Application Of Species Distribution Models Research Articles

Improved prediction of Canada lynx distribution through regional model transferability and data efficiency.

The application of species distribution models (SDMs) to areas outside of where a model was created allows informed decisions across large spatial scales, yet transferability remains a challenge in ecological modeling. We examined how regional variation in animal‐environment relationships influenced model transferability for Canada lynx (Lynx canadensis), with an additional conservation aim of modeling lynx habitat across the northwestern United States. Simultaneously, we explored the effect of sample size from GPS data on SDM model performance and transferability. We used data from three geographically distinct Canada lynx populations in Washington (n = 17 individuals), Montana (n = 66), and Wyoming (n = 10) from 1996 to 2015. We assessed regional variation in lynx‐environment relationships between these three populations using principal components analysis (PCA). We used ensemble modeling to develop SDMs for each population and all populations combined and assessed model prediction and transferability for each model scenario using withheld data and an extensive independent dataset (n = 650). Finally, we examined GPS data efficiency by testing models created with sample sizes of 5%–100% of the original datasets. PCA results indicated some differences in environmental characteristics between populations; models created from individual populations showed differential transferability based on the populations' similarity in PCA space. Despite population differences, a single model created from all populations performed as well, or better, than each individual population. Model performance was mostly insensitive to GPS sample size, with a plateau in predictive ability reached at ~30% of the total GPS dataset when initial sample size was large. Based on these results, we generated well‐validated spatial predictions of Canada lynx distribution across a large portion of the species' southern range, with precipitation and temperature the primary environmental predictors in the model. We also demonstrated substantial redundancy in our large GPS dataset, with predictive performance insensitive to sample sizes above 30% of the original.

A meaningful application of species distribution models and functional traits to understand invasion dynamics

A recommendation of: Manuele Bazzichetto, François Massol, Marta Carboni, Jonathan Lenoir, Jonas Johan Lembrechts, Rémi Joly, David Renault Once upon a time in the far south: Influence of local drivers and functional traits on plant invasion in the harsh sub-Antarctic islands https://doi.org/10.1101/2020.07.19.210880

The application of species distribution modeling in wetland restoration: A case study in the Songnen Plain, Northeast China

Species distribution models (SDMs) are an effective tool in ecological restoration, but the application of SDMs in wetland restoration is rare owing to limited survey data. Based on field survey of the iconic, widespread wetland species Phragmites australis (Poaceae), we built maximum entropy (MaxEnt) models to study the current and future delimitation of wetlands in the Songnen Plain in Northeast China. In building the model, we evaluated the selection of environmental predictors and data resolutions. The evaluation showed the addition of soil and topo-hydrological (e.g. slope, Topographic Wetness Index, water table depth) variables significantly improved model performance in comparison with climate-only models, and the best model performance was under spatial resolution of 250 m. This study found that wetlands of the Songnen Plain are facing threats from climate change and anthropogenic expansion. The selected models predicted approximately a 15% net loss in suitable habitats, as well as expansion trends towards more humid and higher altitude areas in the future. Based on the modeling results, we delimited 1.51 × 103 km2 and 1.48 × 103 km2 of potential wetland restoration areas under RCP 4.5 and RCP 8.5 scenarios, respectively. Our study highlighted the importance of optimizing the selection of variables and spatial resolution in the application of SDMs in wetlands, and illuminated priority areas of the Songnen Plain for consideration in future wetland restoration efforts.

Applying species distribution models in public health research by predicting snakebite risk using venomous snakes\u2019 habitat suitability as an indicating factor

Snakebite envenoming is an important public health problem in Iran, despite its risk not being quantified. This study aims to use venomous snakes’ habitat suitability as an indicator of snakebite risk, to identify high-priority areas for snakebite management across the country. Thus, an ensemble approach using five distribution modelling methods: Generalized Boosted Models, Generalized Additive Models, Maximum Entropy Modelling, Generalized Linear Models, and Random Forest was applied to produce a spatial snakebite risk model for Iran. To achieve this, four venomous snakes’ habitat suitability (Macrovipera lebetinus, Echis carinatus, Pseudocerastes persicus and Naja oxiana) were modelled and then multiplied. These medically important snakes are responsible for the most snakebite incidents in Iran. Multiplying habitat suitability models of the four snakes showed that the northeast of Iran (west of Khorasan-e-Razavi province) has the highest snakebite risk in the country. In addition, villages that were at risk of envenoming from the four snakes were identified. Results revealed that 51,112 villages are at risk of envenoming from M. lebetinus, 30,339 from E. carinatus, 51,657 from P. persicus and 12,124 from N. oxiana. Precipitation seasonality was identified as the most important variable influencing distribution of the P. persicus, E. carinatus and M. lebetinus in Iran. Precipitation of the driest quarter was the most important predictor of suitable habitats of the N. oxiana. Since climatic variables play an important role in shaping the distribution of the four venomous snakes in Iran, thus their distribution may alter with changing climate. This paper demonstrates application of species distribution modelling in public health research and identified potential snakebite risk areas in Iran by using venomous snakes’ habitat suitability models as an indicating factor. Results of this study can be used in snakebite and human–snake conflict management in Iran. We recommend increasing public awareness of snakebite envenoming and education of local people in areas which identified with the highest snakebite risk.

Species distribution models for the eastern blacklegged tick, Ixodes scapularis, and the Lyme disease pathogen, Borrelia burgdorferi, in Ontario, Canada.

The blacklegged tick, Ixodes scapularis, is established in several regions of Ontario, Canada, and continues to spread into new geographic areas across the province at a rapid rate. This poses a significant public health risk since I. scapularis transmits the Lyme disease-causing bacterium, Borrelia burgdorferi, and other pathogens of potential public health concern. The objective of this study was to develop species distribution models for I. scapularis and B. burgdorferi to predict and compare the potential distributions of the tick vector and the Lyme disease pathogen as well as the ecological factors most important for species establishment. Ticks were collected via tick dragging at 120 sites across southern, central, and eastern Ontario between 2015 and 2018 and tested for tick-borne pathogens. A maximum entropy (Maxent) approach was used to model the potential distributions of I. scapularis and B. burgdorferi. Two independent datasets derived from tick dragging at 25 new sites in 2019 and ticks submitted by the public to local health units between 2015 and 2017 were used to validate the predictive accuracy of the models. The model for I. scapularis showed high suitability for blacklegged ticks in eastern Ontario and some regions along the shorelines of the Great Lakes, and moderate suitability near Algonquin Provincial Park and the Georgian Bay with good predictive accuracy (tick dragging 2019: AUC = 0.898; ticks from public: AUC = 0.727). The model for B. burgdorferi showed a similar predicted distribution but was more constrained to eastern Ontario, particularly between Ottawa and Kingston, and along Lake Ontario, with similarly good predictive accuracy (tick dragging 2019: AUC = 0.958; ticks from public: AUC = 0.863. The ecological variables most important for predicting the distributions of I. scapularis and B. burgdorferi included elevation, distance to deciduous and coniferous forest, proportions of agricultural land, water, and infrastructure, mean summer/spring temperature, and cumulative annual degree days above 0°C. Our study presents a novel application of species distribution modelling for I. scapularis and B. burgdorferi in Ontario, Canada, and provides an up to date projection of their potential distributions for public health knowledge users.

Species distribution models have limited spatial transferability for invasive species.

The reliability of transferring species distribution models (SDMs) to new ranges and future climates has been widely debated. Biological invasions offer the unique opportunity to evaluate model transferability, as distribution data between species' native and introduced ranges are geographically independent of each other. Here, we performed the first global quantitative synthesis of the spatial transferability of SDMs for 235 invasive species and assessed the association of model transferability with the focal invader, model choice and parameterisation. We found that SDMs had limited spatial transferability overall. However, model transferability was higher for terrestrial endotherms, species introduced from or to the Southern Hemisphere, and species introduced more recently. Model transferability was also positively associated with the number of presences for model calibration and evaluation, respectively, but negatively with the number of predictors. These findings highlight the importance of considering the characteristics of the focal invader, environment and modelling in the application and assessment of SDMs.

A standard protocol for reporting species distribution models

Species distribution models (SDMs) constitute the most common class of models across ecology, evolution and conservation. The advent of ready‐to‐use software packages and increasing availability of digital geoinformation have considerably assisted the application of SDMs in the past decade, greatly enabling their broader use for informing conservation and management, and for quantifying impacts from global change. However, models must be fit for purpose, with all important aspects of their development and applications properly considered. Despite the widespread use of SDMs, standardisation and documentation of modelling protocols remain limited, which makes it hard to assess whether development steps are appropriate for end use. To address these issues, we propose a standard protocol for reporting SDMs, with an emphasis on describing how a study's objective is achieved through a series of modeling decisions. We call this the ODMAP (Overview, Data, Model, Assessment and Prediction) protocol, as its components reflect the main steps involved in building SDMs and other empirically‐based biodiversity models. The ODMAP protocol serves two main purposes. First, it provides a checklist for authors, detailing key steps for model building and analyses, and thus represents a quick guide and generic workflow for modern SDMs. Second, it introduces a structured format for documenting and communicating the models, ensuring transparency and reproducibility, facilitating peer review and expert evaluation of model quality, as well as meta‐analyses. We detail all elements of ODMAP, and explain how it can be used for different model objectives and applications, and how it complements efforts to store associated metadata and define modelling standards. We illustrate its utility by revisiting nine previously published case studies, and provide an interactive web‐based application to facilitate its use. We plan to advance ODMAP by encouraging its further refinement and adoption by the scientific community.

Testing the utility of species distribution modelling using Random Forests for a species in decline

AbstractHabitat suitability estimates derived from species distribution models (SDMs) are increasingly used to guide management of threatened species. Poorly estimating species’ ranges can lead to underestimation of threatened status, undervaluing of remaining habitat and misdirection of conservation funding. We aimed to evaluate the utility of a SDM, similar to the models used to inform government regulation of habitat in our study region, in estimating the contemporary distribution of a threatened and declining species. We developed a presence‐only SDM for the endangered New Holland Mouse (Pseudomys novaehollandiae) across Victoria, Australia. We conducted extensive camera trap surveys across model‐predicted and expert‐selected areas to generate an independent data set for use in evaluating the model, determining confidence in absence data from non‐detection sites with occupancy and detectability modelling. We assessed the predictive capacity of the model at thresholds based on (1) sum of sensitivity and specificity (SSS), and (2) the lowest presence threshold (LPT; i.e. the lowest non‐zero model‐predicted habitat suitability value at which we detected the species). We detected P. novaehollandiae at 40 of 472 surveyed sites, with strong support for the species’ probable absence from non‐detection sites. Based on our post hoc optimised SSS threshold of the SDM, 25% of our detection sites were falsely predicted as non‐suitable habitat and 75% of sites predicted as suitable habitat did not contain the species at the time of our survey. One occupied site had a model‐predicted suitability value of zero, and at the LPT, 88% of sites predicted as suitable habitat did not contain the species at the time of our survey. Our findings demonstrate that application of generic SDMs in both regulatory and investment contexts should be tempered by considering their limitations and currency. Further, we recommend engaging species experts in the extrapolation and application of SDM outputs.

A multi-scale, integrative modeling framework for setting conservation priorities at the catchment scale for the Freshwater Pearl Mussel Margaritifera margaritifera

The identification and prioritization of sites for conservation actions to protect biodiversity in lotic systems is crucial when economic resources or available areas are limited. Challenges include the incorporation of multi-scale interactions, and the application of species distribution models (SDMs) to rare organism with multiple life stages. To support the planning of conservation actions for the highly endangered Freshwater Pearl Mussel Margaritifera margaritifera (FPM), this paper aims at developing an ecohydrological modeling cascade including a hydrological model (SWAT) and a hydraulic model (HEC-RAS). Building on hydrology and hydraulics, Random Forest models for potential risk to juveniles due to sand accumulation, SDMs for adults habitat niche, and a landscape connectivity assessment of dispersal potential were developed. The feasibility of such models integration was tested in the Aist catchment (630 km2) in Austria. The potential FPM habitat and the sand accumulation risk for the whole catchment were predicted with good accuracy. Results show that while the potentially suitable habitats for adults FPM cover 34% of the river network, only few habitat patches can maximize the dispersal potential (4% of the river network) and even less are showing limited impact of accumulations (3.5% of river network). No habitat patch that meets all the three criteria is available, suggesting approaches that target the patch-specific critical life stage-factors are promising for conservation.

Testing whether ensemble modelling is advantageous for maximising predictive performance of species distribution models

Predictive performance is important to many applications of species distribution models (SDMs). The SDM ‘ensemble’ approach, which combines predictions across different modelling methods, is believed to improve predictive performance, and is used in many recent SDM studies. Here, we aim to compare the predictive performance of ensemble species distribution models to that of individual models, using a large presence–absence dataset of eucalypt tree species. To test model performance, we divided our dataset into calibration and evaluation folds using two spatial blocking strategies (checkerboard‐pattern and latitudinal slicing). We calibrated and cross‐validated all models within the calibration folds, using both repeated random division of data (a common approach) and spatial blocking. Ensembles were built using the software package ‘biomod2’, with standard (‘untuned’) settings. Boosted regression tree (BRT) models were also fitted to the same data, tuned according to published procedures. We then used evaluation folds to compare ensembles against both their component untuned individual models, and against the BRTs. We used area under the receiver‐operating characteristic curve (AUC) and log‐likelihood for assessing model performance. In all our tests, ensemble models performed well, but not consistently better than their component untuned individual models or tuned BRTs across all tests. Moreover, choosing untuned individual models with best cross‐validation performance also yielded good external performance, with blocked cross‐validation proving better suited for this choice, in this study, than repeated random cross‐validation. The latitudinal slice test was only possible for four species; this showed some individual models, and particularly the tuned one, performing better than ensembles. This study shows no particular benefit to using ensembles over individual tuned models. It also suggests that further robust testing of performance is required for situations where models are used to predict to distant places or environments.

The role of spatial units in modelling freshwater fish distributions: Comparing a subcatchment and river network approach using MaxEnt

Species Distribution Models (SDM) are frequently used in ecological research, but the effects of model extent and granularity on model outcomes are rarely addressed. In freshwater SDMs, two different approaches are commonly used to define the granularity of the models, i.e. to subdivide entire river systems into appropriate spatial modelling units: river reaches on the actual river network or subcatchments. We built maximum entropy (MaxEnt) SDMs for barbel (Barbus barbus) and grayling (Thymallus thymallus) in the River Ruhr catchment in Germany with identical environmental predictor data and analysed the difference between these two approaches. All models performed well (AUC > 0.9) and geographic predictors (e.g. distance to the river source) dominated over hydromorphological predictors in explaining species distributions. There was high agreement between the two model setups in river parts of low and high habitat suitability but considerably lower agreement in river parts of medium habitat suitability. In these medium suitable river parts, model results were spatially very heterogeneous and alternated at fine spatial scales especially in models based on river reaches. Increasing model regularization, a setting to control overfitting, had a smoothing effect on the environmental variables in the river reach models similar to the coarser subcatchment granularity. A restriction of the spatial extent led to a shift in predictor contributions to the model and an increase of the importance of hydromorphologic predictors by ca. 45 %. Restricting the model extend to the natural core distributional area of a given fish species might therefore be considered beneficial for the application of SDMs in a management context. By decreasing the weight of fixed geographic predictors in the model, predictors relating to hydromorphological river structures (i.e. which are accessible for restoration projects) gain importance. We conclude that SDM setups based on river reaches and subcatchments can both give valuable complementary information about the distribution of species. The high-resolution model based on river reaches might better discover individual local habitat features, whereas the subcatchment model might better account for the minimum spatial requirements of a fish population.

Larus relictus HABITAT HIERARCHICAL EVALUATION BASED ON A DATA DRIVEN APPROACH

The application of Species Distribution Models (SDM) in the management of species habitat and environmental impact changes has been widely used by scientists and ecologists globally, however this study proposes a novel analytical technique involving the combination of a statistical ranking algorithm and Remote sensing GIS data (habitat and threat) to analyze the suitability hierarchy of the habitat of a signature water-bird (Larus relictus). The results indicate that about 47.63% of the study region was suitable to accommodate L. Relictus during their breeding/resting season, while 27.08% proved to be highly unsuitable for this species. Based on the spatial distribution of the statistical data already incorporated into the GIS environment, it was observed that regions surrounding the Bojiang Lake (especially polygons 38 and 42) dominated by low vegetation and increased amount of moisture proved to be the best region for this species. On the contrary, polygon 23, located in the southern part of the study region proved to be the worst region for this species being dominated by significantly high amount of threat factors having the highest mean normalized hierarchical value and ranking 60th out of 61 in terms of standard deviation. To distinctively capture different suitability level of the study region, we could say that the application of this technique is quite effective and beneficial. This is because, compared to other decision making tools, this technique which solely relies on remote sensing and vector data gives decision makers an option of weight application if so desired.

Small-scale distribution modeling of benthic species in a protected natural hard ground area in the German North Sea (Helgoländer Steingrund)

Natural stony and coarse-grained habitats entail important ecological features for the marine environment. Due to the complexity of their bottom characteristics, they host a high biodiversity compared to surrounding soft bottom areas. The German nature conservation area “Helgolander Steingrund” (HSG; 54°14.00 N and 8°03.00 W) is subject to regular monitoring but lacks information on the spatial distribution of benthic species. Within this study, a new approach using species distribution models (SDM) was tested to fill these gaps of knowledge. Newly recorded environmental data (depth, sediments, current velocities) in the HSG and information on the presence and absences of nine benthic species (Echinus esculentus, Metridium senile, Cancer pagurus, Phymatolithon spp., Axinella polypoides, Homarus gammarus, Flustra foliacea, Alcyonidium diaphanum, Alcyonium digitatum), collected using video analysis of drop camera records, was used to perform SDMs. The models revealed good evaluation measures (true skill statistic > 0.7; area under the receiver operation characteristic curve > 0.90), implying that the model showed good predictive performance for the potential distribution of the tested species. The outcome of this study is a clear recommendation on SDM application in further environmental monitoring programs on the HSG and other protected hard ground areas.

Incorporating biotic relationships improves species distribution models: Modeling the temporal influence of competition in conspecific nesting birds

AimComplex, biotic interactions are notably excluded from species distribution models (SDMs) as they are often difficult to quantify and accommodate in a traditional modeling framework, especially those with a temporal component. The territorial nature of breeding Cactus wren is well-documented and typically involves nest usurping (i.e., destruction) of conspecifics. Due to their similar nesting ecology, breeding Verdin are frequently the target of such behavior and are often forced to move or abandon nests. Using the Verdin/Cactus wren system as a case study, our goal was to evaluate the performance of SDMs that include only environmental predictors with SDMs that also include biotic relationships as predictors. LocationEast Foundation’s San Antonio Viejo Ranch in south Texas. MethodsWe built SDMs (MaxEnt, Boosted Regression Tree [BRT], and Random Forest [RF]) to project Verdin distribution during the early (April through mid-May), peak (mid-May through mid-June), and late (mid-June through mid-July) breeding periods using occurrence data collected during the 2015 and 2016 breeding seasons. We ran parallel analyses using relevant environmental features alone as predictors and then environmental features with observed Cactus wren density. ResultsRandom Forest (RF) produced the highest predictive performance SDMs for all three breeding periods (AUC = 0.81-0.99; TSS = 0.23-0.73). All models improved in predictive power (Δ AUC = 0.01-0.10) and model sensitivity (Δ TSS = 0.09-0.66) with the inclusion of Cactus wren density as a predictor of Verdin presence. Main conclusionsOur results indicate that SDM performance is improved by the inclusion of biotic relationships as predictors. Incorporating biotic interactions, as well as their temporal trends, is essential in efforts to monitor or conserve bird species with similar nesting ecologies. Further, modeling algorithms that can accommodate complex, non-linear relationships (e.g., Random Forest) should be preferred in SDM development and application.

Two alternative evaluation metrics to replace the true skill statistic in the assessment of species distribution models

Model evaluation metrics play a critical role in the selection of adequate species distribution models for conservation and for any application of species distribution modelling (SDM) in general. The responses of these metrics to modelling conditions, however, are rarely taken into account. This leads to inadequate model selection, downstream analyses and uniformed decisions. To aid modellers in critically assessing modelling conditions when choosing and interpreting model evaluation metrics, we analysed the responses of the True Skill Statistic (TSS) under a variety of presence-background modelling conditions using purely theoretical scenarios. We then compared these responses with those of two evaluation metrics commonly applied in the field of meteorology which have potential for use in SDM: the Odds Ratio Skill Score (ORSS) and the Symmetric Extremal Dependence Index (SEDI). We demonstrate that (1) large cell number totals in the confusion matrix, which is strongly biased towards ‘true’ absences in presence-background SDM and (2) low prevalence both compromise model evaluation with TSS. This is since (1) TSS fails to differentiate useful from random models at extreme prevalence levels if the confusion matrix cell number total exceeds ~30,000 cells and (2) TSS converges to hit rate (sensitivity) when prevalence is lower than ~2.5%. We conclude that SEDI is optimal for most presence-background SDM initiatives. Further, ORSS may provide a better alternative if absence data are available or if equal error weighting is strictly required.

Dealing with physical barriers in bottlenose dolphin (Tursiops truncatus) distribution

Worldwide, cetacean species have started to be protected, but they are still very vulnerable to accidental damage from an expanding range of human activities at sea. To properly manage these potential threats we need a detailed understanding of the seasonal distributions of these highly mobile populations. To achieve this goal, a growing effort has been underway to develop species distribution models (SDMs) that correctly describe and predict preferred species areas. However, accuracy is not always easy to achieve when physical barriers, such as islands, are present. Indeed, SDMs assume, if only implicitly, that the spatial effect is stationary, and that correlation is only dependent on the distance between observations and not on the direction or a spatial coordinates. The application of stationary SDMs in these cases could lead to incorrect predictions and, consequently, to uninformed decision making. In this study, we identify vulnerable habitats for the bottlenose dolphin in the Archipelago de La Maddalena, Northern Sardinia (Italy) using Bayesian hierarchical SDMs that account for the physical barriers issue and provide a full specification of the associated uncertainty. The approach we propose constitutes a major step forward in the understanding of cetacean species in many ecosystems where physical, geographical and topographical barriers are present.

Application of Species Distribution Model for Predicting Areas at Risk of Highly Pathogenic Avian Influenza in the Republic of Korea

Application of Species Distribution Model for Predicting Areas at Risk of Highly Pathogenic Avian Influenza in the Republic of Korea

Correction to ‘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'.

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.