A species distribution model of the giant kelp Macrocystis pyrifera: Worldwide changes and a focus on the Southeast Pacific

Worldwide climate-driven shifts in the distribution of species is of special concern when it involves habitat-forming species. In the coastal environment, large Laminarian algae-kelps-form key coastal ecosystems that support complex and diverse food webs. Among kelps, Macrocystis pyrifera is the most widely distributed habitat-forming species and provides essential ecosystem services. This study aimed to establish the main drivers of future distributional changes on a global scale and use them to predict future habitat suitability. Using species distribution models (SDM), we examined the changes in global distribution of M. pyrifera under different emission scenarios with a focus on the Southeast Pacific shores. To constrain the drivers of our simulations to the most important factors controlling kelp forest distribution across spatial scales, we explored a suite of environmental variables and validated the predictions derived from the SDMs. Minimum sea surface temperature was the single most important variable explaining the global distribution of suitable habitat for M. pyrifera. Under different climate change scenarios, we always observed a decrease of suitable habitat at low latitudes, while an increase was detected in other regions, mostly at high latitudes. Along the Southeast Pacific, we observed an upper range contraction of -17.08° S of latitude for 2090-2100 under the RCP8.5 scenario, implying a loss of habitat suitability throughout the coast of Peru and poleward to -27.83° S in Chile. Along the area of Northern Chile where a complete habitat loss is predicted by our model, natural stands are under heavy exploitation. The loss of habitat suitability will take place worldwide: Significant impacts on marine biodiversity and ecosystem functioning are likely. Furthermore, changes in habitat suitability are a harbinger of massive impacts in the socio-ecological systems of the Southeast Pacific.

Potential Distribution of Cedrela odorata L. in Mexico according to Its Optimal Thermal Range for Seed Germination under Different Climate Change Scenarios

Cedrela odorata is a native tree of economic importance, as its wood is highly demanded in the international market. In this work, the current and future distributions of C. odorata in Mexico under climate change scenarios were analyzed according to their optimal temperature ranges for seed germination. For the present distribution, 256 localities of the species' presence were obtained from the Global Biodiversity Information Facility (GBIF) database and modelled with MaxEnt. For the potential distribution, the National Center for Atmospheric Research model (CCSM4) was used under conservative and drastic scenarios (RCP2.6 and RCP8.5 Watts/m², respectively) for the intermediate future (2050) and far future (2070). Potential distribution models were built from occurrence data within the optimum germination temperature range of the species. The potential distribution expanded by 5 and 7.8% in the intermediate and far future, respectively, compared with the current distribution. With the increase in temperature, adequate environmental conditions for the species distribution should be met in the central Mexican state of Guanajuato. The states of Chihuahua, Mexico, Morelos, Guerrero, and Durango presented a negative trend in potential distribution. Additionally, in the far future, the state of Chihuahua it is likely to not have adequate conditions for the presence of the species. For the prediction of the models, the precipitation variable during the driest month presented the greatest contribution. When the humidity is not limiting, the thermal climatic variables are the most important ones. Models based on its thermal niche for seed germination allowed for the identification of areas where temperature will positively affect seed germination, which will help maximize the establishment of plant populations and adaptation to different climate change scenarios.

Mapping breeding bird species richness at management-relevant resolutions across the United States

Human activities alter ecosystems everywhere, causing rapid biodiversity loss and biotic homogenization. These losses necessitate coordinated conservation actions guided by biodiversity and species distribution spatial data that cover large areas yet have fine-enough resolution to be management-relevant (i.e., ≤5 km). However, most biodiversity products are too coarse for management or are only available for small areas. Furthermore, many maps generated for biodiversity assessment and conservation do not explicitly quantify the inherent tradeoff between resolution and accuracy when predicting biodiversity patterns. Our goals were to generate predictive models of overall breeding bird species richness and species richness of different guilds based on nine functional or life-history-based traits across the conterminous United States at three resolutions (0.5, 2.5, and 5 km) and quantify the tradeoff between resolution and accuracy and, hence, relevance for management of the resulting biodiversity maps. We summarized 18 years of North American Breeding Bird Survey data (1992-2019) and modeled species richness using random forests, including 66 predictor variables (describing climate, vegetation, geomorphology, and anthropogenic conditions), 20 of which we newly derived. Among the three spatial resolutions, the percentage variance explained ranged from 27% to 60% (median = 54%; mean = 57%) for overall species richness and 12% to 87% (median = 61%; mean = 58%) for our different guilds. Overall species richness and guild-specific species richness were best explained at 5-km resolution using ~24 predictor variables based on percentage variance explained, symmetric mean absolute percentage error, and root mean square error values. However, our 2.5-km-resolution maps were almost as accurate and provided more spatially detailed information, which is why we recommend them for most management applications. Our results represent the first consistent, occurrence-based, and nationwide maps of breeding bird richness with a thorough accuracy assessment that are also spatially detailed enough to inform local management decisions. More broadly, our findings highlight the importance of explicitly considering tradeoffs between resolution and accuracy to create management-relevant biodiversity products for large areas.

[Application of integrated species distribution models in parasitic diseases prevention and control: a review]

Species distribution model, a mathematical model theoretically based on the temporal and spatial locations of species in the ecosystem and their associations with other species, is useful to display the current species distribution and predict the future species distribution, which has been widely applied in ecology, biogeography and spatial transmission of infectious diseases. Integrated species distribution model is a comprehensive, summative and reliable combination model that simultaneously uses multiple species distribution models or integrates multiple data sources for modeling. This review describes the theory and classification of species distribution models, and summarizes the creation and verification of integrated species distribution models and their applications in parasitic disease prevention and control. During the practical applications of integrated species distribution models, the study scale, niche matching, big data utilization, combination of multidisciplinary methods and knowledge background and ecological implications should be emphasized.

Extension of the Virtual Cell Based Assay from a 2-D to a 3-D Cell Culture Model

Prediction of chemical toxicity is very useful in risk assessment. With the current paradigm shift towards the use of in vitro and in silico systems, we present herein a theoretical mathematical description of a quasi-diffusion process to predict chemical concentrations in 3-D spheroid cell cultures. By extending a 2-D Virtual Cell Based Assay (VCBA) model into a 3-D spheroid cell model, we assume that cells are arranged in a series of concentric layers within the sphere. We formulate the chemical quasi-diffusion process by simplifying the spheroid with respect to the number of cells in each layer. The system was calibrated and tested with acetaminophen (APAP). Simulated predictions of APAP toxicity were compared with empirical data from in vitro measurements by using a 3-D spheroid model. The results of this first attempt to extend the VCBA model are promising - they show that the VCBA model simulates close correlation between the influence of compound concentration and the viability of the HepaRG 3-D cell culture. The 3-D VCBA model provides a complement to current in vitro procedures to refine experimental setups, to fill data gaps and help in the interpretation of in vitro data for the purposes of risk assessment.

Sand Fly Fauna, Spatial Distribution of Lutzomyia longipalpis (Diptera: Psychodidae), and Climate Factors in Dourados, Brazil

Studies of the geographic distribution of sand flies and the factors associated with their occurrence are necessary to understand the risk of leishmaniasis transmission. The objective of this study was to characterize the sand fly fauna, particularly the spatial distribution of Lutzomyia longipalpis (Lutz & Neiva), and correlate these with climate factors in the Dourados municipality, Brazil. The collection of sand flies was carried out with CDC Light Traps over two periods: at six sites for three consecutive nights each month from August 2012 to July 2013; and at four other sites for two consecutive nights each month from April 2017 to February 2018. We collected 591 sand flies in the first period and 121 in the second period for a total of 712 sand flies; 697 of the total collected were Lu. longipalpis. The minimum and maximum sand fly infestation rate (sites with vector presence) was 11.1% and 83.33% in the first period, and 0% and 50.0% in the second period. No sand flies with Leishmania were identified via PCR. Lu. longipalpis presented an aggregate disposition with excellent adjustment. Rainfall and relative humidity were the abiotic factors that influenced the vector infestation level. The aggregate distribution for this species was predicted by the environmental factors that favor the proliferation of Lu. longipalpis. The results of this study should assist in devising measures to control sand flies in Dourados, Brazil.

Landscape epidemiology of Batrachochytrium salamandrivorans: reconciling data limitations and conservation urgency

Starting in 2010, rapid fire salamander (Salamandra salamandra) population declines in northwestern Europe heralded the emergence of Batrachochytrium salamandrivorans (Bsal), a salamander-pathogenic chytrid fungus. Bsal poses an imminent threat to global salamander diversity owing to its wide host range, high pathogenicity, and long-term persistence in ecosystems. While there is a pressing need to develop further research and conservation actions, data limitations inherent to recent pathogen emergence obscure necessary insights into Bsal disease ecology. Here, we use a hierarchical modeling framework to describe Bsal landscape epidemiology of outbreak sites in light of these methodological challenges. Using model selection and machine learning, we find that Bsal presence is associated with humid and relatively cool, stable climates. Outbreaks are generally located in areas characterized by low landscape heterogeneity and low steepness of slope. We further find an association between Bsal presence and high trail density, suggesting that human-mediated spread may increase risk for spillover between populations. We then use distribution modeling to show that favorable conditions occur in lowlands influenced by the North Sea, where increased survey effort is needed to determine how Bsal impacts local newt populations, but also in hill- and mountain ranges in northeastern France and the lower half of Germany. Finally, connectivity analyses suggest that these hill- and mountain ranges may act as stepping stones for further spread southward. Our results provide initial insight into regional environmental conditions underlying Bsal epizootics, present updated invasibility predictions for northwestern Europe, and lead us to discuss a wide variety of potential survey and research actions needed to advance future conservation and mitigation efforts.

Assessing the Impact of Climate Change on the Distribution of Lime (16srii-B) and Alfalfa (16srii-D) Phytoplasma Disease Using MaxEnt

Witches' broom disease has led to major losses in lime and alfalfa production in Oman. This paper identifies bioclimatic variables that contribute to the prediction of distribution of witches' broom disease in current and future climatic scenarios. It also explores the expansion, reduction, or shift in the climatic niche of the distribution of the disease across the different geographical areas of the entire country (309,501 km²). The maximum entropy model (MaxEnt) and geographical information system were used to investigate the potential suitability of habitats for the phytoplasma disease. This study used current (1970-2000) and future projected climatic scenarios (2021-2040, 2041-2060, 2061-2080, and 2081-2100) to model the distribution of phytoplasma for lime trees and alfalfa in Oman. Bioclimatic variables were downloaded from WorldClim with ± 60 occurrence points for lime trees and alfalfa. The area under the curve (AUC) was used to evaluate the model's performance. Quantitatively, the results showed that the mean of the AUC values for lime (16SrII-B) and alfalfa (16SrII-D) future distribution for the periods of 2021-2040, 2041-2060, 2061-2080, and 2081-2100 were rated as "excellent", with the values for the specified time periods being 0.859, 0.900, 0.931, and 0.913 for 16SrII-B; and 0.826, 0.837, 08.58, and 0.894 for 16SrII-D respectively. In addition, this study identified the hotspots and proportions of the areas that are vulnerable under the projected climate-change scenarios. The area of current (2021-2040) highly suitable distribution within the entire country for 16SrII-D was 19474.2 km² (7.1%), while for 16SrII-B, an area of 8835 km² (3.2%) was also highly suitable for the disease distribution. The proportions of these suitable areas are very significant from the available arable land standpoint. Therefore, the results from this study will be of immense benefit and will also bring significant contributions in mapping the areas of witches' broom diseases in Oman. The results will equally aid the development of new strategies and the formulation of agricultural policies and practices in controlling the spread of the disease across Oman.

Medical Emergency Resource Allocation Model in Large-Scale Emergencies Based on Artificial Intelligence: Algorithm Development

Background

Before major emergencies occur, the government needs to prepare various emergency supplies in advance. To do this, it should consider the coordinated storage of different types of materials while ensuring that emergency materials are not missed or superfluous.

Objective

This paper aims to improve the dispatch and transportation efficiency of emergency materials under a model in which the government makes full use of Internet of Things technology and artificial intelligence technology.

Methods

The paper established a model for emergency material preparation and dispatch based on queueing theory and further established a workflow system for emergency material preparation, dispatch, and transportation based on a Petri net, resulting in a highly efficient emergency material preparation and dispatch simulation system framework.

Results

A decision support platform was designed to integrate all the algorithms and principles proposed.

Conclusions

The resulting framework can effectively coordinate the workflow of emergency material preparation and dispatch, helping to shorten the total time of emergency material preparation, dispatch, and transportation.

Local cold adaption increases the thermal window of temperate mussels in the Arctic

Species expand towards higher latitudes in response to climate warming, but the pace of this expansion is related to the physiological capacity to resist cold stress. However, few studies exist that have quantified the level of inter-population local adaptation in marine species freeze tolerance, especially in the Arctic. We investigated the importance of cold adaptation and thermal window width towards high latitudes from the temperate to the Arctic region. We measured upper and lower lethal air temperatures (i.e. LT and LT50) in temperate and Arctic populations of blue mussels (Mytilus edulis), and analysed weather data and membrane fatty acid compositions, following emersion simulations. Both populations had similar upper LT (~38 °C), but Arctic mussels survived 4°C colder air temperatures than temperate mussels (-13 vs. -9°C, respectively), corresponding to an 8% increase in their thermal window. There were strong latitudinal relationships between thermal window width and local air temperatures, indicating Arctic mussels are highly adapted to the Arctic environment where the seasonal temperature span exceeds 60°C. Local adaptation and local habitat heterogeneity thus allow leading-edge M. edulis to inhabit high Arctic intertidal zones. This intraspecific pattern provides insight into the importance of accounting for cold adaptation in climate change, conservation and biogeographic studies.

Deathbed choice by ASF-infected wild boar can help find carcasses

African swine fever (ASF) is a fatal disease infectious to wild and domesticated suids. This disease entered the European Union in 2014 and recently reached western Europe, with the first cases observed in Belgium in September 2018. Carcasses of ASF-infected wild boar play an important role in the spread and persistence of the virus in the environment. Thus, rapidly finding and removing carcasses is a crucial measure for effective ASF control. Using distribution modelling, we investigated whether the fine-scale distribution of ASF-infected animals can be predicted and support wild boar carcass searches. Our results suggest that ASF-infected wild boar selected deathbeds in cool and moist habitats; thus, deathbed choice was mostly influenced by topographic and water-dependent covariates. Furthermore, we show that in the case of an epidemic, it is important to quickly collect a minimum of 75-100 carcasses with exact locations to build a well-performing and efficient carcass distribution model. The proposed model provides an indication of where carcasses are most likely to be found and can be used as a guide to strategically allocate resources.

Pteropus lylei primarily forages in residential areas in Kandal, Cambodia

Bats are the second most species-rich Mammalian order and provide a wide range of ecologically important and economically significant ecosystem services. Nipah virus is a zoonotic emerging infectious disease for which pteropodid bats have been identified as a natural reservoir. In Cambodia, Nipah virus circulation has been reported in Pteropus lylei, but little is known about the spatial distribution of the species and the associated implications for conservation and public health.We deployed Global Positioning System (GPS) collars on 14 P. lylei to study their movements and foraging behavior in Cambodia in 2016. All of the flying foxes were captured from the same roost, and GPS locations were collected for 1 month. The habitats used by each bat were characterized through ground-truthing, and a spatial distribution model was developed of foraging sites.A total of 13,643 valid locations were collected during the study. Our study bats flew approximately 20 km from the roost each night to forage. The maximum distance traveled per night ranged from 6.88-105 km and averaged 28.3 km. Six of the 14 bats visited another roost for at least one night during the study, including one roost located 105 km away.Most foraging locations were in residential areas (53.7%) followed by plantations (26.6%). Our spatial distribution model confirmed that residential areas were the preferred foraging habitat for P. lylei, although our results should be interpreted with caution due to the limited number of individuals studied. Synthesis and applications: Our findings suggest that the use of residential and agricultural habitats by P. lylei may create opportunities for bats to interact with humans and livestock. They also suggest the importance of anthropogenic habitats for conservation of this vulnerable and ecologically important group in Cambodia. Our mapping of the probability of occurrence of foraging sites will help identification of areas where public awareness should be promoted regarding the ecosystem services provided by flying foxes and potential for disease transmission through indirect contact.

Predicting the range of a regionally threatened, benthic fish using species distribution models and field surveys

Understanding a species' historical and current distribution is critical when making conservation and management decisions. Recent observations in headwater streams of northern Illinois, USA, where no previous records of Iowa Darters Etheostoma exile occurred, revealed the need to re-evaluate its state-wide distribution. We conducted a series of species distribution modelling procedures coupled with targeted field surveys to generate historical and contemporary distribution models. The historical distribution model indicated E. exile distributions were concentrated across extreme northern Illinois. Both contemporary models included results of the historical model as a model variable. Based on the initial contemporary model, 30 potential Iowa Darter sites, 10 each of three groups representing low, medium and high model scores, were sampled during the summer of 2016. Field surveys provided nine new E. exile localities and presence-absence data from field surveys were then applied to revise the contemporary model. The revised contemporary model suggests a decrease in certain areas of its historic Illinois range, as well as areas where it has potentially expanded its range.

Vulnerability of eastern US tree species to climate change

Climate change is expected to alter the distribution of tree species because of critical environmental tolerances related to growth, mortality, reproduction, disturbances, and biotic interactions. How this is realized in 21st century remains uncertain, in large part due to limitations on plant migration and the impacts of landscape fragmentation. Understanding these changes is of particular concern for forest management, which requires information at an appropriately fine spatial resolution. Here we provide a framework and application for tree species vulnerability to climate change in the eastern United States that accounts for influential drivers of future distributions. We used species distribution models to project changes in habitat suitability at 800 m for 40 tree species that vary in physiology, range, and environmental niche. We then developed layers of adaptive capacity based on migration potential, forest fragmentation, and propagule pressure. These were combined into metrics of vulnerability, including an overall index and spatially explicit categories designed to inform management. Despite overall favorable changes in suitability, the majority of species and the landscape were considered vulnerable to climate change. Vulnerability was significantly exacerbated by projections of pests and pathogens for some species. Northern and high-elevation species tended to be the most vulnerable. There were, however, some notable areas of particular resilience, including most of West Virginia. Our approach combines some of the most important considerations for species vulnerability in a straightforward framework, and can be used as a tool for managers to prioritize species, areas, and actions.

A model of chlorpyrifos distribution and its biochemical effects on the liver and kidneys of rats

This study investigated the main target sites of chlorpyrifos (CPF), its effect on biochemical indices, and the pathological changes observed in rat liver and kidney function using gas chromatography/mass spectrometry. Adult female Wistar rats (n = 12) were randomly assigned into two groups (one control and one test group; n = 6 each). The test group received CPF via oral gavage for 21 days at 5 mg/kg daily. The distribution of CPF was determined in various organs (liver, brain, heart, lung, kidney, ovary, adipose tissue, and skeletal muscle), urine and stool samples using GCMS. Approximately 6.18% of CPF was distributed in the body tissues, and the highest CPF concentration (3.80%) was found in adipose tissue. CPF also accumulated in the liver (0.29%), brain (0.22%), kidney (0.10%), and ovary (0.03%). Approximately 83.60% of CPF was detected in the urine. CPF exposure resulted in a significant increase in plasma transaminases, alkaline phosphatase, and total bilirubin levels, a significant reduction in total protein levels and an altered lipid profile. Oxidative stress due to CPF administration was also evidenced by a significant increase in liver malondialdehyde levels. The detrimental effects of CPF on kidney function consisted of a significant increase in plasma urea and creatinine levels. Liver and kidney histology confirmed the observed biochemical changes. In conclusion, CPF bioaccumulates over time and exerts toxic effects on animals.

A unique drug distribution process for radium Ra 223 dichloride injection and its implication for product quality, patient privacy, and delineation of professional responsibilities

On May 15, 2013, Bayer Healthcare Pharmaceuticals announced that it had received marketing approval for the therapeutic radioactive medication radium Ra 223 dichloride injection (Xofigo; Ra 223). The product acquisition and distribution process for hospital-based nuclear pharmacies and nuclear medicine services is unlike any other. The product is distributed as a low-risk compounded sterile preparation through a single compounding nuclear pharmacy located in Denver, Colorado, pursuant to a prescription. This model for drug distribution and delivery to the user institution has implications for product quality, patient privacy, and delineation of professional responsibilities.

Sensitivity to thermal extremes in Australian Drosophila implies similar impacts of climate change on the distribution of widespread and tropical species

Climatic factors influence the distribution of ectotherms, raising the possibility that distributions of many species will shift rapidly under climate change and/or that species will become locally extinct. Recent studies have compared performance curves of species from different climate zones and suggested that tropical species may be more susceptible to climate change than those from temperate environments. However, in other comparisons involving responses to thermal extremes it has been suggested that mid-latitude populations are more susceptible. Using a group of 10 closely related Drosophila species with known tropical or widespread distribution, we undertake a detailed investigation of their growth performance curves and their tolerance to thermal extremes. Thermal sensitivity of life history traits (fecundity, developmental success, and developmental time) and adult heat resistance were similar in tropical and widespread species groups, while widespread species had higher adult cold tolerance under all acclimation regimes. Laboratory measurements of either population growth capacity or acute tolerance to heat and cold extremes were compared to daily air temperature under current (2002-2007) and future (2100) conditions to investigate if these traits could explain current distributions and, therefore, also forecast future effects of climate change. Life history traits examining the thermal sensitivity of population growth proved to be a poor predictor of current species distributions. In contrast, we validate that adult tolerance to thermal extremes provides a good correlate of current distributions. Thus, in their current distribution range, most of the examined species experience heat exposure close to, but rarely above, the functional heat resistance limit. Similarly, adult functional cold resistance proved a good predictor of species distribution in cooler climates. When using the species' functional tolerance limits under a global warming scenario, we find that both tropical and widespread Drosophila species will face a similar proportional reduction in distribution range under future warming.

Using a high-dimensional graph of semantic space to model relationships among words

The GOLD model (Graph Of Language Distribution) is a network model constructed based on co-occurrence in a large corpus of natural language that may be used to explore what information may be present in a graph-structured model of language, and what information may be extracted through theoretically-driven algorithms as well as standard graph analysis methods. The present study will employ GOLD to examine two types of relationship between words: semantic similarity and associative relatedness. Semantic similarity refers to the degree of overlap in meaning between words, while associative relatedness refers to the degree to which two words occur in the same schematic context. It is expected that a graph structured model of language constructed based on co-occurrence should easily capture associative relatedness, because this type of relationship is thought to be present directly in lexical co-occurrence. However, it is hypothesized that semantic similarity may be extracted from the intersection of the set of first-order connections, because two words that are semantically similar may occupy similar thematic or syntactic roles across contexts and thus would co-occur lexically with the same set of nodes. Two versions the GOLD model that differed in terms of the co-occurence window, bigGOLD at the paragraph level and smallGOLD at the adjacent word level, were directly compared to the performance of a well-established distributional model, Latent Semantic Analysis (LSA). The superior performance of the GOLD models (big and small) suggest that a single acquisition and storage mechanism, namely co-occurrence, can account for associative and conceptual relationships between words and is more psychologically plausible than models using singular value decomposition (SVD).

Incorporating uncertainty in predictive species distribution modelling

Motivated by the need to solve ecological problems (climate change, habitat fragmentation and biological invasions), there has been increasing interest in species distribution models (SDMs). Predictions from these models inform conservation policy, invasive species management and disease-control measures. However, predictions are subject to uncertainty, the degree and source of which is often unrecognized. Here, we review the SDM literature in the context of uncertainty, focusing on three main classes of SDM: niche-based models, demographic models and process-based models. We identify sources of uncertainty for each class and discuss how uncertainty can be minimized or included in the modelling process to give realistic measures of confidence around predictions. Because this has typically not been performed, we conclude that uncertainty in SDMs has often been underestimated and a false precision assigned to predictions of geographical distribution. We identify areas where development of new statistical tools will improve predictions from distribution models, notably the development of hierarchical models that link different types of distribution model and their attendant uncertainties across spatial scales. Finally, we discuss the need to develop more defensible methods for assessing predictive performance, quantifying model goodness-of-fit and for assessing the significance of model covariates.