic factors on the non-point source pollution in a small watershed in the Three Gorges Reservoir Area, China

&lt;p&gt;Determining the impacts of environmental and socioeconomic factors on nitrogen (N) and phosphorus (P) loss in the watershed is critical to reducing non-point source (NPS) pollution. This paper, we set 13 sampling points in the main stream and tributaries of watershed and sampled every two weeks from 2018 to 2020 to monitor the total nitrogen (TN) and total phosphorus (TP) concentration in the waterbodies. Twenty-six potential influencing factors affecting the nitrogen and phosphorus loss in the watershed were selected. The partial least squares regression (PLSR) was used to determine the relationship between TN and TP concentrations in the watershed and the 26 selected potential influencing factors. The results showed that the mean TN concentrations and mean TP concentrations in the dry season (11.42 mg&amp;#183;L&lt;sup&gt;&amp;#8722;1&lt;/sup&gt; and 0.09 mg&amp;#183;L&lt;sup&gt;&amp;#8722;1&lt;/sup&gt;, respectively) were both less than those in the wet season (13.20 mg&amp;#183;L&amp;#8722;1 and 0.11mg&amp;#183;L&amp;#8722;1, respectively). The optimal PLSR model explained 69.6%, 73.1% and 66.1% of the TN concentration variability, and 65.7%, 79.5% and 67.4% of the TP concentration variability during annual, dry season and wet season, respectively. According to the importance of the variables in the predicted value (VIP), topographic wetness index (TWI), planting structure (PS), interspersion and juxtaposition index (IJI), Orchard land use (OP), nitrogen fertilizer application (NF), per capita income (INCOME) and catchment area (AREA) were the key factors affecting TN concentration, whereas topographic wetness index (TWI), interspersion and juxtaposition index (IJI), population density (POP), slope gradient (SLOPE) and hypsometric integral (HI) were the key controlling factors of TP concentration. In addition, TN concentration was affected by cropland land use (CP) during the dry season and proportion of labor (LABOR) and per capita agricultural land area (ALA) during the wet season. TP concentration was affected by mean patch size (AREA_MN), phosphate fertilizer application (PF) and patch density (PD) during the dry season and residential area (RP) and values during the wet season. This study illustrates the impact of environmental and socioeconomic factors on NPS pollution, and can be used as a guide for effective NPS pollution control and water quality management.&lt;/p&gt;

Relic charcoal hearths geomorphology and hydrology in mid-Appalachian region of Pennsylvania

&lt;p&gt;Throughout the northeastern United States and Europe, relic charcoal hearths (RCHs) are more regularly being discovered in proximity to furnaces used for iron or quick-lime production; charcoal was used as a primary fuel source in the furnaces.&amp;#160; RCHs have been found across parts of Europe and Connecticut, USA in different hillslope positions, on vary degrees of slope and aspect, all of which can be a factor affecting the shape of the RCH.&amp;#160; Their usage for charcoal production varied with the time period, furnaces were in operation with some hearths being used once and older ones (such as in Europe) being used multiple times. RCHs across the northcentral Appalachians, USA have been minimally investigated, thus determining where they occur on the landscape, their shape, and their morphologic positions will be useful in discerning their effect on surface hydrology and soil development. Our study focuses on developing a repeatable process for: finding RCHs and quantifying how RCHs may alter surface hydrology. &amp;#160;&lt;/p&gt;&lt;p&gt;We used a combination of processed LiDAR data to create hillshades, and slope gradients to visualize RCHs.&amp;#160; A total of 6,758 hearths have been digitized across three study areas that reflect different historical time periods of construction and environments. We hypothesize that the construction of RCHs can alter the surface hydrology of their surrounding environments. To fully quantify the landscape-level effects of RCHs, a subset of the total was created to fully digitize the RCHs&amp;#8217; area.&amp;#160; The RCH was broken into their rim and platform components.&amp;#160; A topographic wetness index (TWI), and SAGA wetness index (SWI) was created for two study areas in order to quantify surface hydrology effects.&amp;#160; We found that RCH platforms have a significantly higher TWI and SWI than the rim counterparts indicating that the platform is wetter than the RCH outer rims.&amp;#160; Geomorphic position was found to not effect wetness. &amp;#160;Using field measured volumetric water content, we found that as distance from the center of the hearth increases, the drier the soil becomes. Using a combination of GIS flow path analysis, and RCH geometry, standardized ellipses using the axis of local RCHs and the mean area of the total RCHs were created to understand the upslope (control) and downslope (experiment) effects of hearths on the surface hydrology.&amp;#160; Preliminary analysis indicates that downslope positions from RCHs are drier than upslope positions and that there is a significant difference in the relationship between slope position and distance from an RCH and the corresponding TWI and SWI values. Future research will address the effect of slope position and distance to quantify the effect of RHCs on surface hydrology. Furthermore, the soil chemical changes from RCH creation and the increase moisture may increase the habitat for rare species of both plants and animals that otherwise would not be present.&amp;#160; Understanding the extent of the impact human activity can have on various ecosystems can help forest managers, conservationists, pedologists, and climatologists better adapt their management or research pursuits within a specific environment to prepare for future changes, natural or anthropogenic.&lt;/p&gt;

Topographic characteristics of rainfall triggered landslides from a newly compiled set of inventories

&lt;p&gt;Landslides triggered by rainfall or seismic activity are a significant source of loss of life and property damage in mountainous regions. In these settings, it is critical to plan development and infrastructure to avoid impact from landslides. To do so, it is necessary to have a clear understanding of the topographic characteristics of areas both where landslides are initially triggered but also the down-slope areas where debris and bedrock fragments are deposited. Recent research has investigated the characteristics of landslide locations triggered by seismic motion, providing guidelines about the most hazardous parts of a given landscape. In this contribution, we report on a set of analyses conducted on a large compilation of landslide inventories associated with major rainfall events around the world. This compilation includes a number of previously published inventories together with 6 newly mapped inventories of landslides created using high-resolution imagery and machine learning techniques. To our knowledge, together these form the most comprehensive compilation of rainfall triggered landslide inventories gathered to date.&lt;/p&gt;&lt;p&gt;We analyse a number of topographic characteristics associated with these landslides using the 30m resolution SRTM DEM, including local slope, average upstream slope, relief, topographic roughness, wetness index, and topographic position index. We analyse these parameters for both the scar of the landslide as well as the area of deposition. While there is significant dispersion across inventories for several of these parameters, there are consistent relationships between landslide likelihood and roughness, slope, and wetness index. Although the relationships identified with slope and roughness are consistent with prior work, the relationship between wetness index and landslide likelihood suggests that the calculation of wetness index from topography alone may not effectively represent the saturation state of the hillslopes. We anticipate that these findings could be useful for other regional and global landslide modelling studies and local calibration of landslide susceptibility assessment.&lt;/p&gt;