Predictive Distribution Models Research Articles

Analysis of Difference for Gully Density and the Relationship between Terrain Factors and Gully Density in the Losses Plateau Based on Google Earth and GIS

Based on ASTER GDEM V2 (30 meter) and the corresponding Google Earth high-resolution images data, small-catchments artificial interaction and random points sample method were used to estimate gully density of five selected catchments in the Losses Plateau and analysis the relationship between the difference for gully density from two methods and the areas of catchments. Besides the gully density, terrain factors such as slope, flow accumulation as well as the distribution probability of gully were calculated by using spatial analysis method. The statistical analysis method was used to analyze the correlation between gully density and terrain factors and obtain gully characteristic distribution prediction model. The results of the study indicate the following: (1) the average difference of gully density extracted by random point method and artificial interactive method is 7.86%, and the difference appears to increase first and then decrease with the watershed area. When the area of catchment is 76 km2, the difference is closed to 0; when the area is 131 km2, the difference is closed to maximum value, which is 0.182. The time-saving and labor-saving random point method can replace the tedious artificial interaction method. (2) The gully slope on Loess Plateau is mainly distributed at 10°∼30° (74%), and the flow accumulation of gully distribution is mainly distributed at 0∼0.07 km2 (95%); 10°∼30° and 0∼0.07 km2 can be used as critical reference values for, respectively. (3) The gully distribution probability showed a when slope gradient was lower than 30 degree, while a negative correlation of gully density and the flow accumulation were found in this study.

Modelling distributions of Aedes aegypti and Aedes albopictus using climate, host density and interspecies competition.

Florida faces the challenge of repeated introduction and autochthonous transmission of arboviruses transmitted by Aedes aegypti and Aedes albopictus. Empirically-based predictive models of the spatial distribution of these species would aid surveillance and vector control efforts. To predict the occurrence and abundance of these species, we fit a mixed-effects zero-inflated negative binomial regression to a mosquito surveillance dataset with records from more than 200,000 trap days, representative of 53% of the land area and ranging from 2004 to 2018 in Florida. We found an asymmetrical competitive interaction between adult populations of Aedes aegypti and Aedes albopictus for the sampled sites. Wind speed was negatively associated with the occurrence and abundance of both vectors. Our model predictions show high accuracy (72.9% to 94.5%) in validation tests leaving out a random 10% subset of sites and data since 2017, suggesting a potential for predicting the distribution of the two Aedes vectors.

Prediction Model for Morphological Distribution and Evolution of Submarine Cuttings Piles in Offshore Drilling

Summary Offshore oil and gas has effectively alleviated the global shortage of oil and gas resources, and drilling operations are becoming increasingly frequent. However, the cuttings discharged during surface drilling are transported and deposited to form cuttings piles, which pose a serious threat to the marine ecological environment. In this study, we consider the randomness and uncertainty of cuttings movement to divide the transport process into parabola and collision motion between the moving particles and slope particles after falling on the slope surface of cuttings piles. Through specific analysis of the stress state of a single particle in the transport process and changes in momentum distribution of the particle swarm, the evolution model of the morphological distribution of cuttings piles and the nearby flow field is established. This model can quantitatively analyze the evolution law of the morphological distribution of cuttings piles under the action of ocean current and the disturbance law of the flow field near the cuttings piles caused by the invasion of cuttings particles. Comparing the measured data at an offshore drilling field and prediction results of the model of Sun et al. (2020), the relative error of the model amounts to less than 15%, which demonstrates its rationality. The simulation results show that the morphological distribution of cuttings piles and the nearby flow field change significantly under the action of ocean current, and the intensity of evolution is related to the current velocity and cuttings size, which is of great significance for the quantitative analysis of the evolution of cuttings piles under the action of ocean currents and accurate prediction of their morphological distribution.

Predictive models of distribution and abundance of a threatened mountain species show that impacts of climate change overrule those of land use change

AbstractAimClimate is often the sole focus of global change research in mountain ecosystems although concomitant changes in land use might represent an equally important threat. As mountain species typically depend on fine‐scale environmental characteristics, integrating land use change in predictive models is crucial to properly assess their vulnerability. Here, we present a modelling framework that aims at providing more comprehensive projections of both species’ distribution and abundance under realistic scenarios of land use and climate change, and at disentangling their relative effects.LocationSwitzerland.MethodsWe used the ring ouzel (Turdus torquatus), a red‐listed and declining mountain bird species, as a study model. Based on standardized monitoring data collected across the whole country, we fitted high‐resolution ensemble species distribution models to predict current occurrence probability, while spatially explicit density estimates were obtained from N‐mixture models. We then tested for the effects of realistic scenarios of land use (land abandonment versus farming intensification) and climate change on future species distribution and abundance.ResultsOccurrence probability was mostly explained by climatic conditions, so that climate change was predicted to have larger impacts on distribution and abundance than any scenarios of land use change. In the mid‐term (2030–2050), predicted effects of environmental change show a high spatial heterogeneity due to regional differences in climate and dominant land use, with farming intensification identified as an important threat locally. In the long term (2080–2100), climate models forecast a marked upward range shift (up to +560 m) and further population decline (up to −35%).Main conclusionsOur innovative approach highlights the spatio‐temporal heterogeneity in the relative effects of different environmental drivers on species distribution and abundance. The proposed framework thus provides a useful tool not only for better assessing species’ vulnerability in the face of global change, but also for identifying key areas for conservation interventions at a meaningful scale.

An Overview Of The Research Status And Development Trend Of Dynamic Pressure Distribution Testing For Clothing

Clothing pressure can be divided into dynamic pressure and static pressure. When measuring dynamic garment pressure, it is difficult to accurately measure dynamic garment pressure due to the complexity of measuring device and the influence factors such as sliding between skin and fabric during movement. To understand the influencing factors of clothing pressure, clothing pressure measurement technology and the existing dynamic pressure distribution prediction model, so as to achieve accurate prediction of dynamic pressure distribution of clothing, can provide ideas for future research on dynamic pressure comfort of clothing and provide scientific basis for clothing design.

Improving waterbird monitoring and conservation in the Sahel using remote sensing: a case study with the International Waterbird Census in Sudan

In several regions of the world, the remoteness of potential bird hotspots and lack of trained observers have often prevented countries from effectively designing proper monitoring schemes at a national scale. For many countries, it is not known whether certain bird strongholds have been missed that should be included for more complete censuses. Such gaps at national scales, sometimes large, may be detrimental for global monitoring schemes. To address this, we used the irregular participation of Sudan to the International Waterbird Census (IWC) as a case study. We designed and tested a method based on remote‐sensing data of the country’s lowlands to detect open water bodies in order to develop predictive models of the potential distribution of waterbird abundance and diversity. To identify open water bodies and their flooding duration, we used a Modified Normalized Difference Water Index (MNDWI) derived from Landsat 8 data. Field ornithological surveys were then used as ground‐truth data to estimate the method’s accuracy. The statistical results (overall accuracy = 0.972; Kappa index = 0.93) confirmed its effectiveness. Remotely sensed water bodies and additional environmental covariates were then used to build simple habitat models of the distribution of waterbird abundance and diversity based on IWC field survey data. Of the 3119 remotely sensed clusters of open water bodies, three were predicted to host more than 10 000 waterbirds, 89 more than 1000 waterbirds and five more than 30 waterbird species. Located mainly in the southern agricultural floodplains along the main rivers, these predicted waterbird strongholds are therefore recommended for inclusion in the next IWC survey in Sudan. Our findings indicate that using remote sensing to identify open water bodies combined with simple statistical modelling is likely to be a cost‐effective solution to improve IWC sampling and to enhance both waterbird and wetland monitoring in vast under‐surveyed regions.

Birds of a feather moult together: Differences in moulting distribution of four species of storm-petrels.

The non-breeding period of pelagic seabirds, and particularly the moulting stage, is an important, but understudied part of their annual cycle as they are hardly accessible outside of the breeding period. Knowledge about the moulting ecology of seabirds is important to understand the challenges they face outside and within the breeding season. Here, we combined stable carbon (δ13C) and oxygen (δ18O) signatures of rectrices grown during the non-breeding period of two pairs of storm-petrel species breeding in the northern (European storm-petrel, Hydrobates pelagicus, ESP; Leach's storm-petrel, Hydrobates leucorhous, LSP) and southern (black-bellied storm-petrel, Fregetta tropica, BBSP; Wilson's storm-petrel, Oceanites oceanicus, WSP) hemispheres to determine differences in moulting ranges within and between species. To understand clustering patterns in δ13C and δ18O moulting signatures, we examined various variables: species, sexes, years, morphologies (feather growth rate, body mass, tarsus length, wing length) and δ15N. We found that different factors could explain the differences within and between the four species. We additionally employed a geographical distribution prediction model based on oceanic δ13C and δ18O isoscapes, combined with chlorophyll-a concentrations and observational data to predict potential moulting areas of the sampled feather type. The northern species were predicted to moult in temperate and tropical Atlantic zones. BBSP was predicted to moult on the southern hemisphere north of the Southern Ocean, while WSP was predicted to moult further North, including in the Arctic and northern Pacific. While moulting distribution can only be estimated on large geographical scales using δ13C and δ18O, validating predictive outcomes with food availability proxies and observational data may provide valuable insights into important moulting grounds. Establishing those, in turn, is important for conservation management of elusive pelagic seabirds.

A predictive model for cortical bone temperature distribution during drilling.

Bone drilling is an important procedure in medical orthopedic surgery and it is inevitable that heat will be generated during the drilling process and higher temperatures can cause thermal damage to the bone tissue near the drilled hole. Therefore, the capability to obtain the cortical bone drilling temperature distribution area can have great significance for medical bone surgery. Based on the theory of heat transfer, a predictive model for cortical bone drilling temperature distribution was established. The energy distribution coefficient in cortical bone drilling was derived, based on conjugate gradient inversion. A cortical bone drilling experiment platform was built to verify the temperature distribution prediction model. The results show that the model of cortical bone drill temperature distribution could predict accurately the drilling temperature distribution, both for different depths and for different radial distances. Additionally, the effects of different drilling conditions (spindle speed, feed rate, drill diameter) on the temperature of drilling cortical bone were considered.

Using fisheries observation data to develop a predictive species distribution model for endangered sea turtles

AbstractThe Eastern Pacific leatherback turtle population (Dermochelys coriacea) has declined precipitously in recent years. One of the major causes is bycatch from coastal and pelagic fisheries. Fisheries observations are often underutilized, despite strong potential for this data to affect policy. In this study, we created a spatiotemporal species distribution model that synthesizes fisheries observations with remotely sensed environmental data. The model will be developed into a dynamic management tool for the Eastern Pacific leatherback population. We obtained leatherback observation data from multiple fisheries that have operated in the Southeast Pacific (2001–2018). A dynamic Poisson point process model was applied to predict leatherback intensity (observation per unit area) as a function of dynamic environmental covariates. This model serves as a tool for application by managers and stakeholders toward the reduction of leatherback turtle bycatch and provides a modeling framework for analyzing fisheries observations from other vulnerable populations and species.

Comparison of the Hatch of Newly Laid Lycorma delicatula (Hemiptera: Fulgoridae) Eggs From the United States After Exposure to Different Temperatures and Durations of Low Temperature.

Comparisons were made of the effects of temperature and duration of low temperature on hatch of newly laid egg masses of the invasive spotted lanternfly, Lycorma delicatula (White). Egg masses were collected in mid-October 2019 and estimated to be less than 14 d old. There was a significant positive nonlinear relationship between temperature and developmental rate (1/d) for eggs held at constant temperatures. The lower threshold for egg development was estimated as 7.39°C. Eggs held at constant 10, 15, and 20°C were estimated to require 635, 715, and 849 DD7.39, respectively, to develop. Egg hatch was variable, egg hatch rates were highest (58.4%) when held at a constant 15°C, though high rates (52.7%) were also obtained when eggs were held for 84 d at 10°C, then moved to 25°C. Almost all eggs enter diapause since very few eggs were able hatch when moved to 25°C after 7 d of chill at either 5 or 10°C. Chilling at 5 or 10°C increased percentage egg hatch as the duration in chill increased up to ~100 d and eggs held at 10°C were able to complete some or all the post-diapause development before being moved to 25°C. All egg masses were held at constant 16:8 (L:D) photoperiod and 65%RH. Our data suggest that temperature is the driving factor for diapause termination in spotted lanternfly, but other abiotic factors should be investigated. These identified developmental temperature threshold and degree day requirements for egg hatch will improve predictive distribution and phenological models.

Investigation of the Battery Temperature Gradient and its Influence on Battery Aging from Different Thermal Management Methods Under the Application of Electric Tools

Due to its spatial efficiency, phase-change materials (PCMs) are particularly welcomed in the application of the electric tools where extremely high current at 10~15 C-rate is needed. With the Samsung 18650 20R Nickel-Cobalt-Manganese batteries, this study explores the temperature distribution from battery core to surface at extreme working current (15 C) with thermal conductive or storage wrapping materials including HDPE, thermal conductive sheet, thermal conductive adhesive, and PCMs. The proposed temperature distribution prediction model is validated with experimental data. The results indicate that the best battery surface temperature decrease is 18.60 ℃ when 2mm PCMs are applied. The discharge capacity of batteries is thus prolonged by 80-100 % compared to the naked battery cell. On the other hand, the battery core temperature reduction is much lower, only by 6.40 ℃ (from 139.70 ℃ to 133.29 ℃). Accordingly, the internal temperature gradient rises from 35.92 ℃ to 48.12 ℃. This is due to the heat generation exceeded the heat exported from the core to the outside under the 15C working condition of the electric tool. The study further evaluates the effects of high temperature gradient and finds that the capacity fade 90-250 % times faster with 2mm PCMs wrapping.

A study of species distribution prediction based on kernel density estimation

The occurrence of Vespa mandarine in the State of Washington is terrible news for the local Western bees, as Vespa mandarine is a very effective predictor. The Washington State government is taking the Vespa mandarine invasion seriously and hopes that people will report it when pests are found. This article processes the sighting reports provided, establishes a model, predicts the potential distribution of coriander, and can effectively provide the possibility of a new report being positive so as to quickly determine the most likely positive report and help the government allocate resources rationally. First, a species distribution prediction model based on kernel density estimation (KDE) was established. We took the Gaussian function as the kernel function, performed KDE on the given data, and controlled the search radius of the kernel function to be 30km from the center of the nest to obtain a probability density heat map reflecting the possibility of coriander spreading to a certain area, which shows that the scope of the spread and the possibility of each location are predictable. It is also essential to consider the circumstances under which the hives are cleared. Therefore, when a hive is removed, we will update the KDE model. If the distance is less than 30km, the point closest to the coordinates of the cleared hive will be deleted.

Modelling the habitat selection of the bearded vulture to predict areas of potential conflict with wind energy development in the Swiss Alps

Global warming impels countries to dramatically reduce their release of greenhouse gas emissions and increase their reliance on green energy, notably wind power. Yet, without cautious planning, the sprawl of wind turbines could negatively impact biodiversity, especially flying vertebrates that are otherwise already threatened. Inherent risks for vulnerable and endangered species are usually mitigated by banning constructions within buffer areas around nesting locations. This approach, however, neglects species’ range dynamics and particularly falls short of protecting expanding populations, as in the case of natural returns or reintroduction programmes. We present here an alternative approach to mitigate wildlife-infrastructure conflicts, applying it to the bearded vulture, a species reintroduced in the European Alps. Combining casual observations and GPS locations of tagged individuals, we built several predictive distribution models with respect to bearded vulture age class and season and tested for models’ ability to correctly predict its future expansion in the Alps. Although immature and adult birds showed different habitat selection patterns, both in summer and winter, wide areas of the Swiss Alps (40%) offer suitable habitat. The above combined information enabled correctly predicting today’s use by breeding bearded vultures of previously unused areas. This study not only provides a detailed analysis of the bearded vulture’s ecological requirements in the Alps but also helps delineating areas where conflicts with wind energy production and other aerial infrastructure will likely occur in Switzerland. The resulting maps provide a large-scale planning tool that companies, landscape planners and wildlife managers can use in any environmental risk assessments.

Comparative swimming performance and behaviour of three benthic fish species: The invasive round goby (Neogobius melanostomus), the native bullhead (Cottus gobio), and the native gudgeon (Gobio gobio)

AbstractEfforts to restore river ecosystem connectivity focus predominantly on diadromous, economically important fish species, and less attention is given to nonmigratory, small‐bodied, benthic fish species. Data on benthic fish swimming performance and behaviour in comparison with ecologically similar native species are especially relevant for the study of one of the most successful invaders in the last decades: the Ponto‐Caspian gobiid species Neogobius melanostomus. To evaluate future measures against its further upstream dispersal, we conducted comparative swimming performance and behaviour experiments with round goby and two native species: the European bullhead (Cottus gobio) and the gudgeon (Gobio gobio). Experiments in a swim tunnel revealed a high variation in the swimming performance and behaviour within and among the three species. Gudgeon performed best in both Ucrit and Usprint experiments and displayed a rather continuous, subcarangiform swimming mode, whereas bullhead and round goby displayed a burst‐and‐hold swimming mode. Experiments in a vertical slot pass model, which contained a hydraulic barrier as a challenge to upstream movement, confirmed the high swimming performance of gudgeon. Gudgeon dispersed upstream even across the hydraulic barrier at the highest flow velocities. Round goby showed a higher capability to disperse upstream than bullhead, but failed to pass the hydraulic barrier. Our results on comparative swimming performance and behaviour can inform predictive distribution modelling and range expansion models, and also inform the design of selective barriers to prevent the round goby from dispersing farther upstream.

Root-associated community composition and co-occurrence patterns of fungi in wild grapevine

Crops’ wild relatives host a wide range of microorganisms, including some beneficial species that are not found or are under-represented in the domesticated crops. Our goal was to study the underexplored composition of root-associated fungal communities in endangered wild grapevines. We found high taxonomic diversity representing multiple trophic guilds that include beneficial symbiotrophs and endophytes. Soil factors explain a relatively small part of their overall variability. In contrast, the majority of the associated fungal taxa shows a close fit to the neutral model for prediction of their distributions. Only beneficial arbuscular mycorrhizal fungi and the pathogenic Ilyonectria depart from the neutral distribution model and form intimate interactions with the plant host. In addition, pathogenic fungi rarely occurred in samples that included ectomycorrhizal fungi, which suggested potentially applicable inter-microorganism interactions. High abundance and diversity of fungal endophytes on the wild grapevine roots highlight the need for their careful consideration in future studies. • Wild grapevine roots host high diversity of fungi from multiple trophic guilds. • Root-associated fungi fit mainly into neutral model for distribution prediction. • Arbuscular mycorrhizal fungi and Ilyonectria most intimately relate to the host. • Pathogenic fungi rarely occurred in samples that included ectomycorrhizal fung.

Species distribution models for deep-water coral habitats that account for spatial uncertainty in trap-camera fishery data

Bottom-contact fisheries present risks to vulnerable marine ecosystems (VMEs) such as deep-water coral and sponge communities. Managing these risks requires better knowledge about VME spatial distribution within fishing areas. In this paper, we develop predictive species distribution models for alcyonacean (Order Alcyonacea) corals at SGaan Kinghlas-Bowie Seamount (SK-B) in British Columbia, Canada, based on direct presence/absence observations obtained from deep-water cameras attached to commercial fishing gear. We obtained in situ presence/absence observations of deep-water corals (Order Alcyonacea, Order Antipatharia, Order Pennatulacea, Family Stylasteridae) and sponges (Class Hexactinellida, Class Demospongiae) at 124 locations during commercial fishing trips at the SK-B marine protected area. We developed species distribution models for alcyonacean corals at SK-B and compared the performance of models using 4 different estimators of trap landing position (surface drop position and 3 Bayesian estimators) to account for spatial uncertainty in observation locations. We found that the different estimators for landing position affected variable selection, model performance, and model predictions. The best-fitting models using the 4 different landing position estimators had mean AUC values ranging from 0.71 to 0.78 and maximum kappa values ranging from 0.36 to 0.47. This study demonstrates how collaborative research surveys with commercial fisheries can provide fine-scale spatial data for coral and sponge habitat mapping using an approach that is scalable for benthic habitat risk assessment for large, possibly remote, areas where fisheries operate.

Predicting habitat suitability of filter-feeder communities in a shallow marine environment, New Zealand

The distribution of benthic ecosystems, dominated by filter-feeding communities, is highly influenced by the seabed geomorphology. However, the spatial variation in settlement of these species is also affected by near-bottom currents and any changes in light, nutrient concentration and food quality often associated with increases of suspended sediment concentrations within the water column. Detailed predictions of the geographic distribution of filter-feeder species and a deeper understanding of the physical processes influencing their distribution patterns is key for effective management and conservation. To date, predictive distribution modelling has been derived essentially from geomorphological parameters, mainly using spatially limited observations. In this study, seabed mapping, oceanographic modelling, hydrographic records and biological observations are integrated to provide high-resolution prediction of filter-feeder habitat distribution within Queen Charlotte Sound/Tōtaranui and Tory Channel/Kura Te Au, South Island of New Zealand. The aim is to evaluate potential suitable habitat areas for filter-feeders to inform where habitat restoration management should focus efforts to recover communities such as the horse mussel (Atrina zelandica) or the green-lipped mussel (Perna canaliculus), both of which have high economic impact in New Zealand. To accomplish this, Maximum Entropy (MaxEnt) predictive modelling was used to produce Habitat Suitability (HS) maps, using geomorphological parameters and seafloor classification information. Final HS maps also incorporated oceanographic and sediment dynamic information, showing that filter-feeder habitat distribution is highly influenced by the hydrodynamics and sedimentary processes apart from the seafloor geomorphology. Filter-feeder communities inhabit quiescent areas, limited by depth, slope and sediment type; and coincide with regions presenting low near-bottom currents and low turbidity levels. Additionally, the obtained results reveal the effects of the coastal settlements and major marine traffic routes, limiting the suitable habitats to areas with less human impact. This study demonstrates that a multidisciplinary approach is crucial to better predict the spatial distribution of benthic communities, which is key to improve benthic habitat restoration and recovery assessments.

Data quantity is more important than its spatial bias for predictive species distribution modelling.

Biological records are often the data of choice for training predictive species distribution models (SDMs), but spatial sampling bias is pervasive in biological records data at multiple spatial scales and is thought to impair the performance of SDMs. We simulated presences and absences of virtual species as well as the process of recording these species to evaluate the effect on species distribution model prediction performance of (1) spatial bias in training data, (2) sample size (the average number of observations per species), and (3) the choice of species distribution modelling method. Our approach is novel in quantifying and applying real-world spatial sampling biases to simulated data. Spatial bias in training data decreased species distribution model prediction performance, but sample size and the choice of modelling method were more important than spatial bias in determining the prediction performance of species distribution models.

Searching for manatees in the dark waters of a transboundary river between Mexico and Belize: a predictive distribution model

Antillean manatees in the Hondo River have been recorded from aerial and aquatic surveys, and interviews. However, these studies have been conducted only in the lower riverbed, leaving a gap of information about their presence and habitat characteristics in the rest of the river. We characterize and determine the ecohydrological variables influencing the presence and habitat use of manatees in the Hondo River. During 2017 and 2018, 30 based-boat field trips were conducted in five consecutive transects of 15 km each. A mixed methodology was used for manatee detection: side-scan sonar, direct sightings, and feces collection. Ecohydrological variables were measured in all transects and fixed points. The survey effort was 136.5 h. We recorded 123 manatees: 47% were observations during the boat-based surveys, 29% were at fixed points, and 24% were opportunistic. Additionally, 10 manatee feces were found. The first transect of the river showed the highest relative abundance for the two sampled seasons (windy = 0.27 manatees/km, dry = 0.55 manatees/km). According to the Poisson model, the estimated population was equal to 51 manatees. A random forest model suggested high probability of observing manatees in the first transects and decreasing at the upstream. The ecohydrological variables influencing the detection of manatees were conductivity, transparency, depth, and proximity to the Four Mile lagoon. The first two transects have ecohydrological characteristics that makes a benign environment for refuge, rest and feeding of manatees. We recommend carrying out conservation efforts in the first transects, such as protection and the regulation of boat transit.

The Model of How Languages will Develop

The total number of speakers of a language may increase or decrease over time because of various and complicated influences. In this paper, we build the quantitative prediction model based on gray estimation, FCM model and geographical distribution prediction model to predict the distribution of language users. In addition, we conducted a sensitivity analysis of the model to facilitate future research.