Pest species distribution modelling: origins and lessons from history

Establishing the distribution of Carpophilus truncatus in Australia using an integrative approach for an emerging global pest

The nitidulid beetle Carpophilus truncatus is rapidly becoming a major pest of nut crops around the world. This insect first infested Australian almonds in 2013 and has since escalated to be the preeminent insect pest for the industry. Data pertaining to C. truncatus distribution are scant, but without awareness of its origin, distribution, and ecological factors that influence distribution, efforts to understand and manage the insect as a pest are stymied. Here, we employ an integrative approach to gain a multifaceted understanding of the distribution of C. truncatus in Australia. Methods employed were (1) reviewing historical records in insect collections to establish the presence of C. truncatus prior to commercial almond horticulture, (2) field trapping of insects to establish presence in regions of interest, (3) laboratory trials to determine the thermal limits of the organism, and (4) correlative species distribution modelling to describe its current distribution. We find that C. truncatus is more widespread across Australia than was previously known, with historical records preceding commercial almond production in Australia by a century. The methods developed in this study can be applied elsewhere in the world where C. truncatus is an emerging pest, or to novel pest species as they arise with increasing frequency in a globalised and warming world.

Which SDM Model, CLIMEX vs. MaxEnt, Best Forecasts Aeolesthes sarta Distribution at a Global Scale under Climate Change Scenarios?

A precise evaluation of the risk of establishing insect pests is essential for national plant protection organizations. This accuracy is crucial in negotiating international trade agreements for forestry-related commodities, which have the potential to carry pests and lead to unintended introductions in the importing countries. In our study, we employed both mechanistic and correlative niche models to assess and map the global patterns of potential establishment for Aeolesthes sarta under current and future climates. This insect is a significant pest affecting tree species of the genus Populus, Salix, Acer, Malus, Juglans, and other hardwood trees. Notably, it is also categorized as a quarantine pest in countries where it is not currently present. The mechanistic model, CLIMEX, was calibrated using species-specific physiological tolerance thresholds, providing a detailed understanding of the environmental factors influencing the species. In contrast, the correlative model, maximum entropy (MaxEnt), utilized species occurrences and spatial climatic data, offering insights into the species' distribution based on observed data and environmental conditions. The projected potential distribution from CLIMEX and MaxEnt models aligns well with the currently known distribution of A. sarta. CLIMEX predicts a broader global distribution than MaxEnt, indicating that most central and southern hemispheres are suitable for its distribution, excluding the extreme northern hemisphere, central African countries, and the northern part of Australia. Both models accurately predict the known distribution of A. sarta in the Asian continent, and their projections suggest a slight overall increase in the global distribution range of A. sarta with future changes in climate temperature, majorly concentrating in the central and northern hemispheres. Furthermore, the models anticipate suitable conditions in Europe and North America, where A. sarta currently does not occur but where its preferred host species, Populus alba, is present. The main environmental variables associated with the distribution of A. sarta at a global level were the average annual temperature and precipitation rate. The predictive models developed in this study offer insights into the global risk of A. sarta establishment and can be valuable for monitoring potential pest introductions in different countries. Additionally, policymakers and trade negotiators can utilize these models to make science-based decisions regarding pest management and international trade agreements.

Evidence that recent climatic changes have expanded the potential geographical range of the Mediterranean fruit fly

The species distributions migration poleward and into higher altitudes in a warming climate is especially concerning for economically important insect pest species, as their introduction can potentially occur in places previously considered unsuitable for year-round survival. We explore the expansion of the climatically suitable areas for a horticultural pest, the Mediterranean fruit fly (medfly) Ceratitis capitata (Diptera, Tephritidae), with an emphasis on Europe and California. We reviewed and refined a published CLIMEX model for C. capitata, taking into consideration new records in marginal locations, with a particular focus on Europe. To assess the model fit and to aid in interpreting the meaning of the new European distribution records, we used a time series climate dataset to explore the temporal patterns of climate suitability for C. capitata from 1970 to 2019. At selected bellwether sites in Europe, we found statistically significant trends in increasing climate suitability, as well as a substantial northward expansion in the modelled potential range. In California, we also found a significant trend of northward and altitudinal expansion of areas suitable for C. capitata establishment. These results provide further evidence of climate change impacts on species distributions and the need for innovative responses to increased invasion threats.

An integrative phenology and climatic suitability model for emerald ash borer

Introduction

Decision support models that predict both when and where to expect emerald ash borer (EAB), Agrilus planipennis Fairmaire (Coleoptera: Buprestidae), are needed for the development and implementation of effective management strategies against this major invasive pest of ash (Fraxinus species) in North America and other regions such as Europe. We present a spatialized model of phenology and climatic suitability for EAB for use in the Degree-Days, Risk, and Phenological event mapping (DDRP) platform, which is an open-source decision support tool to help detect, monitor, and manage invasive threats.

Methods

We evaluated the model using presence records from three geographic regions (China, North America, and Europe) and a phenological dataset consisting primarily of observations from the northeastern and midwestern United States. To demonstrate the model, we produced phenological event maps for a recent year and tested for trends in EAB's phenology and potential distribution over a recent 20-year period.

Results

Overall, the model exhibited strong performance. Presence was correctly estimated for over 99% of presence records and predicted dates of adult phenological events corresponded closely with observed dates, with a mean absolute error of ca. 7 days and low estimates of bias. Climate stresses were insufficient to exclude EAB from areas with native Fraxinus species in North America and Europe; however, extreme weather events, climate warming, and an inability for EAB to complete its life cycle may reduce suitability for some areas. Significant trends toward earlier adult emergence over 20 years occurred in only some areas.

Discussion

Near real-time model forecasts for the conterminous United States are available at two websites to provide end-users with decision-support for surveillance and management of this invasive pest. Forecasts of adult emergence and egg hatch are particularly relevant for surveillance and for managing existing populations with pesticide treatments and parasitoid introductions.

Modeling impacts of climate change on the geographic distribution and abundances of Tamarindus indica in Tigray region, Ethiopia

Tamarindus indica is a multipurpose dry land species in sub-Saharan that is traditionally used to build resilience into the farming system. The species is highly threatened and listed on the IUCN Red List. However, information on how climatic condition locally influences its ecological distribution is limited. This study investigates the current and future suitable habitat for the species in the Tigray region, in northern Ethiopia. A total of 220 species presence points and the number of T. indica within a 50 m × 50 m plot were collected. In addition, 19 bioclimatic variables, 3 topographic variables and soil data were used to model the impact of future climate conditions under two Representative Concentration Path Ways (RCP4.5 and RCP 8.5). MaxEnt-v-3.3.3 k, Diva-GIS-7.5, and GIS10.6 were used to model the current and future distribution. SPSSv-26 was also utilized to analyze the relationship between the species' abundance and environmental variables. Results showed that the environmental variables determining most for the distribution of T. indica were mean diurnal range (Bio2 (56.9%)); temperature seasonality (Bio4 (10.3%)) and temperature annual range (Bio7 (9.2%)). The model suggested that the current distribution of T. indica covers an area of 9209 km² (14.04%). This would have increased to 29,363 km² (44.78%) and 11,046 km² (16.85%) by 2070 under RCP4.5 and RCP8.5, respectively. Compared to the high-impact areas, new gains of suitable areas (net 25,081 km²) for the future distribution of the species were predicted in 2070-RCP4.5. Altitude, rainfall, temperature, silt contents of soils and soil pH have significant contributions (P-value<0.05) to the abundance of T. indica. However, altitude has a negative relationship with the abundance of T. indica. Additional studies to understand population trends and other threats are recommended.

Climate suitability modeling for Anastrepha suspensa (Diptera: Tephritidae): current and future invasion risk analysis

The Caribbean fruit fly, Anastrepha suspensa (Lower, 1862) (Diptera: Tephritidae), is a pest of significant economic importance in Central America and Florida (USA). This study was carried out to examine the influence of climate change on the space-time distribution of A. suspensa on temporal and spatial scales. The CLIMEX software was used to model the current distribution and for climate change. The future distribution was performed using two global climate models (GCMs), CSIRO-Mk3.0 (CS) and MIROC-H (MR), under the emission scenarios (SRES) A2 and A1B for the years 2050, 2080, and 2100. The results indicate a low potential for global distribution of A. suspensa in all scenarios studied. However, tropical areas were identified with high climatic suitability for A. suspensa in South America, Central America, Africa, and Oceania until the end of the century. Projections of areas with climatic suitability for A. suspensa can provide helpful information to develop preventive strategies of phytosanitary management avoiding economic impacts with the introduction of the species.

Climate change and plant pathogens

Global food security is threatened by climate change, both directly through responses of crop physiology and productivity, and indirectly through responses of plant-associated microbiota, including plant pathogens. While the interactions between host plants, pathogens and environmental drivers can be complex, recent research is beginning to indicate certain overall patterns in how plant diseases will affect crop production in future. Here, we review the results of three methodological approaches: large-scale observational studies, process-based disease models and experimental comparisons of pathosystems under current and future conditions. We find that observational studies have tended to identify rising temperatures as the primary driver of disease impact. Process-based models suggest that rising temperatures will lead to latitudinal shifts in disease pressure, but drying conditions could mitigate disease risk. Experimental studies suggest that rising atmospheric CO₂ will exacerbate disease impacts. Plant diseases may therefore counteract any crop yield increases due to climate change.

Predicting potential global and future distributions of the African armyworm (Spodoptera exempta) using species distribution models

Invasive species have historically been a problem derived from global trade and transport. To aid in the control and management of these species, species distribution models (SDMs) have been used to help predict possible areas of expansion. Our focal organism, the African Armyworm (AAW), has historically been known as an important pest species in Africa, occurring at high larval densities and causing outbreaks that can cause enormous economic damage to staple crops. The goal of this study is to map the AAW’s present and potential distribution in three future scenarios for the region, and the potential global distribution if the species were to invade other territories, using 40 years of data on more than 700 larval outbreak reports from Kenya and Tanzania. The present distribution in East Africa coincides with its previously known distribution, as well as other areas of grassland and cropland, which are the host plants for this species. The different future climatic scenarios show broadly similar potential distributions in East Africa to the present day. The predicted global distribution shows areas where the AAW has already been reported, but also shows many potential areas in the Americas where, if transported, environmental conditions are suitable for AAW to thrive and where it could become an invasive species.

On the road: Anthropogenic factors drive the invasion risk of a wild solitary bee species

Complex biotic networks of invaders and their new environments pose immense challenges for researchers aiming to predict current and future occupancy of introduced species. This might be especially true for invasive bees, as they enter novel trophic interactions. Little attention has been paid to solitary, invasive wild bees, despite their increasing recognition as a potential global threat to biodiversity. Here, we present the first comprehensive species distribution modelling approach targeting the invasive bee Megachile sculpturalis, which is currently undergoing parallel range expansion in North America and Europe. While the species has largely colonised the most highly suitable areas of North America over the past decades, its invasion of Europe seems to be in its early stages. We showed that its current distribution is largely explained by anthropogenic factors, suggesting that its spread is facilitated by road and maritime traffic, largely beyond its intrinsic dispersal ability. Our results suggest that M. sculpturalis is likely to be negatively affected by future climate change in North America, while in Europe the potential suitable areas at-risk of invasion remain equally large. Based on our study, we emphasise the role of expert knowledge for evaluation of ecologically meaningful variables implemented and interpreted for species distribution modelling. We strongly recommend that the monitoring of this and other invasive pollinator species should be prioritised in areas identified as at-risk, alongside development of effective management strategies.

Potential Distribution of Invasive Boxwood Blight Pathogen (Calonectriapseudonaviculata) as Predicted by Process-Based and Correlative Models

Boxwood blight caused by Cps is an emerging disease that has had devastating impacts on Buxus spp. in the horticultural sector, landscapes, and native ecosystems. In this study, we produced a process-based climatic suitability model in the CLIMEX program and combined outputs of four different correlative modeling algorithms to generate an ensemble correlative model. All models were fit and validated using a presence record dataset comprised of Cps detections across its entire known invaded range. Evaluations of model performance provided validation of good model fit for all models. A consensus map of CLIMEX and ensemble correlative model predictions indicated that not-yet-invaded areas in eastern and southern Europe and in the southeastern, midwestern, and Pacific coast regions of North America are climatically suitable for Cps establishment. Most regions of the world where Buxus and its congeners are native are also at risk of establishment. These findings provide the first insights into Cps global invasion threat, suggesting that this invasive pathogen has the potential to significantly expand its range.

Modeling the Influence of Abiotic and Biotic Factors on Spatial and Temporal Fluctuations of Prostephanus truncatus (Coleoptera: Bostrichidae) Populations in Mozambique

The larger grain borer, Prostephanus truncatus (Horn) (Coleoptera: Bostrichidae), is a serious pest of stored maize in Mozambique and in other African countries. This study investigated the influence of abiotic and biotic factors on populations of P. truncatus at four sites over a two-year period (2013-2014) in Mozambique. Fourteen 250 × 250 m quadrants were selected at each site. Plant species diversity, temperature, precipitation, and relative humidity data were recorded. Pheromone-baited Uni-traps were used to monitor P. truncatus inside each quadrant. In addition, plant species were identified using visual observation and measurement of morphological features of leaves and fruits, and quantified, after which the percentage of host plant species of P. truncatus was determined out of all species in each quadrant. Multiple regression analysis and generalized linear models showed that host plant species dominance, maximum and minimum temperature, relative humidity, and rainfall influenced the variations in P. truncatus abundance. The development of these models of P. truncatus flight activity provides a baseline for further studies predicting dispersal and potential areas of invasion by this pest.

Current and future potential distributions of Helicoverpa punctigera (Lepidoptera: Noctuidae): is this the next FAW?

Helicoverpa punctigera (Wallengren), the native budworm, is an important highly polyphagous pest that has caused serious damage on a wide variety of crops in Australia. In Australia, its range overlaps that of its congener, Helicoverpa armigera (Hübner), a notorious invasive pest globally. We used CLIMEX, a bioclimatic niche modelling software package, to estimate the potential geographical distribution of H. punctigera under current and future climates (A1B scenario). Under both current and future climate conditions, the model indicates that H. punctigera could establish throughout the tropics and subtropics. Comparing the potential distributions under each climate scenario revealed that in the future its potential distribution is likely to shift poleward and into higher altitudes, into areas that are currently too cold as observed in the South of Brazil, Europe, North America, South East Asia, and South Pacific Islands including New Zealand. The projected potential distribution can inform pre- and post-border biosecurity strategies for the management of this pest in each country.

Comparing, evaluating and combining statistical species distribution models and CLIMEX to forecast the distributions of emerging crop pests

BACKGROUND

Forecasting the spread of emerging pests is widely requested by pest management agencies in order to prioritise and target efforts. Two widely used approaches are statistical Species Distribution Models (SDMs) and CLIMEX, which uses ecophysiological parameters. Each have strengths and weaknesses. SDMs can incorporate almost any environmental condition and their accuracy can be formally evaluated to inform managers. However, accuracy is affected by data availability and can be limited for emerging pests, and SDMs usually predict year-round distributions, not seasonal outbreaks. CLIMEX can formally incorporate expert ecophysiological knowledge and predicts seasonal outbreaks. However, the methods for formal evaluation are limited and rarely applied. We argue that both approaches can be informative and complementary, but we need tools to integrate and evaluate their accuracy. Here we develop such an approach, and test it by forecasting the potential global range of the tomato pest Tuta absoluta.

RESULTS

The accuracy of previously developed CLIMEX and new statistical SDMs were comparable on average, but the best statistical SDM techniques and environmental data substantially outperformed CLIMEX. The ensembled approach changes expectations of T. absoluta's spread. The pest's environmental tolerances and potential range in Africa, the Arabian Peninsula, Central Asia and Australia will be larger than previous estimates.

CONCLUSION

We recommend that CLIMEX be considered one of a suite of SDM techniques and thus evaluated formally. CLIMEX and statistical SDMs should be compared and ensembled if possible. We provide code that can be used to do so when employing the biomod suite of SDM techniques. © 2021 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Distribution and invasion risk assessments of Chrysodeixis includens (Walker, [1858]) (Lepidoptera: Noctuidae) using CLIMEX

Chrysodeixis includens is a polyphagous pest restricted to the American continent. The occurrence of C. includens is allied, among other factors, by favorable conditions such as temperature, humidity, presence of hosts, and migratory behavior. In this work, we built spatiotemporal species distribution models at continental and global levels for the distribution of C. includens using CLIMEX to determine times and regions favorable for year-round survival and migration of this species and in case of invasion on other continents to apply timely and right phytosanitary measures. Our models estimated high climate suitability for C. includens in Central and large proportions of South America throughout the year. Moreover, there is suitability for C. includens growth in all months of the year in Central and northern part of South America. In the northern hemisphere, these conditions range from April to October, while in mid-southern parts of South America, favorable periods comprise October through June. The countries with the highest suitability for C. includens outside the American continent are located on the African and Asian continents. Our results show variable climate suitability for C. includens during the year that help to understand likely migration pattern in North America. This information would direct efforts for appropriate C. includens management during warm and moist periods of the year. Furthermore, our models notify the need for the development of strategies for the inspection and interception of C. includens especially in central Africa, India, South and Southeast Asia, and Northeast Australia.

Modelling the Potential Geographic Distribution of Two Trissolcus Species for the Brown Marmorated Stink Bug, Halyomorpha halys

Simple Summary

The brown marmorated stink bug, Halyomorpha halys (Stål) (Hemiptera: Pentatomidae), native to Asia, has been accidentally introduced to Europe and North America, where it has become a key pest by feeding on numerous important crops. Although H. halys has not yet established in Australia, there is a general consensus that this is only a matter of time, and thus, it is prudent to investigate management options. Previous studies have modelled the potential distribution of H. halys and one of its principal natural enemies, Trissolcus japonicus (Ashmead) (Hymenoptera: Scelionidae). Here, we developed a similar model of the potential distribution of Trissolcus mitsukurii (Ashmead), which is a primary parasitoid of H. halys in Japan, and which was introduced to Australia in the 1960s to control another introduced pest. We used the three models to examine the overlap in the projected distributions of both T. mitsukurii and T. japonicus with H. halys, and to assess the potential for the two Trissolcus species to help mitigate the impacts of H. halys in its global adventive range.

Abstract

The brown marmorated stink bug, Halyomorpha halys (Stål) (Hemiptera: Pentatomidae), is native to northeast Asia. It was accidentally introduced to Europe and North America, where it has become a key pest, feeding on many important crops. Previous eco-climatic niche modelling indicates that H. halys could expand its distribution vastly, and numerous border interceptions of this pest in many countries, including Australia and New Zealand, indicate that it would be prudent to prepare for its eventual arrival. Similar niche modelling was used to assess the potential distribution of Trissolcus japonicus (Ashmead) (Hymenoptera: Scelionidae), the key parasitoid of H. halys in China. Trissolcus mitsukurii (Ashmead) is one of the main parasitoids of H. halys in Japan. It is known to have existed in Australia since the early 20th century and was also specifically introduced to Australia in the 1960s, and it has now also invaded Italy. We used CLIMEX to model the climatic niche of T. mitsukurii to estimate its global potential distribution. We found that T. mitsukurii should be able to significantly expand its range globally, and that there is a significant degree of overlap in the projected ranges of T. mitsukurii, T. japonicus and H. halys. From a biological control perspective, this implies that the two Trissolcus species may be able to help mitigate the potential impacts of H. halys.

DDRP: Real-time phenology and climatic suitability modeling of invasive insects

Rapidly detecting and responding to new invasive species and the spread of those that are already established is essential for reducing their potential threat to food production, the economy, and the environment. We describe a new spatial modeling platform that integrates mapping of phenology and climatic suitability in real-time to provide timely and comprehensive guidance for stakeholders needing to know both where and when invasive insect species could potentially invade the conterminous United States. The Degree-Days, Risk, and Phenological event mapping (DDRP) platform serves as an open-source and relatively easy-to-parameterize decision support tool to help detect new invasive threats, schedule monitoring and management actions, optimize biological control, and predict potential impacts on agricultural production. DDRP uses a process-based modeling approach in which degree-days and temperature stress are calculated daily and accumulate over time to model phenology and climatic suitability, respectively. Outputs include predictions of the number of completed generations, life stages present, dates of phenological events, and climatically suitable areas based on two levels of climate stress. Species parameter values can be derived from laboratory and field studies or estimated through an additional modeling step. DDRP is written entirely in R, making it flexible and extensible, and capitalizes on multiple R packages to generate gridded and graphical outputs. We illustrate the DDRP modeling platform and the process of model parameterization using two invasive insect species as example threats to United States agriculture: the light brown apple moth, Epiphyas postvittana, and the small tomato borer, Neoleucinodes elegantalis. We then discuss example applications of DDRP as a decision support tool, review its potential limitations and sources of model error, and outline some ideas for future improvements to the platform.

Global Cropland Connectivity: A Risk Factor for Invasion and Saturation by Emerging Pathogens and Pests

The geographic pattern of cropland is an important risk factor for invasion and saturation by crop-specific pathogens and arthropods. Understanding cropland networks supports smart pest sampling and mitigation strategies. We evaluate global networks of cropland connectivity for key vegetatively propagated crops (banana and plantain, cassava, potato, sweet potato, and yam) important for food security in the tropics. For each crop, potential movement between geographic location pairs was evaluated using a gravity model, with associated uncertainty quantification. The highly linked hub and bridge locations in cropland connectivity risk maps are likely priorities for surveillance and management, and for tracing intraregion movement of pathogens and pests. Important locations are identified beyond those locations that simply have high crop density. Cropland connectivity risk maps provide a new risk component for integration with other factors-such as climatic suitability, genetic resistance, and global trade routes-to inform pest risk assessment and mitigation.

Landscape structure and climate drive population dynamics of an insect vector within intensely managed agroecosystems

Characterizing factors affecting insect pest populations across variable landscapes is a major challenge for agriculture. In natural ecosystems, insect populations are strongly mediated by landscape and climatic factors. However, it has proven difficult to evaluate if similar factors predict pest dynamics in agroecosystems because control tactics exert strong confounding effects. We addressed this by assessing whether species distribution models could effectively characterize dynamics of an insect pest in intensely managed agroecosystems. Our study used a regional multi-year data set to assess landscape and climatic drivers of potato psyllid (Bactericera cockerelli) populations, which are often subjected to calendar-based insecticide treatments because they transmit pathogens to crops. Despite this, we show that psyllid populations were strongly affected by landscape and climatic factors. Psyllids were more abundant in landscapes with high connectivity, low crop diversity, and large natural areas. Psyllid population dynamics were also mediated by climatic factors, particularly precipitation and humidity. Our results show that many of the same factors that drive insect population dynamics in natural ecosystems can have similar effects in an intensive agroecosystem. More broadly, our study shows that models incorporating landscape and climatic factors can describe pest populations in agroecosystems and may thus promote more sustainable pest management.

Predicting the Potential Invasive Range of Klambothrips myopori (Thysanopetra: Phlaeothripidae)

Temperature-driven development of myoporum thrips, Klambothrips myopori (Thysanopetra: Phlaeothripidae), was examined at seven constant temperatures (15, 17, 20, 25, 30, 34, and 35.5°C) on Myoporum laetum Forst (Lamiales: Scrophulariaceae). Thrips successfully completed development to adult stage between 15 and 35.5°C. One linear and three nonlinear models were fitted to describe developmental rates of K. myopori as a function of temperature, and for estimating thermal constants and bioclimatic thresholds (Tmin, Topt, and Tmax). The Briere-1 model performed best in describing the developmental rate of cumulative life stages. Two ecological niche models, CLIMEX and Maxent, were used to predict the geographic distribution of K. myopori in its native range and globally. Overall predictions of environmental suitability differed greatly across models. The CLIMEX model accurately predicted known invasive and native localities, while the Maxent model failed to predict the native localities and parts of the invasive range. Based on the CLIMEX model, K. myopori has the potential to establish in many regions of the globe.

Climate change effects on Black Sigatoka disease of banana

Climate change has significantly altered species distributions in the wild and has the potential to affect the interactions between pests and diseases and their human, animal and plant hosts. While several studies have projected changes in disease distributions in the future, responses to historical climate change are poorly understood. Such analyses are required to dissect the relative contributions of climate change, host availability and dispersal to the emergence of pests and diseases. Here, we model the influence of climate change on the most damaging disease of a major tropical food plant, Black Sigatoka disease of banana. Black Sigatoka emerged from Asia in the late twentieth Century and has recently completed its invasion of Latin American and Caribbean banana-growing areas. We parametrize an infection model with published experimental data and drive the model with hourly microclimate data from a global climate reanalysis dataset. We define infection risk as the sum of the number of modelled hourly spore cohorts that infect a leaf over a time interval. The model shows that infection risk has increased by a median of 44.2% across banana-growing areas of Latin America and the Caribbean since the 1960s, due to increasing canopy wetness and improving temperature conditions for the pathogen. Thus, while increasing banana production and global trade have probably facilitated Black Sigatoka establishment and spread, climate change has made the region increasingly conducive for plant infection.

This article is part of the theme issue ‘Modelling infectious disease outbreaks in humans, animals and plants: approaches and important themes’. This issue is linked with the subsequent theme issue ‘Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control’.

Many unreported crop pests and pathogens are probably already present

Invasive species threaten global biodiversity, food security and ecosystem function. Such incursions present challenges to agriculture where invasive species cause significant crop damage and require major economic investment to control production losses. Pest risk analysis (PRA) is key to prioritize agricultural biosecurity efforts, but is hampered by incomplete knowledge of current crop pest and pathogen distributions. Here, we develop predictive models of current pest distributions and test these models using new observations at subnational resolution. We apply generalized linear models (GLM) to estimate presence probabilities for 1,739 crop pests in the CABI pest distribution database. We test model predictions for 100 unobserved pest occurrences in the People's Republic of China (PRC), against observations of these pests abstracted from the Chinese literature. This resource has hitherto been omitted from databases on global pest distributions. Finally, we predict occurrences of all unobserved pests globally. Presence probability increases with host presence, presence in neighbouring regions, per capita GDP and global prevalence. Presence probability decreases with mean distance from coast and known host number per pest. The models are good predictors of pest presence in provinces of the PRC, with area under the ROC curve (AUC) values of 0.75-0.76. Large numbers of currently unobserved, but probably present pests (defined here as unreported pests with a predicted presence probability >0.75), are predicted in China, India, southern Brazil and some countries of the former USSR. We show that GLMs can predict presences of pseudoabsent pests at subnational resolution. The Chinese literature has been largely inaccessible to Western academia but contains important information that can support PRA. Prior studies have often assumed that unreported pests in a global distribution database represent a true absence. Our analysis provides a method for quantifying pseudoabsences to enable improved PRA and species distribution modelling.

Many unreported crop pests and pathogens are probably already present

Invasive species threaten global biodiversity, food security and ecosystem function. Such incursions present challenges to agriculture where invasive species cause significant crop damage and require major economic investment to control production losses. Pest risk analysis (PRA) is key to prioritize agricultural biosecurity efforts, but is hampered by incomplete knowledge of current crop pest and pathogen distributions. Here, we develop predictive models of current pest distributions and test these models using new observations at subnational resolution. We apply generalized linear models (GLM) to estimate presence probabilities for 1,739 crop pests in the CABI pest distribution database. We test model predictions for 100 unobserved pest occurrences in the People's Republic of China (PRC), against observations of these pests abstracted from the Chinese literature. This resource has hitherto been omitted from databases on global pest distributions. Finally, we predict occurrences of all unobserved pests globally. Presence probability increases with host presence, presence in neighbouring regions, per capita GDP and global prevalence. Presence probability decreases with mean distance from coast and known host number per pest. The models are good predictors of pest presence in provinces of the PRC, with area under the ROC curve (AUC) values of 0.75-0.76. Large numbers of currently unobserved, but probably present pests (defined here as unreported pests with a predicted presence probability >0.75), are predicted in China, India, southern Brazil and some countries of the former USSR. We show that GLMs can predict presences of pseudoabsent pests at subnational resolution. The Chinese literature has been largely inaccessible to Western academia but contains important information that can support PRA. Prior studies have often assumed that unreported pests in a global distribution database represent a true absence. Our analysis provides a method for quantifying pseudoabsences to enable improved PRA and species distribution modelling.

Climate, human influence and the distribution limits of the invasive European earwig, Forficula auricularia, in Australia

BACKGROUND

By modelling species-environment relationships of pest species, it is possible to understand potential limits to their distributions when they invade new regions, and their likely continued spread. The European earwig, Forficula auricularia, is a non-native invasive species in Australia that has been in the country for over 170 years. However, in the last few decades it has invaded new areas. Unlike in other countries, F. auricularia is a pest species of grain production in Australia. In this study we detail the Australian distribution of this species, adding new samples focused around grain-growing regions. Using this information, we build global species distribution models for F. auricularia to better understand species-environment relationships.

RESULTS

Our models indicate that the distribution of F. auricularia is strongly associated with temperate through to semi-arid environments, a high winter rainfall and pronounced temperature seasonality. We identified regions that hold suitable, but as yet vacant, niche space for Australian populations, suggesting further potential for range expansion. Beyond climate, an index describing human influence on the landscape was important to understand the distribution limits of this pest. We identified regions where there was suitable climate space, but which F. auricularia has not occupied, probably due to low levels of human impact.

CONCLUSION

Modelling the global distribution of a non-native pest species aided understanding of the regional distribution limits within Australia and highlighted the usefulness of human impact measures for modelling globally invasive insect species. © 2018 Society of Chemical Industry.

Herbarium data: Global biodiversity and societal botanical needs for novel research

Building on centuries of research based on herbarium specimens gathered through time and around the globe, a new era of discovery, synthesis, and prediction using digitized collections data has begun. This paper provides an overview of how aggregated, open access botanical and associated biological, environmental, and ecological data sets, from genes to the ecosystem, can be used to document the impacts of global change on communities, organisms, and society; predict future impacts; and help to drive the remediation of change. Advocacy for botanical collections and their expansion is needed, including ongoing digitization and online publishing. The addition of non-traditional digitized data fields, user annotation capability, and born-digital field data collection enables the rapid access of rich, digitally available data sets for research, education, informed decision-making, and other scholarly and creative activities. Researchers are receiving enormous benefits from data aggregators including the Global Biodiversity Information Facility (GBIF), Integrated Digitized Biocollections (iDigBio), the Atlas of Living Australia (ALA), and the Biodiversity Heritage Library (BHL), but effective collaboration around data infrastructures is needed when working with large and disparate data sets. Tools for data discovery, visualization, analysis, and skills training are increasingly important for inspiring novel research that improves the intrinsic value of physical and digital botanical collections.

Dynamic models in research and management of biological invasions

Invasive species are increasing in number, extent and impact worldwide. Effective invasion management has thus become a core socio-ecological challenge. To tackle this challenge, integrating spatial-temporal dynamics of invasion processes with modelling approaches is a promising approach. The inclusion of dynamic processes in such modelling frameworks (i.e. dynamic or hybrid models, here defined as models that integrate both dynamic and static approaches) adds an explicit temporal dimension to the study and management of invasions, enabling the prediction of invasions and optimisation of multi-scale management and governance. However, the extent to which dynamic approaches have been used for that purpose is under-investigated. Based on a literature review, we examined the extent to which dynamic modelling has been used to address invasions worldwide. We then evaluated how the use of dynamic modelling has evolved through time in the scope of invasive species management. The results suggest that modelling, in particular dynamic modelling, has been increasingly applied to biological invasions, especially to support management decisions at local scales. Also, the combination of dynamic and static modelling approaches (hybrid models with a spatially explicit output) can be especially effective, not only to support management at early invasion stages (from prevention to early detection), but also to improve the monitoring of invasion processes and impact assessment. Further development and testing of such hybrid models may well be regarded as a priority for future research aiming to improve the management of invasions across scales.

Accuracy of climate-based forecasts of pathogen spread

Species distribution models (SDMs) are a tool for predicting the eventual geographical range of an emerging pathogen. Most SDMs, however, rely on an assumption of equilibrium with the environment, which an emerging pathogen, by definition, has not reached. To determine if some SDM approaches work better than others for modelling the spread of emerging, non-equilibrium pathogens, we studied time-sensitive predictive performance of SDMs for Batrachochytrium dendrobatidis, a devastating infectious fungus of amphibians, using multiple methods trained on time-incremented subsets of the available data. We split our data into timeline-based training and testing sets, and evaluated models on each set using standard performance criteria, including AUC, kappa, false negative rate and the Boyce index. Of eight models examined, we found that boosted regression trees and random forests performed best, closely followed by MaxEnt. As expected, predictive performance generally improved with the length of time series used for model training. These results provide information on how quickly the potential extent of an emerging disease may be determined, and identify which modelling frameworks are likely to provide useful information during the early phases of pathogen expansion.

Accuracy of climate-based forecasts of pathogen spread

Species distribution models (SDMs) are a tool for predicting the eventual geographical range of an emerging pathogen. Most SDMs, however, rely on an assumption of equilibrium with the environment, which an emerging pathogen, by definition, has not reached. To determine if some SDM approaches work better than others for modelling the spread of emerging, non-equilibrium pathogens, we studied time-sensitive predictive performance of SDMs for Batrachochytrium dendrobatidis, a devastating infectious fungus of amphibians, using multiple methods trained on time-incremented subsets of the available data. We split our data into timeline-based training and testing sets, and evaluated models on each set using standard performance criteria, including AUC, kappa, false negative rate and the Boyce index. Of eight models examined, we found that boosted regression trees and random forests performed best, closely followed by MaxEnt. As expected, predictive performance generally improved with the length of time series used for model training. These results provide information on how quickly the potential extent of an emerging disease may be determined, and identify which modelling frameworks are likely to provide useful information during the early phases of pathogen expansion.

The use of occurrence data to predict the effects of climate change on insects

Experimental information on the roles played by climatic factors in determining the ecology and distribution of insect species is scarce. This has stimulated the increasing use of the climatic characteristics of the localities in which the species are observed to derive predictions under different climatic scenarios (the so called species-distribution models or SDMs). This text reviews the main limitations of these correlative models when they are applied to organisms, such as insects, that are characterized by a high degree of collector bias and incompleteness. It is argued that SDMs must rely solely on presence information, rejecting the use of background or pseudoabsences, and that we are not predicting the future distribution of a species but exploring the future location of the climatic conditions in which a species was observed. The scarcity and bias of the available occurrence information in insects as well as our ignorance about the non-climatic factors delimiting species ranges forces us to be extremely careful. It is therefore desirable to avoid the use of central tendency measures reflecting supposed optimum niche conditions because they are particularly dependent on the quantity and biases of the occurrence information. The use of simple algorithms and procedures aimed at extracting information on environmental limits from the available occurrences would be more convenient in this case.

Ecological change predicts population dynamics and genetic diversity over 120 000 years

While ecological effects on short-term population dynamics are well understood, their effects over millennia are difficult to demonstrate and convincing evidence is scant. Using coalescent methods, we analysed past population dynamics of three lizard species (Psammodromus hispanicus, P. edwardsianus, P. occidentalis) and linked the results with climate change data covering the same temporal horizon (120 000 years). An increase in population size over time was observed in two species, and in P. occidentalis, no change was observed. Temporal changes in temperature seasonality and the maximum temperature of the warmest month were congruent with changes in population dynamics observed for the three species and both variables affected population density, either directly or indirectly (via a life-history trait). These results constitute the first solid link between ecological change and long-term population dynamics. The results moreover suggest that ecological change leaves genetic signatures that can be retrospectively traced, providing evidence that ecological change is a crucial driver of genetic diversity and speciation.

Downscaling Pest Risk Analyses: Identifying Current and Future Potentially Suitable Habitats for Parthenium hysterophorus with Particular Reference to Europe and North Africa

Pest Risk Assessments (PRAs) routinely employ climatic niche models to identify endangered areas. Typically, these models consider only climatic factors, ignoring the 'Swiss Cheese' nature of species ranges due to the interplay of climatic and habitat factors. As part of a PRA conducted for the European and Mediterranean Plant Protection Organization, we developed a climatic niche model for Parthenium hysterophorus, explicitly including the effects of irrigation where it was known to be practiced. We then downscaled the climatic risk model using two different methods to identify the suitable habitat types: expert opinion (following the EPPO PRA guidelines) and inferred from the global spatial distribution. The PRA revealed a substantial risk to the EPPO region and Central and Western Africa, highlighting the desirability of avoiding an invasion by P. hysterophorus. We also consider the effects of climate change on the modelled risks. The climate change scenario indicated the risk of substantial further spread of P. hysterophorus in temperate northern hemisphere regions (North America, Europe and the northern Middle East), and also high elevation equatorial regions (Western Brazil, Central Africa, and South East Asia) if minimum temperatures increase substantially. Downscaling the climate model using habitat factors resulted in substantial (approximately 22-53%) reductions in the areas estimated to be endangered. Applying expert assessments as to suitable habitat classes resulted in the greatest reduction in the estimated endangered area, whereas inferring suitable habitats factors from distribution data identified more land use classes and a larger endangered area. Despite some scaling issues with using a globally conformal Land Use Systems dataset, the inferential downscaling method shows promise as a routine addition to the PRA toolkit, as either a direct model component, or simply as a means of better informing an expert assessment of the suitable habitat types.

Changes in the distribution of multispecies pest assemblages affect levels of crop damage in warming tropical Andes

Climate induced species range shifts might create novel interactions among species that may outweigh direct climatic effects. In an agricultural context, climate change might alter the intensity of competition or facilitation interactions among pests with, potentially, negative consequences on the levels of damage to crop. This could threaten the productivity of agricultural systems and have negative impacts on food security, but has yet been poorly considered in studies. In this contribution, we constructed and evaluated process-based species distribution models for three invasive potato pests in the Tropical Andean Region. These three species have been found to co-occur and interact within the same potato tuber, causing different levels of damage to crop. Our models allowed us to predict the current and future distribution of the species and therefore, to assess how damage to crop might change in the future due to novel interactions. In general, our study revealed the main challenges related to distribution modeling of invasive pests in highly heterogeneous regions. It yielded different results for the three species, both in terms of accuracy and distribution, with one species surviving best at lower altitudes and the other two performing better at higher altitudes. As to future distributions our results suggested that the three species will show different responses to climate change, with one of them expanding to higher altitudes, another contracting its range and the other shifting its distribution to higher altitudes. These changes will result in novel areas of co-occurrence and hence, interactions of the pests, which will cause different levels of damage to crop. Combining population dynamics and species distribution models that incorporate interspecific trade-off relationships in different environments revealed a powerful approach to provide predictions about the response of an assemblage of interacting species to future environmental changes and their impact on process rates.

Range-expanding pests and pathogens in a warming world

Crop pests and pathogens (CPPs) present a growing threat to food security and ecosystem management. The interactions between plants and their natural enemies are influenced by environmental conditions and thus global warming and climate change could affect CPP ranges and impact. Observations of changing CPP distributions over the twentieth century suggest that growing agricultural production and trade have been most important in disseminating CPPs, but there is some evidence for a latitudinal bias in range shifts that indicates a global warming signal. Species distribution models using climatic variables as drivers suggest that ranges will shift latitudinally in the future. The rapid spread of the Colorado potato beetle across Eurasia illustrates the importance of evolutionary adaptation, host distribution, and migration patterns in affecting the predictions of climate-based species distribution models. Understanding species range shifts in the framework of ecological niche theory may help to direct future research needs.