Ecological Subregions Research Articles

High-risk driving factors of rain-induced flooding hazard events on the Loess Plateau and its ecological subregions

Rain-induced flooding hazards are prevalent on the Loess Plateau (LP). Descriptive statistics, kernel density estimation, and geographical detector methods were used to explore the spatial and temporal distribution, driving factors, and their high-risk intervals of rain-induced flooding hazard events (RFHEs) on the LP and whether they differ across the entire LP and its ecological subregions. The findings showed that 91 RFHEs occurred mainly in the south-central LP during 2004–2020. The daily rainfall, surface relief amplitude (SRA), elevation, normalized difference vegetation index (NDVI), soil texture, and gross domestic product (GDP) were identified as the driving factors of RFHEs on the LP. However, the driving factors of RFHEs in the Sandy and Agricultural Irrigation Regions (Subregion C), and Earth-rocky Mountainous Region and River Valley Plain Region (Subregion D) all had an added population factor compared to the entire LP, but they lacked NDVI and SRA factors, respectively. The driving factors for the Loess Plateau Gully Region (Subregion A) lacked SRA and soil texture factors. The Loess Hilly and Gully Region (Subregion B) lacked NDVI, soil texture, and population factors. There were also differences between high-risk intervals on the LP and its subregions. The high-risk daily rainfall for the entire LP was 64.5 mm, while it was 64.5, 82.3, 14.7, and 50.0 mm for subregions A, B, C, and D, respectively. Therefore, adopting uniform standards on the LP may over-estimate or under-estimate RFHE occurrence in ecological subregions. These findings contribute to guiding decision-makers involved in ecosystem management and hazard prevention.

What Are the Variation Patterns of Vegetation and Its Influencing Factors in China from 2000–2020 from the Partition Perspective?

China’s vegetation ecosystem has undergone profound changes, and there is an urgent need to explore the mechanisms behind vegetation changes in different ecological sub-regions and historical periods across China. Based on NDVI (normalized difference vegetation index) data, this study analyzed the spatial and temporal evolution patterns and driving mechanisms of six ecological sub-regions in China. The results showed that: (1) over the past 20 years, the vegetation coverage in mainland China showed a decreasing trend from east to west. (2) Over the past 20 years, the vegetation coverage of the six ecological sub-regions showed an increasing trend, with the highest increase in Central Southern China (0.0039) and the lowest increase in East China (0.002). (3) The gravity center of Northeast China showed a trend of migration to the northwest. The gravity center of North China, East China, and Central South China showed a trend of migration to the southwest, while that of Northwest and Southwest China showed a trend of migration to the southeast. (4) During the period from 2000 to 2020, vegetation cover levels showed an upward trend. (5) The lag time of vegetation types in different regions was different. (6) Precipitation was the dominant influencing factor in the evolution of vegetation in Northeast China, North China, Northwest China, and Southwest China. The dominant influencing factors of vegetation evolution in East China were land use and GDP (gross domestic product), while the dominant influencing factors of vegetation evolution in Central South China were precipitation and land use.

Assessment of vegetation net primary productivity variation and influencing factors in the Beijing-Tianjin-Hebei region

Exploring the spatiotemporal variations of vegetation net primary productivity (NPP) and analyzing the relationships between NPP and its influencing factors are vital for ecological protection in the Beijing-Tianjin-Hebei (BTH) region. In this study, we employed the CASA model in conjunction with spatiotemporal analysis techniques to estimate and analyze the spatiotemporal variations of NPP in BTH and different ecological function sub-regions over the past two decades. Subsequently, we established three scenarios (actual, climate-driven and land cover-driven) to assess the influencing factors and quantify their relative contributions. The results indicated that the overall NPP in BTH exhibited a discernible upward trend from 2000 to 2020, with a growth rate of 3.83 gC·m−2a−1. Furthermore, all six sub-regions exhibited an increase. The Bashang Plateau Ecological Protection Zone (BP) exhibited the highest growth rate (5.03 gC·m−2a−1), while the Low Plains Ecological Restoration Zone (LP) exhibited the lowest (2.07 gC·m−2a−1). Geographically, the stability of NPP exhibited a spatial pattern of gradual increase from west to east. Climate and land cover changes collectively increased NPP by 0.04 TgC·a−1 and 0.07 TgC·a−1, respectively, in the BTH region. Climate factors were found to have the greatest influence on NPP variations, contributing 40.49% across the BTH region. This influence exhibited a decreasing trend from northwest to southeast, with precipitation identified as the most influential climatic factor compared to temperature and solar radiation. Land cover change has profound effects on ecosystems, which is an important factor on NPP. From 2000 to 2020, 15.45% area of the BTH region underwent land cover type change, resulting in a total increase in NPP of 1.33 TgC. The conversion of grass into forest brought about the 0.89 TgC increase in NPP, which is the largest of all change types. In the area where land cover had undergone change, the land cover factor has been found to be the dominant factor influencing variations in NPP, with an average contribution of 49.37%. In contrast, in the south-central area where there has been no change in land cover, the residual factor has been identified as the most influential factor influencing variations in NPP. Our study highlights the important role of land cover change in influencing NPP variations in BTH. It also offers a novel approach to elucidating the influences of diverse factors on NPP, which is crucial for the scientific assessment of vegetation productivity and carbon sequestration capacity.

Spatio-temporal Variation in NEP in Ecological Zoning on the Loess Plateau and Its Driving Factors from 2000 to 2021

Net ecosystem productivity (NEP) is an important index for the quantitative evaluation of carbon sources and sinks in terrestrial ecosystems. Based on MOD17A3 and meteorological data, the vegetation NEP was estimated from 2000 to 2021 in the Loess Plateau (LP) and its six ecological subregions of the LP (loess sorghum gully subregions:A1, A2; loess hilly and gully subregions:B1, B2; sandy land and agricultural irrigation subregion:C; and earth-rock mountain and river valley plain subregion:D). Combined with the terrain, remote sensing, and human activity data, Theil-Sen Median trend analysis, correlation analysis, multiple regression residual analysis, and geographic detector were used, respectively, to explore the spatio-temporal characteristics of NEP and its response mechanism to climate, terrain, and human activity. The results showed that:① On the temporal scale, from 2000 to 2021 the annual mean NEP of the LP region (in terms of C) was 104.62 g·(m2·a)-1. The annual mean NEP for both the whole LP and each of the ecological subregions showed a significant increase trend, and the NEP of the LP increased by 6.10 g·(m2·a)-1 during the study period. The highest growth rate of the NEP was 9.04 g·(m2·a)-1, occurring in the A2 subregion of the loess sorghum gully subregions. The subregion C had the lowest growth rate of 2.74 g·(m2·a)-1. Except for the C subregion, all other ecological subregions (A1, A2, B1, B2, and D) were carbon sinks. ② On the spatial scale, the spatial distribution of annual NEP on the LP was significantly different, with the higher NEP distribution in the southeast of the LP and the lower in the northwest of the LP. The high carbon sink area was mainly distributed in the southern part of the loess sorghum gully subregions, and the carbon source area was mainly distributed in the northern part of the loess sorghum gully subregions and most of the C subregion. The high growth rate was mainly distributed in the central and the southern part of the A2 subregion and the southwest part of the B2 subregion. ③ Human activities had the greatest influence on the temporal variation in NEP in the LP and all the ecological subregions, with the correlation coefficient between human activity data and NEP being above 0.80, and the relative contribution rates of human factors was greater than 50%. The spatial distribution was greatly affected by meteorological factors, among which the precipitation and solar radiation were the main factors affecting the spatial changes in the NEP of the LP. The temporal and spatial variations in the NEP in the LP were influenced by natural and human social factors. To some extent, these results can provide a reference for the terrestrial ecosystem in the LP to reduce emissions and increase sinks and to achieve the goal of double carbon.

Ecological problems and ecological restoration zoning of the Aral Sea

The Aral Sea was the fourth largest lake in the world but it has shrunk dramatically as a result of irrational human activities, triggering the “Aral Sea ecological crisis”. The ecological problems of the Aral Sea have attracted widespread attention, and the alleviation of the Aral Sea ecological crisis has reached a consensus among the five Central Asian countries (Kazakhstan, Uzbekistan, Tajikistan, Kyrgyzstan, and Turkmenistan). In the past decades, many ecological management measures have been implemented for the ecological restoration of the Aral Sea. However, due to the lack of regional planning and zoning, the results are not ideal. In this study, we mapped the ecological zoning of the Aral Sea from the perspective of ecological restoration based on soil type, soil salinity, surface water, groundwater table, Normalized Difference Vegetation Index (NDVI), land cover, and aerosol optical depth (AOD) data. Soil salinization and salt dust are the most prominent ecological problems in the Aral Sea. The Aral Sea was divided into seven first-level ecological restoration subregions (North Aral Sea catchment area in the downstream of the Syr Darya River (Subregion I); artificial flood overflow areas downstream of the Aral Sea (Subregion II); physical/chemical remediation area of the salt dust source area in the eastern part of the South Aral Sea (Subregion III); physical/chemical remediation areas of severe salinization in the central part of the South Aral Sea (Subregion IV); existing water surface and potential restoration areas of the South Aral Sea (Subregion V); Aral Sea vegetation natural recovery area (Subregion VI); and vegetation planting areas with light salinity in the South Aral Sea (Subregion VII)) and 14 second-level ecological restoration subregions according to the ecological zoning principles. Implementable measures are proposed for each ecological restoration subregion. For Subregion I and Subregion II with lower elevations, artificial flooding should be carried out to restore the surface of the Aral Sea. Subregion III and Subregion IV have severe soil salinization, making it difficult for vegetation to grow. In these subregions, it is recommended to cover and pave the areas with green biomatrix coverings and environmentally sustainable bonding materials. In Subregion V located in the central and western parts of the South Aral Sea, surface water recharge should be increased to ensure that this subregion can maintain normal water levels. In Subregion VI and Subregion VII where natural conditions are suitable for vegetation growth, measures such as afforestation and buffer zones should be implemented to protect vegetation. This study could provide a reference basis for future comprehensive ecological management and restoration of the Aral Sea.

Eco-Asset Variations and Their Driving Factors in the Qinghai–Tibet Plateau, China, under the Context of Global Change

The Qinghai–Tibet plateau (QTP), as the “roof of the world” and the “Asian Water Tower”, provides important ecological resources for China and other Asian countries. The changing trend of ecological assets and their dominant influencing factors in different sub-regions and periods are not yet clear. In order to reveal the differences in driving mechanisms among sub-regions under the context of global changes, this study quantitatively analyzed the ecological assets and their spatial and temporal evolution patterns during 2000–2015 by using the value equivalent method. Then, the Geodetector was introduced to reveal and clarify the dominant factors of ecological asset changes in different ecological sub-regions. The results show the following. (1) From 2000 to 2010, the total value of ecological assets in Nakchu County was the highest, followed by Kangding County, while that in 2015 was the highest in Kangding County, followed by Nakchu County. (2) During 2000–2015, the average value of ecological assets of the Qinghai–Tibet plateau gradually decreased from east to west, while the average ecological asset value in the southern Qinghai–Tibet plateau was lower. (3) The QTP showed the highest value in 2005 with an increasing trend from 2000 to 2005, followed by a subsequent decrease from 2005 to 2015. (4) Between 2000 and 2015, the area of the stable zone (slight or no change) of ecological assets was the largest, followed by that of the decreasing zone. (5) During all the study period, the spatio-temporal evolution of ecological assets in different ecological sub-regions was mainly affected by natural factors, which were the main driving variables rather than human activities. These results could provide important support for decisions regarding the protection of ecosystems and resources in the Qinghai–Tibet plateau.

Timing and extent of forest disturbance in the Laurentian Mixed Forest

An analysis of United States national forest inventory observations in the Laurentian Mixed Forest reveals a marked increase in forest disturbance between 1999 and 2015. The Laurentian Mixed Forest Province ecological subregion spans the northern sections of Michigan, Minnesota, and Wisconsin and includes forest area of between 16.7 and 17.5 million hectares depending on the year. Forest disturbance ranges from a low of 0.13 million hectares (0.8% of forest area) in 2000 to a high of 2.09 million hectares (11.9% of forest area) in 2014. The year 2015 is notable for being the first year since 2000 where forest disturbance declines, albeit modestly (11.4% of forest area). The marked increase is attributable to disturbances occurring continuously over time between remeasurement. Disturbances with the highest annual averages are insect damage to trees, disease damage to trees, and deer/ungulate at 291 thousand, 189 thousand, and 126 thousand hectares per year, respectively. Disturbances occurring in a specific year, what we term discrete disturbances, show no discernible trend during the period. The most extensive discrete disturbances are wind in 1999, 2011, and 2012 at 108 thousand, 62 thousand, and 61 thousand hectares, respectively. Standard estimates from national forest inventory lack specificity as to the actual year of the disturbance. The estimates reported here are actual annual estimates of disturbance that apply estimation methods accounting for the retrospective nature of the disturbance observation. The timing (year) and location (ecological section) of the two most extensive wind events coincide with historical records.

A machine learning analysis of drought and rural population change on the North American Great Plains since the 1970s.

Machine learning techniques have to date not been widely used in population-environment research, but represent a promising tool for identifying relationships between environmental variables and population outcomes. They may be particularly useful for instances where the nature of the relationship is not obvious or not easily detected using other methods, or where the relationship potentially varies across spatial scales within a given study unit. Machine learning techniques may also help the researcher identify the relative strength of influence of specific variables within a larger set of interacting ones, and so provide a useful methodological approach for exploratory research. In this study, we use machine learning techniques in the form of random forest and regression tree analyses to look for possible connections between drought and rural population loss on the North American Great Plains between 1970 and 2020. In doing so, we analyzed four decades of population count data (at county-size spatial scales), monthly climate data, and Palmer Drought Severity Index scores for Canada and the USA at multiple spatial scales (regional, sub-regional, national, and county/census division levels), along with county level irrigation data. We found that in some parts of Saskatchewan and the Dakotas − particularly those areas that fall within more temperate/less arid ecological sub-regions − drought conditions in the middle years of the 1970s had a significant association with rural population losses. A similar but weaker association was identified in a small cluster of North Dakota counties in the 1990s. Our models detected few links between drought and rural population loss in other decades or in other parts of the Great Plains. Based on R-squared results, models for US portions of the Plains generally exhibited stronger drought-population loss associations than did Canadian portions, and temperate ecological sub-regions exhibited stronger associations than did more arid sub-regions. Irrigation rates showed no significant influence on population loss. This article focuses on describing the methodological steps, considerations, and benefits of employing this type of machine learning approach to investigating connections between drought and rural population change.Supplementary InformationThe online version contains supplementary material available at 10.1007/s11111-022-00399-9.

Priority areas for protection of plant-pollinator interaction networks in the Atlantic Forest

Quantitative methods of prioritization are necessary to optimize the selection of protected areas for biodiversity conservation. Reserve selection is traditionally based on single species, considers representative habitats or, occasionally, spatial configuration but mostly the needs of the society. However, protecting particular species as independent entities is not enough to ensure effective conservation of ecological communities, since their functioning depends on the interactions between species. We propose a strategy to identify priority areas for protection based on species interaction networks. Similar local networks are grouped according to two different sets of network features: interacting species pairs and overall network structure. These groups or clusters of networks are used to delimitate ecological subregions, which are then compared to current nature reserves. Subregions with a lower proportion of protected area are given higher priority. Results from species pairs and network structure are finally combined to obtain the network protection priority index. We present a case study applying this strategy to the Brazilian Atlantic Forest, using plant-pollinator networks. We found that subregions based on network structure show a more grainy pattern, and approach spatial patterns related to forest formation types, while subregions based on species pairs show more distinct patches and a higher level of detail in the division, especially for interior forests. Highest priority is given to portions of the seasonal semi-deciduous and deciduous forest, especially NE São Paulo, NW Paraná, N Rio Grande do Sul and E Minas Gerais and, secondarily, W São Paulo and the São Francisco region. The approach we suggest here goes beyond the level of species, seeking to perpetuate the ecological interactions and networks that make up biological communities. It is our hope and conviction that this strategy contributes to the development of more effective conservation planning.

Determining the contributions of climate change and human activities to the vegetation NPP dynamics in the Qinghai-Tibet Plateau, China, from 2000 to 2015.

Reflecting on the change in the global biodiversity pattern, the Tibetan Plateau, considered to be a "natural laboratory" for analyzing environmental change in China and around the world, has suffered profound changes in the vegetation ecosystem. This study introduces the gravity center model and geographical detectors to examine and discuss the spatial-temporal change pattern and the driving mechanism behind vegetation net primary production (NPP) in the Qinghai-Tibet Plateau from the year 2000 to 2015 while also quantitatively classifying the relative roles incorporated in the NPP change process. The study found that (1) from 2000 to 2015, the annual average NPP of the Tibetan Plateau demonstrated a declining trend from southeast to northwest. (2) The gravity center of vegetation NPP on the Qinghai-Tibet Plateau seems to have shifted eastward in the past 16 years, indicating that the level of vegetation NPP in the east depicts a greater increment and growth rate than the west. (3) In the arid regions, temperature and rainfall appear as the dominant factors for vegetation NPP, while slope and aspect parameters have constantly assumed dominancy for the same in the tropical rainforest-monsoon ecological zone in southeastern Tibet. (4) The structure of vegetation NPP exhibits an interaction between human and natural factors, which enhances the influence of single factors. (5) Considering the global ecological change and related human activities, certain differences are observed in the dominant and interaction factors for different study periods and ecological subregions in the Qinghai-Tibet Plateau. The research results could prove conclusive for vegetation ecological protection in the Qinghai-Tibet plateau.

Characterization of landform in Bastar plateau using high resolution ALOS-DEM data

In the study, an attempt has been made to delineate the landforms of Goriyabahar Nala watershed in hot sub-humid ecological sub-regions (ESR) of Bastar Plateau, Chhattisgarh. To achieve the objectives of the study, freely available open source high resolution satellite data viz. 12.5 m ALOS-DEM (Digital Elevation Model) were utilized. ALOS-DEM was analyzed to extract terrain parameters like elevation, contour, slope, hillshade, drainage, etc. and was visually interpreted. Based on the contour lines at 5 m intervals, seven major landforms were identified, namely subdued hills, undulating uplands, undulating midlands, undulating lowlands, upper valley, narrow valley and alluvial plains. The study manifest that the high resolution satellite data viz. ALOS-DEM will be of immense help in delineation of distinct landforms.

A multiscale natural community and species-level vulnerability assessment of the Gulf Coast, USA.

Vulnerability assessments combine quantitative and qualitative evaluations of the exposure, sensitivity, and adaptive capacity of species or natural communities to current and future threats. When combined with the economic, ecological or evolutionary value of the species, vulnerability assessments quantify the relative risk to regional species and natural communities and can enable informed prioritization of conservation efforts. Vulnerability assessments are common practice in conservation biology, including the potential impacts of future climate scenarios. However, geographic variation in scenarios and vulnerabilities is rarely quantified. This gap is particularly limiting for informing ecosystem management given that conservation practices typically vary by sociopolitical boundaries rather than by ecological boundaries. To support prioritization of conservation actions across a range of spatial scales, we conducted the Gulf Coast Vulnerability Assessment (GCVA) for four natural communities and eleven focal species around the Gulf of Mexico based on current and future threats from climate change and land-use practices out to 2060. We used the Standardized Index of Vulnerability and Value (SIVVA) tool to assess both natural community and species vulnerabilities. We observed greater variation across ecologically delineated subregions within the Gulf Coast of the U.S. than across climate scenarios. This novel finding suggests that future vulnerability assessments incorporate regional variation and that conservation prioritization may vary across ecological subregions. Across subregions and climate scenarios the most prominent threats were legacy effects, primarily from habitat loss and degradation, that compromised the adaptive capacity of species and natural communities. The second most important threats were future threats from sea-level rise. Our results suggest that the substantial threats species and natural communities face from climate change and sea-level rise would be within their adaptive capacity were it not for historic habitat loss, fragmentation, and degradation.

Ecological regional analysis applied to campus sustainability performance

PurposeSustainability performance in higher education is often evaluated at a generalized large scale. It remains unknown to what extent campus efforts address regional sustainability needs. This study begins to address this gap by evaluating trends in performance through the lens of regional environmental characteristics.Design/methodology/approachFour sustainability metrics across 300 North American institutions are analyzed between 2005 and 2014. The study applies two established regional frameworks to group and assess the institutions: Commission on Environmental Cooperation Ecoregions and WaterStat (water scarcity status). Standard t-tests were used to assess significant differences between the groupings of institutions as compared to the North American study population as a whole.FindingsResults indicate that all institutions perform statistically uniformly for most variables when grouped at the broadest (Level I) ecoregional scale. One exception is the Marine West Coast Forest ecoregion where institutions outperformed the North American average for several variables. Only when institutions are grouped at a smaller scale of (Level III) ecoregions do the majority of significant performance patterns emerge.Research limitations/implicationsThis paper demonstrates an ecoregions-based analytical approach to evaluating sustainability performance that contrasts with common evaluation methods in the implementation field. This research also identifies a gap in the literature explicitly linking ecological sub-regions with their associated environmental challenges and identifies next research steps in developing defensible regional targets for applied sustainability efforts.Practical implicationsThe practical implications of this research include the following: substantive changes to methodologies for rating sustainability leadership and performance, a framework that incentivizes institutions to frame sustainability efforts in terms of collaborative or collective impact, a framework within which institutions can meaningfully prioritize efforts, and a potential shift toward regional impact metrics rather than those focused solely on campus-based or generalized targets.Originality/valueThe authors believe this to be the first effort to analyze North American higher education sustainability performance using regional frameworks.

A Comprehensive Survey of Pelagic Megafauna: Their Distribution, Densities, and Taxonomic Richness in the Tropical Southwest Indian Ocean

The distribution and density of pelagic megafauna (marine mammals, seabirds, elasmobranches and sea turtles) are important indicators of marine biodiversity, reflecting the condition of the underlying ecosystems. Taking advantage of their relative accessibility at the sea surface, a dedicated aerial survey was conducted in the tropical Southwest Indian Ocean to map their distribution, the taxonomic diversity and to estimate their densities for a reference standpoint on the area. This large survey across three ecological sub-regions revealed contrasting spatial distributions: maps of taxonomic richness of marine mammals and seabirds revealed different “hotspots” in the area. Densities were estimated for eight cetacean taxa with small and large Delphininae, or small Globicephalinae dominating, and for seven seabird taxa, with terns and noddies dominating. At the community level, the Southwest Indian Ocean megafauna was structured by the marine environment with strong differences between the Mozambique Channel and the Mascarene Islands, or between shelf and slope/oceanic habitats. Our results illustrate how multi-taxa aerial surveys are relevant and cost-effective monitoring tools for marine megafauna, allowing a community-wide approach.

Ant community associated to citrus canopy in Southern Portugal over a century after the invasion by the Argentine ant

The Argentine ant Linepithema humile (Mayr) established in Algarve, Portugal, about 120 years ago. In this work we assess the current impact of this invasive species on ant communities of citrus orchards and compare the invasibility between different ecological sub-regions in relation to some biotic and abiotic requirements. A novel extensive sampling approach in citrus canopy (including the trunk) along 49 orchards in three ecological subregions was performed. In total 21 species were observed, covering 81% of the species reported for the region. Despite the same habitat, ant species composition differed in contiguous subregions with particular significance to the absence of Argentine ant in the inner subregion. Based on the results we hypothesized the existence of ecological factors at regional scale preventing the establishment of L. humile in this subregion, related to anthropogenic factors and geologically unfavorable sites. At a local scale, its establishment and dominance is associated with ant community composition and a lack of dominant native ant species, which can be responsible for straightening the spread of one of the world most invasive ant species.

Predicting Palustrine Wetland Probability Using Random Forest Machine Learning and Digital Elevation Data-Derived Terrain Variables

The probability of palustrine wetland occurrence in the state of West Virginia, USA, was mapped based on topographic variables and using random forests (rf) machine learning. Models were developed for both selected ecological subregions and the entire state. The models were first trained using pixels randomly selected from the United States National Wetland Inventory (nwi) dataset and were tested using a separate random subset from the nwi and a database of wetlands not found in the nwi provided by the West Virginia Division of Natural Resources (wvdnr). The models produced area under the curve (auc) values in excess of 0.90, and as high as 0.998. Models developed in one ecological subregion of the state produced significantly different auc values when applied to other subregions, indicating that the topographical models should be extrapolated to new physiographic regions with caution. Several previously unexplored dem-derived terrain variables were found to be of value, including distance from water bodies, roughness, and dissection. Non-nwi wetlands were mapped with an auc value of 0.956, indicating that the probability maps may be useful for finding potential palustrine wetlands not found in the nwi.

Ownership property size, landscape structure, and spatial relationships in the Edwards Plateau of Texas (USA): landscape scale habitat management implications

The present research focused on using spatial analysis to determine relationships among land ownership property sizes and landscape structure, with a focus on conservation management implications. Indices and metrics of ownership property sizes and landscape structure were calculated for 20 km buffer areas around 31 North American Breeding Bird Survey transects, 12 located within the Edwards Plateau ecoregion and 18 in contiguous ecoregions. The number of bird species observed at each transect provided a measure of avian species richness associated with land cover classes for each respective transect (Gonzalez in Urban influence on diversity of avifauna in the Edwards Plateau of Texas: effect of property sizes on rural landscape structure, Texas A&M University, 2005). Spatial correlations were calculated between each pair of the landscape indices. Spatial analysis identified a “threshold of habitat fragmentation” for the 500 acre (ac) ownership property size. Significant spatial correlations among variables showed that property sizes lower than 500 ac produced habitat fragmentation represented by a decrease in mean patch size (MN) and proximity among habitat patches (Index PROX). Spatial analysis also made possible the prioritization of ecological sub-regions of the Edwards Plateau for conservation or restoration. The Live Oak-Mesquite Savannah showed the highest average ownership property size (7305 ac) and the highest values of patch richness. Based on the results, management in the Live Oak-Mesquite Savannah sub-region should focus on the conservation of land mosaic diversity to assure native avian species turnover (Whittaker 1972). In Balcones Canyon Lands, 64 % of land was covered by farms smaller than 500 ac and the overall average ownership property size was above the threshold of fragmentation (1440 ac), implying that management policies there should focus both on habitat conservation and on restoration. In contrast, 71 % of land in the Lampasas Cut Plains was covered by farms smaller than 500 ac, and average ownership property size was very close to the fragmentation threshold (625 ac). Consequently, the results indicate that management in the Lampasas Cut Plains sub-region should focus on habitat restoration (e.g., corridors that connect isolated habitat patches). In general, the threshold of ownership property size, 500 ac, is important for conservation planning because below that threshold of property size, habitat patch size begins to decrease and the distance between equivalent patches of habitat increases. Isolated patches act as islands within a sea of less suitable habitat which produce negative effects on biodiversity. Identifying the spatial characteristics indicative of habitat fragmentation, or the likelihood thereof, is an important issue for conservation planning in places with urban sprawl influence.

Determining and Mapping Eco-Cultural Subregions Using GIS and Simos Procedure

Immovable cultural properties constituting cultural heritage centre upon regions that have the level of natural features required for basic human needs. Such regions constitute a space with characteristics of an eco-cultural subregion and they do not have any administrative or geographical boundaries. Aims of this study are to introduce the concept of eco-cultural subregion, which is the holistic expression of natural features together with cultural heritage, to the literature; to present a method in identifying geographical boundaries of subregions; and to formulate a classification system. The research was conducted in the Phrygian Valley, which is one of the most important centres of ancient Phrygians in Western Anatolia, using a method based on GIS-MCDM (Geographic Information System-Multiple Criteria Decision Making). The selected MCDM technique was Simos Procedure. Natural features and cultural heritage were considered together and the entire Phrygian Valley was approached as both an ecological and a cultural region. Ecological features were analysed using 7 main and 17 subfactors, while cultural heritage was analysed using 2 main factors and 15 subfactors. Simos Procedure was employed in the assignment of weight for main factors and subfactors. First 17 ecological subregions with similar characteristics under 4 levels were identified. Considering their cultural heritage values, a classification system was formulated and eco-cultural subregions were expressed in level/degree in this system. In consequence of this procedure boundaries of eco-cultural subregions were drawn and mapped. This classification also enabled to identify the concept of eco-cultural subregion which is not found in the literature. It is expected that eco-cultural subregions will provide to discuss ecological features and cultural heritage holistically in conservation and planning studies.

Predicting Palustrine Wetland Probability Using Random Forest Machine Learning and Digital Elevation Data-Derived Terrain Variables

The probability of palustrine wetland occurrence in the state of West Virginia, USA, was mapped based on topographic variables and using random forests (RF) machine learning. Models were developed for both selected ecological subregions and the entire state. The models were first trained using pixels randomly selected from the United States National Wetland Inventory (NWI) dataset and were tested using a separate random subset from the NWI and a database of wetlands not found in the NWI provided by the West Virginia Division of Natural Resources (WVDNR). The models produced area under the curve (AUC) values in excess of 0.90, and as high as 0.998. Models developed in one ecological subregion of the state produced significantly different AUC values when applied to other subregions, indicating that the topographical models should be extrapolated to new physiographic regions with caution. Several previously unexplored DEM-derived terrain variables were found to be of value, including distance from water bodies, roughness, and dissection. Non-NWI wetlands were mapped with an AUC value of 0.956, indicating that the probability maps may be useful for finding potential palustrine wetlands not found in the NWI .

Toward wind farm monitoring optimization: assessment of ecological zones from marine landscapes using machine learning algorithms

Within the perspective of siting wind farms offshore of Rhode Island, USA, the State and National Environmental Agencies had requested a local marine ecological assessment, which led to an ecological zoning of the area. In view of expanding this zoning outside its limit of the test area and filling gaps in ecological zones, an effort to model those ecological zones using marine landscape or abiotic features was carried out. This study tests the accuracy of selected machine learning algorithmic models, decision tree, and random forest, for relating marine landscapes features to ecological sub-regions. Both models show to be good predictive tools with accuracy after cross validation of the order of 5–3%. Key abiotic variables to provide an accurate model were investigated. The study demonstrates the importance of the distance to coast, the sediment characteristics (fraction of clay, median size of the sediments), the hydrodynamic features, in particular not only tidal current/drag force, but also wave drag force, and finally the oceanographic characteristics such as stratification and sea surface temperature to built a good predictive model. Those findings provide some insight on the pre-monitoring effort optimization.