Habitat Suitability Index Research Articles

An improved habitat suitability index for the great crested newt Triturus cristatus

An improved habitat suitability index for the great crested newt Triturus cristatus

The global distribution and the risk prediction of relapsing fever group Borrelia: a data review with modelling analysis

The recent discovery of emerging relapsing fever group Borrelia (RFGB) species, such as Borrelia miyamotoi, poses a growing threat to public health. However, the global distribution and associated risk burden of these species remain uncertain. We aimed to map the diversity, distribution, and potential infection risk of RFGB. We searched PubMed, Web of Science, GenBank, CNKI, and eLibrary from Jan 1, 1874, to Dec 31, 2022, for published articles without language restriction to extract distribution data for RFGB detection in vectors, animals, and humans, and clinical information about human patients. Only articles documenting RFGB infection events were included in this study, and data for RFGB detection in vectors, animals, or humans were composed into a dataset. We used three machine learning algorithms (boosted regression trees, random forest, and least absolute shrinkage and selection operator logistic regression) to assess the environmental, ecoclimatic, biological, and socioeconomic factors associated with the occurrence of four major RFGB species: Borrelia miyamotoi, Borrelia lonestari, Borrelia crocidurae, and Borrelia hermsii; and mapped their worldwide risk level. We retrieved 13 959 unique studies, among which 697 met the selection criteria and were used for data extraction. 29 RFGB species have been recorded worldwide, of which 27 have been identified from 63 tick species, 12from 61 wild animals, and ten from domestic animals. 16 RFGB species caused human infection, with a cumulative count of 26 583 cases reported from Jan 1, 1874, to Dec 31, 2022. Borrelia recurrentis (17 084 cases) and Borrelia persica (2045 cases) accounted for the highest proportion of human infection. B miyamotoi showed the widest distribution among all RFGB, with a predicted environmentally suitable area of 6·92 million km2, followed by Blonestari (1·69 million km2), B crocidurae (1·67 million km2), and B hermsii (1·48 million km2). The habitat suitability index of vector ticks and climatic factors, such as the annual mean temperature, have the most significant effect among all predictive models for the geographical distribution of the four major RFGB species. The predicted high-risk regions are considerably larger than in previous reports. Identification, surveillance, and diagnosis of RFGB infections should be prioritised in high-risk areas, especially within low-income regions. National Key Research and Development Program of China.

Estuary-Specific and Adaptive Habitat Suitability Index Model for the Eastern Oyster Crassostrea Virginica in the Pensacola Bay System, Florida, USA

Oyster reef, a key ecosystem component in many temperate and subtropical estuarine systems, has been greatly diminished by human influences and ongoing climatic changes. Changes in precipitation, oyster adult and larval distribution, benthic composition, hydrology, currents and/or inputs to the system such as pollutants or sediments complicate the identification of locations that may be suitable for oyster reef restoration. In such cases, development of a habitat suitability index model (HSI) can provide useful guidance on where restoration activities might be successful, and once complete, help to focus the area for restoration project siting consideration. HSI models can be constructed from a variety of data categories including biological requirements of the species to be restored, physical characteristics of the system, regulatory constraints, and socio-economic concerns. Here we describe the development of a geographic information system based HSI model to inform restoration of oyster reef in the Pensacola Bay System, Florida, USA that can be used as a template for oyster reef restoration in other Florida estuaries and beyond. We demonstrate that an oyster habitat HSI model can be relatively simple to construct, useful even with limited environmental data, improved with community stakeholder and resource user input, and easily adaptable.

A regional biogeography-based optimization algorithm for the distributed assembly permutation flow-shop scheduling problem with fuzzy processing time

For solving the distributed assembly flow shop scheduling problem with fuzzy processing time (FDAPFSP), a regional biogeography-based optimization algorithm (RBBO) is proposed to minimize the maximum fuzzy completion time. The mathematical model is provided. In RBBO, all habitats are divided into regions based on the habitat suitability index, and the habitats of each region are subject to cross-regional migration and replacement procedures. A critical factory optimization strategy is developed to enhance local search capability. Taguchi method is used to determine the parameters of RBBO. In ten FDAPFSP instances, comparative testing of RBBO algorithm with various heuristic and swarm intelligence algorithms are conducted. The computation results show that in ten FDAPFSP cases, the proposed RBBO outperforms other algorithms in nine out of ten FDAPFSP cases.

Evaluating the spatial transferability of habitat suitability models: implications for conservation and management

Habitat models often rely on data with incomplete or patchy survey coverage, making it necessary to project observed habitat relationships into unsampled areas. Model transferability is often assumed, rarely tested, and may result in uninformed management recommendations if not validated. In the eastern Gulf of Maine, data from the federal trawl survey has historically been used to identify suitable habitat for juvenile Atlantic cod ( Gadus morhua), despite mainly sampling in offshore waters. This study evaluated the spatial transferability of a habitat suitability index model by projecting offshore habitat relationships inshore and comparing them to models built using two inshore surveys. Due to rare warm-water exposure in offshore waters, the federal survey model was unable to identify suitable habitat in the warmer, shallower areas that the inshore surveys identified, suggesting that offshore models are not transferable inshore for juvenile cod in the eastern Gulf of Maine. The results of this study have implications for fisheries worldwide and suggest that incorrectly assuming model transferability could pose a significant hurdle for effective conservation and management.

Fish Module ‐ A prognostic tool for modeling the optimal environmental conditions for fish

This article discusses the service called the Fish Module for the predictive determination of the optimal environmental conditions for the occurrence of sprat, herring, cod, and flounder on the example of fish found in the southern Baltic Sea (in particular in the Gulf of Gdańsk region). The Fish Module utilizes data from the EcoFish ecohydrodynamic model and then uses fuzzy logic and fish preference data to calculate the Habitat Suitability Index (HSI). Data on fish preference were determined based on 587 expeditions during which physicochemical parameters of the sea were recorded. Our analysis determined threshold HSI values below which successful catches are unlikely for sprat, herring, and cod, confirming the system's effectiveness in identifying locations with favorable environmental conditions for these species. Fishermen are advised to select routes with specific HSI thresholds to achieve greater efficiency in fishing. Furthermore, the analysis revealed that fishing durations do not necessarily correlate with higher catches, emphasizing the importance of selecting suitable routes based on favorable environmental conditions for fish habitat. We expect the Fish Module to be the most demanded product of the FindFISH project.

Advancing microgrid efficiency: a study on battery storage systems and wind energy penetration based on a Contracted fitness-dependent optimization algorithm

ABSTRACT This research study presents a novel approach to enhance the efficiency and performance of Battery Energy Storage Systems (BESSs) within microgrids, focusing particularly on the integration of wind energy. The inherent inconsistency and unpredictability of Renewable Energy Resources (RERs) necessitate the development of effective integration solutions to accommodate them. Our article provides a customized iteration of the metaheuristic algorithm referred to as the Contracted fitness-dependent optimizer. The proposed methodology involves the adaptive adjustment of migration rates based on habitat suitability indices, while also considering variations in perturbations. The incorporation of Lévy flight and an elimination phase significantly enhances the algorithm’s efficacy in problem-solving. In order to establish the superiority of our strategy over alternative optimization approaches, we conduct simulations across diverse conditions and subsequently compare the outcomes. It was calculated that the daily scheduling cost was 235.2$ and the daily operational cost was USD 268$. In Scenario 3, the total operating expenses and arranging costs for 1 day, respectively, were determined to be 165$ and 115$. Further, the highest depth of discharge level was around 77%. Moreover, a −9.18 kW and 23.02 kW maximum charging and discharging power adjustment was made. The findings of our study emphasize the economic and operational benefits associated with appropriately sized BESSs within microgrid contexts. These advantages have the potential to enhance battery longevity and promote the development of more sustainable energy systems.

Disentangling the effects of management and climate change on habitat suitability for saproxylic species in boreal forests

Forest degradation induced by intensive forest management and temperature increase by climate change are resulting in biodiversity decline in boreal forests. Intensive forest management and high-end climate emission scenarios can further reduce the amount and diversity of deadwood, the limiting factor for habitats for saproxylic species in European boreal forests. The magnitude of their combined effects and how changes in forest management can affect deadwood diversity under a range of climate change scenarios are poorly understood. We used forest growth simulations to evaluate how forest management and climate change will individually and jointly affect habitats of red-listed saproxylic species in Finland. We simulated seven forest management regimes and three climate scenarios (reference, RCP4.5 and RCP8.5) over 100 years. Management regimes included set aside, continuous cover forestry, business-as-usual (BAU) and four modifications of BAU. Habitat suitability was assessed using a species-specific habitat suitability index, including 21 fungal and invertebrate species groups. “Winner” and “loser” species were identified based on the modelled impacts of forest management and climate change on their habitat suitability. We found that forest management had a major impact on habitat suitability of saproxylic species compared to climate change. Habitat suitability index varied by over 250% among management regimes, while overall change in habitat suitability index caused by climate change was on average only 2%. More species groups were identified as winners than losers from impacts of climate change (52%–95% were winners, depending on the climate change scenario and management regime). The largest increase in habitat suitability index was achieved under set aside (254%) and the climate scenario RCP8.5 (> 2%), while continuous cover forestry was the most suitable regime to increase habitat suitability of saproxylic species (up to + 11%) across all climate change scenarios. Our results show that close-to-nature management regimes (e.g., continuous cover forestry and set aside) can increase the habitat suitability of many saproxylic boreal species more than the basic business-as-usual regime. This suggests that biodiversity loss of many saproxylic species in boreal forests can be mitigated through improved forest management practices, even as climate change progresses.

Response of Siberian Cranes (Grus leucogeranus) to Hydrological Changes and the Availability of Foraging Habitat at Various Water Levels in Poyang Lake.

To assess the Siberian crane (Grus leucogeranus)'s response to changing water levels and habitat quality at Poyang Lake, we analyzed the lake's hydrological trends over the past two decades with the Mann-Kendall and Sen slope methods. Additionally, we explored the link between the crane population size and hydrological conditions at the lake from 2011 to 2019. Meanwhile, five environmental factors, including habitat type, distance from shallow lakes, human footprint index, elevation and normalized vegetation index were selected, and the distribution patterns of suitable habitats for the Siberian crane under 10 water level gradients with intervals of about 1 m (5.3-14.2 m) were simulated by using an improved habitat suitability index model that determines the weights of evaluating factors based on the MaxEnt model. The results showed that the overall trend of the inundated area in Poyang Lake was shrinking in the last 20 years, with a significant increase in the area of exposed floodland during the early wintering period (Z = -2.26). The prolonged drought resulting from this will force vegetation succession, thereby diminishing the food resources for cranes in their natural habitat. The mean inundated area in June demonstrated a significant negative correlation with the population of Siberian cranes in natural habitats (r = -0.75, p = 0.02). Shortage of the Siberian crane-preferred Vallisneria tuber due to June flooding was the primary driver of the crane's altered foraging strategy and habitat shift. In years with relatively normal June inundation, indicating abundant Vallisneria resources, the relationship between the inundated area during the dry season and the crane population fit well, with a quadratic curve (R2 = 0.92, p = 0.02). The dry season's inundated area primarily affected the crane population and distribution pattern by influencing the availability of food resources, and both excessive and insufficient inundation areas were unfavorable for crane survival. The modeling results for habitat suitability indicated that as the water level decreased, the trend of the area of good habitat for the Siberian crane showed an inverted bell shape, peaking at a water level of 8.8 m, with optimal conditions occurring between 8 and 10 m. The combined effects of climate and human activities have made the shortage of food resources in Poyang Lake the new normal. The degradation of natural habitats has led to a decline in the quality of Siberian crane habitats, and artificial habitats can only be used as refuges to a certain extent. Thus, formulating strategies to restore natural habitats and enhance the management of artificial habitats is crucial for the conservation efforts of Siberian cranes.

Spatio-temporal variability of fishing habitat suitability to tuna purse seine fleet in the Western and Central Pacific Ocean

Understanding the spatial distribution of fishing activity is important for ecosystem-based fisheries management.The dynamic vessel trajectory information provided by Automatic Identification System (AIS) together with local remote ocean environment data, provide an opportunity for new technologies to enable improvements in fisheries management.The spatio-temporal and variability to tuna purse seine fleet suitable fishing were evaluated based on fishing effort from AIS by constructing an integrated habitat suitability index (HSI), which weight of variables were determined with boosted regression trees (BRT) model. The results show that the fishing activity tuna purse-seine fleet and catch data from the Western and Central Pacific Fisheries Commission (WCPFC) have the same spatial distribution.The tuna purse seine fleet operation concentrate in EEZ area of the Pacifc Island countries and has seasonal environmental preferences. The fishing habitat suitability can be characterized by a few environmental factors. The HSI model had good capacity to predict habitat suitability to tuna purse seine fleet for mostly catch and fishing effort located in the suitable area. The suitable fishing areas showed significant seasonal changes and low utilization of the available environmentally suitable fishing habitat in each month. The most stable suitable area located in the New Guinea Offshore latitudinal region, may be experiencing high tuna purse-seine fishing pressure throughout the year. The research could help to improve the scientific and effective management of distant water fisheries under limited regulatory capacity.

Greater Glasgow pond amphibian surveys 2022

In 2019, Clyde Amphibian and Reptile Group (CARG) decided to organise a spring 2020 common frogspawn (Rana temporaria) survey of the Greater Glasgow area, Scotland. The COVID-19 lockdown postponed this project until 2022. The 2022 survey area was spread across four council districts: Glasgow City, South Lanarkshire, Renfrewshire and East Renfrewshire. The primary aim of the surveys was to collect records of amphibian breeding throughout the Greater Glasgow Area, with secondary aims of: (1) revisiting the ponds to see if the ponds supported the spawn through to tadpole and froglet stages; (2) assessing the condition of the ponds for amphibians; (3) identifying areas for habitat enhancement work; and (4) collecting data on breeding common toads (Bufo bufo) and newts (Lissotriton, Ichthyosaura and Triturus spp.). A total of 45 volunteers participated in the surveys. Surveys were completed between March and May 2022, with a primary focus between March and April in order to capture frog spawning data. Surveyors were asked to record amphibian population data, and habitat data in order to estimate Habitat Suitability Indices. In total 162 ponds were surveyed, with evidence of breeding common frogs found in 66.1% of ponds with 11,478 frogspawn clumps counted on the first set of surveys. Only 12.2% of ponds were found to contain common toads, and small newts (Lissotriton spp.) were identified in only 7.4% of ponds. In relation to the quality of breeding habitat, 24.5% of ponds were assessed as drying out annually, which significantly reduces the suitability of these ponds for breeding. This figure is predicted to increase with anthropogenic climate change reducing the number of breeding ponds available to amphibians in the absence of intervention. Furthermore, the majority of the ponds surveyed were assessed as offering poor or below average suitability for great crested newts (Triturus cristatus) and nearly half of the ponds surveyed were found to have poor or bad water quality, which can increase stress levels for larval amphibians and may adversely affect mortality levels. However, as the analysis of water quality was subjective, these findings should be treated with caution.

Developing a spatial-based predictive model for conservation area management prioritization using ecosystem service and site suitability index in Java Island

Primary forests in Indonesia are in high decline. The increase in population exacerbates forest reductions, as forests are changed to settlements. This impacts the loss of biodiversity and animals in Indonesia. An important way to prevent the loss of biodiversity and animals is to conserve crucial areas. Spatial modeling can be done to detect conservation areas that are important for the future. This study analyzes the index of forest ecosystem services and habitat suitability of extinct animals to determine priority conservation areas. Here we predict forest areas that are indicated to be lost in the future. We use integrated spatial data using GIS (Geography Information System) to determine priority conservation areas. From the results, we predict that there will be a change in land cover to settlement by 2030. This causes the ecosystem service index (ESI) value to decrease from an initially high value each year. In terms of land suitability, the majority will decrease by 2030. The integration that has been performed also shows a very high decrease in the ESI value and a very high conformity value in the Java region. This research can hopefully be used by policy makers to determine new conservation areas on the island of Java.

Climate-related habitat variations of Humboldt squid in the eastern equatorial Pacific Ocean

An integrated habitat suitability index (HSI) model was developed in this study for Dosidicus gigas in the eastern equatorial waters of the Pacific Ocean to explore climate-related spatial and temporal variability in the habitat distribution pattern based on three crucial environmental variables: sea surface temperature (SST), sea surface salinity (SSS) and chlorophyll-a concentration (Chl-a). Results revealed that the HSI model could accurately predict potential habitats for D. gigas. The habitat suitability varied significantly by month, with highest suitability in April and lowest in March. Besides, from December to May, the longitudinal gravity center of the fishing grounds (LONG) and the HSI overall shifted eastward and the latitudinal gravity center shifted northward then southward. In comparison to the warm ENSO phases in 2019, the cold ENSO phases in 2018 produced increased suitable habitat from December to May, leading to a significantly higher CPUE. Prospective high-quality habitats in 2018 primarily occurred in the western regions, with the exception of December, which resulted in a more westward distribution of LONG from January to May. High-quality habitats moved northward from December to February and southward from March to May 2018, compared to minor latitudinal movement in 2019. It was inferred that annual variations in squid abundance and distribution were largely affected by the SST-related habitat pattern of D. gigas in the eastern equatorial waters. Our findings suggested that D. gigas habitats clearly varied by month and year and were greatly influenced by climate-induced environmental changes.

Defining Collision Risk: Lesser Flamingo Phoeniconaias minor Power Line Collision Sensitivity and Exposure for Proactive Mitigation

Lesser Flamingos Phoeniconaias minor regularly collide with power lines in South Africa. Attaching light-emitting markers to overhead wires seems to be an effective mitigation measure; however, the cost of these devices is prohibitive of large-scale installation. Spatial predictions about flamingo collision risk are thus important for achieving efficient and effective proactive mitigation. In this study, collision risk was defined as a combination of factors related to threat exposure. A habitat suitability index was developed according to changes in surface water occurrence and Chlorophyll-a concentrations, which proved accurate in predicting Lesser Flamingo occurrence. Habitat suitability, and three other species and threat exposure variables, were then used in logistic regression models predicting the occurrence of historic collisions. The most parsimonious model included habitat suitability and flight height. Flamingos were only at risk of collision with power lines when flying lower than 50 m and within 3 km from the water’s edge. High-risk power line sections were thus identified from 3 km buffers around waterbodies ranked according to habitat suitability, which significantly reduced the number of power line spans predicted for proactive marking. While our models indicated that aspects related to exposure were important for predicting flamingo power line collisions, aspects related to sensitivity (e.g., nocturnal behavior) must also guide the choice of mitigation.

Ecological niche modelling in identifying habitats for effective species conservation: A study on Endemic aquatic plant Crinum malabaricum

Ecological niche modelling (ENM) aids in identifying new species populations and suitable habitats for species translocation, addressing the conservation challenge of endemic species with limited distribution. The current study aims to develop a habitat suitability map of Crinum malabaricum Lekhak & S. R. Yadav, an endemic species with a very limited geographical range, using Maxent-based ecological niche modelling to identify suitable receptor sites for conservation translocation. Out of 23 species occurrence records, 11 were used for this study. Seven predictive variables are selected based on Pearson’s correlation coefficient (r = ±0.8) and permutation importance (>3%) from a group of nineteen bioclimatic and two topographic variables. The study area encompasses the Malabar Plains of the Western Ghats. The accuracy of the model was assessed using the ROC curve method, and the contribution of variables was estimated using a Jackknife analysis. The results show that precipitation of the driest quarter and mean temperature of the driest quarter were the leading predictors of habitat suitability. The model shows high accuracy with a value of 1.6 for AUC ratios and values > 8 for AUC, partial AUC, Kappa, and TSS metrics. The model predicted a fundamental niche for this species with 2393 KM2 (3.6% of the study area) as a highly suitable area for C. malabaricum (where habitat suitability index ≥ 0.85), which is much higher than the calculated extend of occurrence (EOO = 283 KM2) of this species. The realized niche of this species was identified using field explorations, and ten suitable areas were selected for species translocation. These selected habitats are distributed across various regions of the southern Western Ghats. As a species with limited distribution and a lack of proper dispersal mechanisms, a successful species translocation to these sites will overcome the endemism in this species. For selecting appropriate sites for conservation translocations, this methodology will be reliable and effective, and it could be useful for other endemic species.

Evaluating Suitability of Fishing Areas for Squid-Jigging Vessels in the Northwest Pacific Ocean Derived from AIS Data

Understanding the spatial distribution of fishing activity and suitable fishing areas is important for improving sustainable fisheries management and protecting vulnerable fish stocks. To identify climate-related habitat changes and variations in the distribution of fishing activity for squid-jigging vessels in the Northwest Pacific Ocean, two types (weighted arithmetic mean method, weighted-AMM; weighted geometric mean method, weighted-GMM) of habitat suitability index (HSI) models were developed in this study with marine environmental data at different depths. The boosted regression tree (BRT) model was adopted to access the monthly important environmental variables and the relative influence of the corresponding variables. The results showed that the weighted-AMM has better prediction performance than the weighted-GMM. The suitable fishing areas showed significant seasonal changes in both spatial location and coverage area. The hotspot map showed that the suitable fishing area for squid-jigging vessels was located in the scope of 42∘ N∼44∘ N, 155∘ E∼170∘ E throughout the year during 2012∼2019, which suggests that high squid-jigging fishing pressure should be given more attention in fishery management. The HSI model also had good prediction performance for the fishery data of Chinese companies, except for June and July. Additionally, fishing efforts could be used as alternative data for fishery research. The study has also suggested that fishery data are restricted by spatial and temporal distribution and fishing experience, which probably biases the results of the research.

Self-reported tick exposure as an indicator of Lyme disease risk in an endemic region of Quebec, Canada

BackgroundLyme disease (LD) and other tick-borne diseases are emerging across Canada. Spatial and temporal LD risk is typically estimated using acarological surveillance and reported human cases, the former not considering human behavior leading to tick exposure and the latter occurring after infection. ObjectivesThe primary objective was to explore, at the census subdivision level (CSD), the associations of self-reported tick exposure, alternative risk indicators (predicted tick density, eTick submissions, public health risk level), and ecological variables (Ixodes scapularis habitat suitability index and cumulative degree days > 0 °C) with incidence proportion of LD. A secondary objective was to explore which of these predictor variables were associated with self-reported tick exposure at the CSD level. MethodsSelf-reported tick exposure was measured in a cross-sectional populational health survey conducted in 2018, among 10,790 respondents living in 116 CSDs of the Estrie region, Quebec, Canada. The number of reported LD cases per CSD in 2018 was obtained from the public health department. Generalized linear mixed-effets models accounting for spatial autocorrelation were built to fulfill the objectives. ResultsSelf-reported tick exposure ranged from 0.0 % to 61.5 % (median 8.9 %) and reported LD incidence rates ranged from 0 to 324 cases per 100,000 person-years, per CSD. A positive association was found between self-reported tick exposure and LD incidence proportion (ß = 0.08, CI = 0.04,0.11, p < 0.0001). The best-fit model included public health risk level (AIC: 144.2), followed by predicted tick density, ecological variables, self-reported tick exposure and eTick submissions (AIC: 158.4, 158.4, 160.4 and 170.1 respectively). Predicted tick density was the only significant predictor of self-reported tick exposure (ß = 0.83, CI = 0.16,1.50, p = 0.02). DiscussionThis proof-of-concept study explores self-reported tick exposure as a potential indicator of LD risk using populational survey data. This approach may offer a low-cost and simple tool for evaluating LD risk and deserves further evaluation.

Modeling habitat suitability for endemic Grizzled leaf monkey (Presbytis comata) using geospatial machine learning approach

Global biodiversity, which plays a significant role in maintaining the sustainability of Earth's ecosystems, is threatened by increasingly destructive and uncontrolled human activities. Indonesia's Grizzled leaf monkey or Javan Surili (Presbytis comata) is an endemic primate whose population continues to decline. As the high potential for reducing animal habitats on Java has implications for endemic Surili primates, it is necessary to study the characteristics of suitable habitats that can be used as the basis for strategic policies. This study aimed to develop a habitat suitability index (HSI) for Surili with a study of forest areas in Java Island, Indonesia, using a multi-machine learning approach. The novelty of this study is the integration of three machine learning methods, random forest (RF), support vector machines (SVM), and maximum entropy (MaxEnt), and the use of four parameters, climate, topography, ecology, and anthropogenic factors. This is expected to increase the accuracy of assessing the suitability of the developed Surili habitat. Of the 116 Surili encounter points in the region, 7 (6%) were in the suitable HSI class, 38 (33%) in the high HSI class, 49 (42%) in the moderate HSI class, and 22 (19%) in the low and unsuitable HSI classes. Meanwhile, of the 13 release points, 12 (92%) were dominated by areas with high-to-suitable HSI classes. Based on the receiver operating characteristic curve value, the integrated multi machine learning algorithms value was 0.8578. This study could help formulate potential strategic policies in the restoration program that has been pursued to conserve Surili species in Indonesia.

Study on suitability and spatial distribution of human settlement environment in arid areas based on GIS and analytic hierarchy process

IntroductionScientific evaluation of the suitability of human settlement in arid areas and a grasp of their spatial differentiation characteristics are of great significance for the implementation of regional human settlement environment (HSE) improvement according to local conditions.MethodsIn this study, we took Gansu Province (GS), a typical arid area in China, as the research object. First, based on field research, we used the analytic hierarchy process to determine the weights of 12 evaluation indicators and build an HSE evaluation model from the two dimensions of nature and humanities. Then, we conduct a comprehensive evaluation of each factor based on the GIS spatial analysis module. Finally, we used the GIS natural breakpoint method and zoning statistics to understand the suitability of the HSE and its spatial distribution characteristics in GS Province.ResultsThe study findings can be summarized as follows: (1) The spatial distribution of the suitability of the natural environment in GS Province shows that the east-central part of the province is significantly higher than the northwestern, and there is a big difference between localities. (2) The comprehensive suitability of human settlement in GS Province appears as a spatial distribution characteristic, gradually decreasing from the central urban area to the surrounding areas. The suitability index of the living environment is between 0.26 and 0.71. (3) The area sizes of the five types of suitability are ranked as the following: low suitable (138,502 km2) &amp;gt; moderately suitable (133,761 km2) &amp;gt; critical suitable (97,244 km2) &amp;gt; high suitable (47,459 km2) &amp;gt; unsuitable (38,980km2); in 14 cities, the average value of habitat suitability index is more than 0.5 accounting for 42.9%.DiscussionOur research not only fills a gap in the research of human settlement in arid zones but also provides policy support for government departments to improve human settlement.

Development of an integrated modelling framework to evaluate impacts of pressures on habitat conditions and riverine biota

AbstractIt is becoming extremely challenging to protect the structural and functional integrity of freshwater ecosystems, as many ecological systems face pressures from climate change, population growth and other environmental impacts. However, existing approaches of assessing impacts of external stressors to habitat conditions often neglect the long‐term hydrological changes. This study aimed to develop an integrated modelling framework to assess impacts of different stressors on reach‐scale habitat conditions for benthic macroinvertebrates in a temperate lowland river in central Poland. We coupled hydrological (SWAT+), hydraulic (HEC‐RAS 1D and SRH‐2D) and habitat (CASiMiR) models to predict the response of habitat suitability for the functional group of filter feeders to changes in the catchment for a 135‐m section of the Jeziorka river. Setting up and calibrating individual models required extensive field data collection (e.g., bathymetry field surveys, gauging water levels, discharge and flow velocity measurements and biological sampling). The performance of individual models in simulating discharge, water levels and depth/flow velocity was assessed as satisfactory according to literature. The modelling chain was then applied to compare the effects of three flow scenarios (average, low and high flows). The results showed an overall high value of hydraulic habitat suitability index for filter feeders, with moderately and severely lower values for low and high flow scenario, respectively. The implemented modelling framework performed well could be applied to examine the impacts of climate change and other anthropogenic stressors on reach‐scale habitat conditions. Its practical implementation could be beneficial towards sustainable water resources and environmental flow regime development.