Application Of Species Distribution Models Research Articles

Identification of highly pathogenic avian influenza suitable areas for wild birds using species distribution models in South Korea.

Event Abstract Back to Event Identification of highly pathogenic avian influenza suitable areas for wild birds using species distribution models in South Korea. Lee Kyuyoung1*, Dae-sung Yu2*, Beatriz Martínez-López1, Jaber A. Belkhiria1, Sung-il Kang2, Hachung Yoon2, Seong-Keun Hong2, ILSEOB LEE2, Han-Mo Son2 and Kwangnyeong Lee2 1 Department of Medicine & Epidemiology, School of Veterinary Medicine, University of California, Davis, United States 2 Animal and Plant Quarantine Agency (South Korea), Republic of Korea Highly pathogenic avian influenza (HPAI) virus is influenza A type virus with high mortality and morbidity in the broad range of host species domestic and wild birds to humans. HPAI infection has been a high-priority concern in global poultry industry because of consistent generation and circulation of novel HPAI strains, and consequent tremendous financial losses. Wild birds are considered one of the most important sources of novel HPAI introductions in poultry farms due to the experimental evidence of their asymptomatic infection with viral shedding, genetic closeness of HPAI virus identified in domestic poultry and wild birds, and spatial and temporal coincidence of identification of HPAI in wild birds and domestic poultry. The poultry industry in South Korea has annually suffered from the introduction of novel HPAI strains since early 2000s. HPAI infection in annually migrated wild birds has been carefully monitored to rapidly establish preventive measures of HPAI introduction into poultry farms in South Korea. However, current understanding of highly risk areas or suitable areas for HPAI in wild birds and contribution of other geographical and environmental factors contributing to HPAI vírus suitability is limited. Application of species distribution models based on digitalized geographical and environmental information facilitates our understanding about suitability of wild species’ habitat and its association with environmental and geographical factors. Furthermore, species distribution modelling can be applied to identify high-risk areas of potential disease transmission at the interface between wild species and domestic animals. Our study aimed to evaluate the areas with higher suitability/risk for HPAI identification in wild birds in South Korea and to describe what is the influence or association with the different environmental and geographical factors. Our results will help to not only have a better understanding of the ecology of HPAI in wild birds but also to establish more effective, risk-based, surveillance to prevent novel HPAI introductions into domestic poultry farms in South Korea We obtained land cover, monthly climate (precipitation, temperature and wind speed) and ecological preservation area (Level 1-3) data of South Korea in digitalized form as environmental and geographical data. The observation records of 7 species of wild birds (Baikal teal, white-front goose, common teal, mandarin duck, mallard, bean goose, spot-billed duck) in 206 habitats in South Korea from 1999 to 2017 were used to estimate the suitability map for the wild bird distribution in South Korea. The geographical records of wild bird HPAI surveillance from 2014 to 2018 was combined with 7 wild birds species distribution and environmental and geographical data to estimate the suitability map of HPAI identification in wild birds. Each suitability maps was estimated by maximum entropy approach (Maxent model) via the “dismo” package in R studio. 3. Results and Discussion Our study presents the suitability map of HPAI identification in wild birds and how geographical, environmental factors and 7 wild birds species distributions are contributing to the prediction. These results will not only provide a high-resolution map for the target allocation of surveillance and rapid detection of HPAI in wild birds but also will allow the improvement of the cost-effectiveness of risk-based surveillance of HPAI introduction into domestic poultry farms in South Korea. Acknowledgements This study was supported by the 2019-20 cooperative research grant from the veterinary epidemiology Division in the Animal and Plant Quarantine Agency (APQA) in South Korea and the fellowship of the graduate student support program (GSSP) at UC Davis

Invasive Plants Distribution Modeling: A Tool for Tropical Biodiversity Conservation With Special Reference to Sri Lanka

The potential threats and habitat preferences of noxious plants in tropical countries are poorly known. Species distribution modeling (SDM) is a robust tool that can be used in conservation planning for early detection of invasion risks. However, the use of SDM for the strategic management of increasing risks of such plant invasions in Sri Lanka has not been undertaken due to several underlying reasons including the long-lasting data gap, technical, technological, and financial issues. In addition, the literature relevant to SDM applications in the country is substantially poor, scattered, and unpublished. Therefore, in this article, we explore SDM applications relevant to invasive plants in Sri Lanka with implications similar to other countries in the tropics. We examine the challenges and potentials for utilization of SDM technology in conservation planning in Sri Lanka and discuss data gap as a major obstacle. We also identify the potential SDM interventions relevant to invasive plants control and management in Sri Lanka and recommend conservation planners to prioritize them and apply for the strategic management of invasive plants in Sri Lanka. Finally, we suggest some recommendations to make an enabling environment in relevant institutions for the utilization of SDM technology in ecosystem management planning in Sri Lanka.

Species distribution models (SDM): applications, benefits and challenges in invasive species management.

Abstract The use of species distribution models (SDM) is of increasing popularity when studying biological invasions, e.g. to assess the impact of climate change on invasive species, to prioritize conservation measures, or to study invasive evolutionary biology. SDM correlate known occurrences of species with environmental variables and predict a species' potential distribution on other geographies over space and time. Today, SDM are widely used to produce invasion risk maps by delineating probable risk areas based on climatic suitability for a species. These maps can guide early detection and rapid response measures. While recent developments in modelling approaches and wider availability of environment datasets have helped to create better and more accurate SDM, in many cases the used models ignore the associated underlying ecological processes for e.g. dispersal and biotic interactions and thus provide only an incomplete picture of invasion risks. In this paper, we present a review of the most common applications of SDM in invasive species management and provide an overview of possible solutions to various challenges like uncertainty and transferability associated with them. Based on our review we conclude that ways towards more accurate model outputs include fitting models with existing ecological knowledge (hybrid models), address uncertainty and biotic interactions and link species dispersal traits with projections of species distributions. In the future, work is required on developing more hybrid approaches and models that addresses both local diffusion and long distance movement of alien species.

Modelling the spatial distribution of three Portunidae crabs in Haizhou Bay, China.

Crab species are economically and ecologically important in coastal ecosystems, and their spatial distributions are pivotal for conservation and fisheries management. This study was focused on modelling the spatial distributions of three Portunidae crabs (Charybdis bimaculata, Charybdis japonica, and Portunus trituberculatus) in Haizhou Bay, China. We applied three analytical approaches (Generalized additive model (GAM), random forest (RF), and artificial neural network (ANN)) to spring and fall bottom trawl survey data (2011, 2013–2016) to develop and compare species distribution models (SDMs). Model predictability was evaluated using cross-validation based on the observed species distribution. Results showed that sea bottom temperature (SBT), sea bottom salinity (SBS), and sediment type were the most important factors affecting crab distributions. The relative importance of candidate variables was not consistent among species, season, or model. In general, we found ANNs to have less stability than both RFs and GAMs. GAMs overall yielded the least complex response curve structure. C. japonica was more pronounced in southwestern portion of Haizhou Bay, and C. bimaculata tended to stay in offshore areas. P. trituberculatus was the least region-specific and exhibited substantial annual variations in abundance. The comparison of multiple SDMs was informative to understand species responses to environmental factors and predict species distributions. This study contributes to better understanding the environmental niches of crabs and demonstrates best practices for the application of SDMs for management and conservation planning.

Using species distribution modelling to determine opportunities for trophic rewilding under future scenarios of climate change.

Trophic rewilding, the (re)introduction of species to promote self-regulating biodiverse ecosystems, is a future-oriented approach to ecological restoration. In the twenty-first century and beyond, human-mediated climate change looms as a major threat to global biodiversity and ecosystem function. A critical aspect in planning trophic rewilding projects is the selection of suitable sites that match the needs of the focal species under both current and future climates. Species distribution models (SDMs) are currently the main tools to derive spatially explicit predictions of environmental suitability for species, but the extent of their adoption for trophic rewilding projects has been limited. Here, we provide an overview of applications of SDMs to trophic rewilding projects, outline methodological choices and issues, and provide a synthesis and outlook. We then predict the potential distribution of 17 large-bodied taxa proposed as trophic rewilding candidates and which represent different continents and habitats. We identified widespread climatic suitability for these species in the discussed (re)introduction regions under current climates. Climatic conditions generally remain suitable in the future, although some species will experience reduced suitability in parts of these regions. We conclude that climate change is not a major barrier to trophic rewilding as currently discussed in the literature.This article is part of the theme issue 'Trophic rewilding: consequences for ecosystems under global change'.

Aerial surveys and distribution models enable monitoring of fishing in Marine Protected Areas

Marine Protected Areas are rapidly becoming a central method for conservation of aquatic resources, but quantifying the success of these reserves in restricting fishing remains a challenge. Monitoring fishing has long been difficult - there are many types of fishers accessing resources in remote places from a diverse set of platforms (e.g., boat types). We used aerial surveys in conjunction with a novel application of species distribution modeling to develop a method for monitoring the change in fisher distributions following the implementation of MPAs. Aerial survey transects were conducted for 3.5 years before and after the implementation of 25 MPAs along the mainland southern California coast in 2012 and resulted in 13,558 vessel observations representing 19 different boat types. We compared actively fishing commercial and recreational vessels with non-fishing vessels to evaluate the use of MPA areas. There was a statistically significant decrease in proportion of vessels observed within MPAs from 17.5% before to 11.4% after MPA implementation, with MPA-implementation, fishing type, and the interaction all predicting the probability of a vessel being observed within MPA boundaries. Distribution models showed both an overall shift in distributions across all boat types and a decrease in predicted probability of habitat suitability of fishing within MPA boundaries after MPA implementation, although results differed among boat types. We illustrate the utility of distribution modeling for evaluating spatial patterns in human activities, providing a powerful tool for conservation biologists and demonstrate the importance of monitoring programs for establishing both baseline and response data needed for adaptive management of marine ecosystems.

Genetic trials improve the transfer of Douglas‐fir distribution models across continents

Climate change is likely to result in novel conditions with no analogy to current climate. Therefore, the application of species distribution models (SDMs) based on the correlation between observed species’ occurrence and their environment is questionable and calls for a better understanding of the traits that determine species occurrence. Here, we compared two intraspecific, trait‐based SDMs with occurrence‐based SDMs, all developed from European data, and analyzed their transferability to the native range of Douglas‐fir in North America.With data from 50 provenance trials of Douglas‐fir in central Europe multivariate universal response functions (URFs) were developed for two functional traits (dominant tree height and basal area) which are good indicators of growth and vitality under given environmental conditions. These trials included 290 North American provenances of Douglas‐fir. The URFs combine genetic effects i.e. the climate of provenance origin and environmental effects, i.e. the climate of planting locations into an integrated model to predict the respective functional trait from climate data. The URFs were applied as SDMs (URF‐SDMs) by converting growth performances into occurrence. For comparison, an ensemble occurrence‐based SDM was developed and block cross validated with the BIOMOD2 modeling platform utilizing the observed occurrence of Douglas‐fir in Europe. The two trait based SDMs and the occurrence‐based SDM, all calibrated with data from Europe, were applied to predict the known distribution of Douglas‐fir in its introduced and native range in Europe and North America.Both models performed well within their calibration range in Europe, but model transfer to its native range in North America was superior in case of the URF‐SDMs showing similar predictive power as SDMs developed in North America itself. The high transferability of the URF‐SDMs is a testimony of their applicability under novel climatic conditions highlighting the role of intraspecific trait variation for adaptation planning in climate change.

Using species distribution models at local scale to guide the search of poorly known species: Review, methodological issues and future directions

Among specific applications of species distribution models (SDMs), the use of SDMs probabilistic maps for guiding field surveys is increasingly applied. This approach is particularly used for poorly known and/or cryptic species in order to better assess their distribution. One of the most interesting aspects of these applications is that predictions could be clearly validated by real data, subsequently obtained in the field. Despite this important difference from other applications, to our knowledge, the efficiency of different algorithms, metrics for model evaluation and algorithm-specific settings have not yet been sufficiently investigated.This research performs a literature survey to investigate which species, study area characteristics, variables, techniques and settings were used or suggested by previous authors. We then applied the most common approaches to guide field surveys for a set of 70 vascular plants in an endemic-rich area of Sardinia (Italy) of approx. 9000 ha, the flora of which was deeply investigated during the last two years. Our main aims were: (1) to use pre-model records for predicting the potential occurrence of plant species with different sample size, detectability and habitat preference, (2) to apply results for guiding searches for new populations of poorly known species, (3) to calculate the model performance according to independent real presence/absence data (testAUC) and (4) to compare different modelling data input and settings on the testAUC basis.By emphasizing the importance of field verification, both the review and the worked example supported the reliability of SDMs for a wide range of species to understand where a species could potentially be present and therefore to optimise resources for the search of new species localities. This study may help understand and summarise the most applied methodological approaches and to highlight future directions for this practical application. Without underrating the importance of most common recommendations, practitioners are encouraged to test the ability of this SDMs’ application with their own data. Indeed, large gaps in biological groups (e.g. insects) and in regions covered by these kind of studies (e.g. many African and Asian territories) were found. Furthermore, eventual biases due to lack of data, experience or staff, have in this experimental case less irreparable consequences than others, such as conservation assessments based on future projections, which cannot be otherwise adjusted by explicit data from ground validation.

Surviving glaciations in the Mediterranean region: an alternative to the long-term refugia hypothesis

The simultaneous application of species distribution modelling (SDM) and study of genetic imprints left by range dynamics is appropriate when examining the biogeographical processes that have favoured the survival of plants through past climate changes. Nevertheless, such an approach is rarely performed on the scale of the entire Mediterranean and almost never concerns widespread thermophilous plants. Here, we examine the biogeographical responses of an important Mediterranean shrub, Myrtus communis (Myrtaceae), to severe Quaternary climate conditions. Our analysis combines SDM and phylogeography based on plastid/nuclear DNA sequences and AFLP data. Palaeoclimatic models using MaxEnt and levels of genetic diversity in M. communis are used to infer drastic changes in areas of climatic suitability during the last 130000 years, with a southward range contraction during the Last Glacial Maximum. Modelling of past areas of suitability for M. communis identifies a few relatively small long-term refugia, suggesting that it survived in temporary refugia during glacial periods. Myrtus communis is characterized by a higher genetic diversity and distinctiveness in the southern part of its range, where it was less impacted by glaciations. The structure of genetic diversity reveals stronger range expansions in the western part of the range, whereas migration processes remained much more restricted in the eastern Mediterranean.

Predicting distributions, habitat preferences and associated conservation implications for a genus of rare fishes, seahorses (Hippocampus spp.)

AbstractAimTo identify useful sources of species data and appropriate habitat variables for species distribution modelling on rare species, with seahorses as an example, deriving ecological knowledge and spatially explicit maps to advance global seahorse conservation.LocationThe shallow seas.MethodsWe applied a typical species distribution model (SDM), maximum entropy, to examine the utility of (1) two versions of habitat variables (habitat occurrences vs. proximity to habitats) and (2) three sources of species data: quality research‐grade (RG) data, quality‐unknown citizen science (CS) and museum‐collection (MC) data. We used the best combinations of species data and habitat variables to predict distributions and estimate species–habitat relations and threatened status for seahorse species.ResultsWe demonstrated that using “proximity to habitats” and integrating all species datasets (RG, CS and MC) derived models with the highest accuracies among all dataset variations. Based on this finding, we derived reliable models for 33 species. Our models suggested that only 0.4% of potential seahorse range was suitable to more than three species together; seahorse biogeographic epicentres were mainly in the Philippines; and proximity to sponges was an important habitat variable. We found that 12 “Data Deficient” species might be threatened based on our predictions according to IUCN criteria.Main conclusionsWe highlight that using proper habitat variables (e.g., proximity to habitats) is critical to determine distributions and key habitats for low‐mobility animals; collating and integrating quality‐unknown occurrences (e.g., CS and MC) with quality research data are meaningful for building SDMs for rare species. We encourage the application of SDMs to estimate area of occupancy for rare organisms to facilitate their conservation status assessment.

Habitat modeling of the common pheasant Phasianus colchicus (Galliformes: Phasianidae) in a highly modified landscape: application of species distribution models in the study of a poorly documented bird in Iran

The common pheasant is listed as a nationally protected species in Iran because it faces many threats such as habitat destruction, pollution from pesticides and overhunting. The species’ habitat selection remains unknown in Iran; consequently, conservation planning for the species is hampered by this lack of information. In this study we used predictor variables including topographic, anthropogenic, land cover and climate and 122 occurrence points to model the distribution of common pheasant (subspecies P. c. talischensis), in Gilan province, Iran. The results showed that distance to agriculture and orchards as well as distance to plantation forests were the most important variables in predicting species distribution. Furthermore, we found that the total potential suitable habitat for the species in Gilan province is 315,990 hectares. We observed a shift of the species to higher altitudes in the province. We recommend raising awareness about the presence of the species especially among private landowners, creating a network of protected areas on private land, and changing land-use policies at the provincial level as essential measures for the conservation of the species in Gilan province. Our results can be applied to management and conservation of the species in other modified or rapidly changing landscapes.

Application of species distribution models in the prediction of marine potential habitat: A review

The relationship between the distribution of halobios and environmental variables has been a focus of present research in marine ecology. Species distribution models (SDMs) have been widely employed to predict the distribution patterns and potential habitat suitability assessments of marine species and provide an efficient approach for marine biodiversity conservation, invasive species prevention, and fishery management. SDMs mainly include habitat suitability index models, mechanism models, and statistical models. In this paper, the theoretical basis of SDMs was firstly concluded and summarized. Next, the exploitation and case-studies of SDMs, especially the statistical models, for predicting potential distribution of marine species were reviewed. Then, comparisons of various methods for variable selection and model validation were made. Conclusions could be drawn that Akaike information criterion showed excellent performance when it came to variable selection, while Kappa coefficient and Area Under receiver operating character Curve (AUC) were widely used in relation to model validation. Finally, problems and prospects of SDMs were discussed. With the development of research on physiological characteristics, using mechanism models to predict potential habitats of halobios would become a trend.

Climate Analyses to Assess Risks from Invasive Forest Insects: Simple Matching to Advanced Models

The number of invasive alien insects that adversely affect trees and forests continues to increase as do associated ecological, economic, and sociological impacts. Prevention strategies remain the most cost-effective approach to address the issue, but risk management decisions, particularly those affecting international trade, must be supported by scientifically credible pest risk assessments. Pest risk assessments typically include an evaluation of the suitability of the climate for pest establishment within an area of concern. A number of species distribution models have been developed to support those efforts, and these models vary in complexity from simple climate matching to mechanistic models. This review discusses the rationale for species distribution models and describes some common and influential approaches. Species distribution models that use distributional records and environmental covariates are routinely applied when ecological information about a species of concern is limited, an all-too common situation for pest risk assessors. However, fundamental assumptions of the models may not always hold. A structured literature review suggests that many common species distribution models are not regularly applied to alien insects that may threaten trees and forests. For ten high-impact alien insect species that are invading North America, MaxEnt and CLIMEX were applied more often than other modeling approaches. Some impediments to model development and publication exist. More applications of species distribution models to forest insects are needed in the peer-reviewed literature to ensure the credibility of pest risk maps for regulatory decision making, to deepen understanding of the factors that dictate species’ distributions, and to better characterize uncertainties associated with these models.

Transferability and scalability of species distribution models: a test with sedentary marine invertebrates

We found the predictive accuracy of species distribution models (SDMs) for sedentary marine invertebrates to be dependent on the methodology of their application. We explored three applications of SDMs: first a model tested at a scale smaller than at which it was trained (downscaled), second a model tested at scale larger than its training scale (upscaled), and third a model tested at the same scale but outside the extent for which it was trained (transferred). The accuracies of these models were compared with the “reference” models that were trained and tested at the same scale and extent. We found that downscaled SDMs had higher predictive accuracy than reference SDMs. Transferred and upscaled models had lower predictive accuracy than their reference counterparts but still performed better than random, making them potentially acceptable alternatives where information is lacking for imminent decisions or in cost-restricted scenarios. Our results provide insights into the techniques available for researchers and managers developing SDMs at varying scales, with different species, and with different levels of initial information.

ENVIREM: an expanded set of bioclimatic and topographic variables increases flexibility and improves performance of ecological niche modeling

Species distribution modeling is a valuable tool with many applications across ecology and evolutionary biology. The selection of biologically meaningful environmental variables that determine relative habitat suitability is a crucial aspect of the modeling pipeline. The 19 bioclimatic variables from WorldClim are frequently employed, primarily because they are easily accessible and available globally for past, present and future climate scenarios. Yet, the availability of relatively few other comparable environmental datasets potentially limits our ability to select appropriate variables that will most successfully characterize a species’ distribution. We identified a set of 16 climatic and two topographic variables in the literature, which we call theENVIREMdataset, many of which are likely to have direct relevance to ecological or physiological processes determining species distributions. We generated this set of variables at the same resolutions as WorldClim, for the present, mid‐Holocene, and Last Glacial Maximum (LGM). For 20 North American vertebrate species, we then assessed whether including theENVIREMvariables led to improved species distribution models compared to models using only the existing WorldClim variables. We found that including theENVIREMdataset in the pool of variables to select from led to substantial improvements in niche modeling performance in 13 out of 20 species. We also show that, when comparing models constructed with different environmental variables, differences in projected distributions were often greater in the LGM than in the present. These variables are worth consideration in species distribution modeling applications, especially as many of the variables have direct links to processes important for species ecology. We provide these variables for download at multiple resolutions and for several time periods at envirem.github.io. Furthermore, we have written the ‘envirem’ R package to facilitate the generation of these variables from other input datasets.

Predicting impacts of climate change on forest tree species of Bangladesh: evidence from threatened Dysoxylum binectariferum (Roxb.) Hook.f. ex Bedd. (Meliaceae)

Abstract: The impact of climate change on ecosystems, especially at the species level, can be currently observed in many parts of the world. Species distribution models (SDMs) are widely used to predict the likely changes in the distribution of species in future climate change scenarios. The aim of the present study is to predict the effect of climate change on a valuable threatened tree species Dysoxylum binectariferum in the northeastern part of Bangladesh using the maximum entropy (MaxEnt) model on species’ occurrence data. The future distribution of D. binectariferum was predicted under two scenarios from the IPCC 5th assessment (RCP 4.5, and RCP 8.5) in 2050 and 2070. Model results showed that approximately 32% (2177 km2) of the studied area is currently suitable for this species to grow. However, future predictions obtained by the model projected a complete loss of suitable habitat for D. binectariferum in the studied area by both 2050 and 2070. Therefore, urgent measures are required for the conservation of D. binectariferum in northeastern Bangladesh. The application of species distribution models to simple inventory data (such as the occurrence of the species) may provide policymakers and conservationists with a useful tool for the prediction of future distribution (at both local and regional scales) of poorly known species with high preservation concerns.

Application of species distribution model using the data of the national survey on the natural environment for primary environmental impact consideration―a case study of the Tokyo salamander Hynobius tokyoensis―

Application of species distribution model using the data of the national survey on the natural environment for primary environmental impact consideration―a case study of the Tokyo salamander <i>Hynobius tokyoensis</i>―

Novel methods to select environmental variables in MaxEnt: A case study using invasive crayfish

The popularity of MaxEnt in species distribution modeling has been driven by several factors including its high degree of accuracy, and flexibility to tailor efforts to species-specific situations. Although many recent studies have identified the importance of adjusting mathematical transformation (feature class) and regularization of coefficient values, collectively known as tuning, few studies have addressed the need to customize the variables used in species distribution modeling, and use unselected variable sets. This study presents two novel methods to select for environmental variables in MaxEnt. The first involves selecting from a priori determined environmental variable sets (pre-selected based on ecological or biological knowledge), and the second utilizes a reiterative process of model formation and stepwise removal of least contributing variables. Both methods were tested on eight known species of invasive crayfish, with results reinforcing the need for species-specific environmental variable sets. While the reiterative process generally performs better than the a priori selected variables, selection of method can be based on information availability. These techniques appear to outperform the current practice of utilizing unselected variable sets and is especially important considering the increasing application of species distribution modeling (across spatial and temporal barriers) in conservation and management efforts whereby inaccurate predictions might have adverse effects.

Application of Species Distribution Modeling for Avian Influenza surveillance in the United States considering the North America Migratory Flyways.

Highly Pathogenic Avian Influenza (HPAI) has recently (2014–2015) re-emerged in the United States (US) causing the largest outbreak in US history with 232 outbreaks and an estimated economic impact of $950 million. This study proposes to use suitability maps for Low Pathogenic Avian Influenza (LPAI) to identify areas at high risk for HPAI outbreaks. LPAI suitability maps were based on wild bird demographics, LPAI surveillance, and poultry density in combination with environmental, climatic, and socio-economic risk factors. Species distribution modeling was used to produce high-resolution (cell size: 500m x 500m) maps for Avian Influenza (AI) suitability in each of the four North American migratory flyways (NAMF). Results reveal that AI suitability is heterogeneously distributed throughout the US with higher suitability in specific zones of the Midwest and coastal areas. The resultant suitability maps adequately predicted most of the HPAI outbreak areas during the 2014–2015 epidemic in the US (i.e. 89% of HPAI outbreaks were located in areas identified as highly suitable for LPAI). Results are potentially useful for poultry producers and stakeholders in designing risk-based surveillance, outreach and intervention strategies to better prevent and control future HPAI outbreaks in the US.

Application of species distribution models for protected areas threatened by invasive plants

Local species distribution models (SDMs) were constructed for three of the most harmful invasive alien plant species (Fallopia spp., Solidago spp. and Heracleum mantegazzianum) in the Kokořínsko Protected Landscape Area (Czech Republic), using Natura 2000 habitat types and other environmental conditions as predictors for the SDMs. Presence or absence data (recorded by field mapping) was entered into the SDMs and used to predict the potential distribution of particular species. Here, we critically evaluate the accuracy of the models and assess their applicability for natural resource protection. Variables such as habitat and soil type tend to dictate the current distribution of Solidago spp., while distance from roads and water corridors and elevation are important for Fallopia spp. distribution. For Solidago spp., ‘generalised boosted models’ and ‘generalised additive models’ were considered the most suitable algorithms. For Fallopia spp., however, the predictive power of the models tended to be weak, while the number of localities was too low for SDMs in the case of H. mantegazzianum. The number of initial localities containing invasive alien species was an important factor for making significant predictions of potential distribution. In general, the predictive power of the models was too low when using less than 10 localities; for good predictive power at the local scale, we suggest that at least 100 localities/100km2 are used.