Biosecurity Commons: Risk Mapping Workflow Overview

The Risk Mapping workflow on Biosecurity Commons is designed to estimate where an invasive species or biosecurity threat is most likely to arrive and establish. It integrates environmental suitability with pathway-driven arrival processes to produce a transparent, reproducible, and decision-relevant estimate of establishment likelihood.

At its core, risk mapping is based on three barriers to establishment. For a threat to establish, it must: (1) arrive, (2) encounter a suitable abiotic environment, and (3) have access to suitable biotic conditions (e.g. hosts or habitat). If any of these barriers are not met, establishment cannot occur (Figure 1).

Figure 1: The three main elements governing the likelihood of threat establishment in an introduced region. (Camac et al. 2024).

How can establishment likelihood maps be used?

Maps of establishment likelihood are important decision-support tools in biosecurity. They help show where a pest, weed, or disease is most likely to establish if introduced, which in turn supports more targeted, transparent, and cost-effective decision-making (Camac et al. 2021; Camac et al. 2024).

• Prioritise surveillance for early detection
  These maps help identify the areas where a threat is most likely to establish, so surveillance can be focused where it is most likely to detect new incursions early.
• Target surveillance for one or many threats
  Maps can be used for a single threat, or combined across multiple threats to identify shared hotspots of establishment potential. This helps prioritise areas where surveillance may provide the greatest value across several threats at once (see Camac et al. 2021 for an example).
• Assess how well surveillance covers high-risk areas
  Establishment likelihood maps can be compared against existing or proposed surveillance designs to evaluate how much of the highest-priority area is being covered, including under different budget constraints (see Camac et al. 2020 for an example).
• Support inference about likelihood of absence
  Not detecting a threat does not prove it is absent. These maps can be combined with surveillance effort and surveillance sensitivity to estimate how likely it is that a threat is truly absent, while accounting for the fact that some areas are more suitable for establishment than others.
• Support proof-of-freedom and market access claims
  Because they improve inference about likely absence, establishment likelihood maps can help support pest freedom assessments that may be important for trade, certification, and market access.
• Initialise spread models more realistically
  Spread models need a starting point. Establishment likelihood maps provide a transparent way to place initial incursions in locations where establishment is actually plausible, rather than relying only on arbitrary or random starting locations.
• Improve invasion and spread simulations
  By accounting for propagule pressure and environmental suitability, these maps help create more realistic simulations of establishment and subsequent spread.
• Compare management strategies
  When used within spread modelling, establishment likelihood maps can help assess the likely benefits and costs of different pre-border, border, and post-border management options.
• Contribute to risk maps
  Establishment likelihood is not the same as risk on its own. However, when combined with information on consequences such as economic, environmental, social, or cultural impacts, it can be used to develop spatial risk maps.
• Prioritise protection and mitigation activities
  Risk maps built from establishment likelihood and consequence layers can help identify where surveillance, control, or asset protection activities should be prioritised to reduce overall risk.

Abiotic Suitability

Abiotic suitability describes whether the physical environment—most commonly climate—is suitable for a species to survive and reproduce. It is commonly estimated using species distribution modelling (SDM) approaches, which link observed occurrences or known physiological limits of a species to environmental variables (e.g. temperature, rainfall), and project these relationships across landscapes to identify areas of suitable conditions.

Biosecurity Commons currently provides two SDM algorithms for estimating abiotic suitability:

• Range Bagging – a presence-only ensemble envelope approach
• Climatch – a climate similarity-based method

These approaches are particularly well suited to invasive species applications, where reliable absence data are rarely available, distributions are still expanding, and species may not yet be in equilibrium with their environment.

Presence-only and climate similarity methods reduce the need for strong modelling assumptions, while still providing robust and interpretable estimates of potential climatic suitability.

They are intentionally lightweight and accessible, making them well suited to rapid assessments, screening analyses, and operational decision-making, where transparency, reproducibility, and speed are critical.

However, if users wish to apply alternative modelling approaches (e.g. MaxEnt, Boosted Regression Trees, Random Forests, or other machine learning methods), these can be run externally and imported into Biosecurity Commons. This can be done either by developing models locally and or on EcoCommons and uploading/importing model results into Biosecurity Commons.

EcoCommons is a national, cloud-based ecological modelling platform that provides access to a wide range of advanced species distribution modelling (SDM) tools. It leverages the same curated environmental datasets available in Biosecurity Commons (e.g. WorldClim, soil, and land use layers), and offers reproducible SDM workflows for model training, evaluation, and projection.

For guidance on using EcoCommons, see:

• EcoCommons Modelling Wizard Guide
• EcoCommons Support Portal

Outputs from EcoCommons (e.g. GeoTIFF suitability layers) can be uploaded directly into Biosecurity Commons and used within the risk mapping workflow.

Range Bagging

Range Bagging is an ensemble species distribution modelling (SDM) approach that constructs multiple environmental envelopes using subsets of occurrence data and environmental variables, and then aggregates predictions across these models (Drake, 2015).

Suitability is estimated as the proportion of models that predict a location as suitable:

Suitability = (number of models predicting presence) / (total number of models)

Advantages

• Does not require background or pseudo-absence data, thereby avoiding a major source of bias and uncertainty in invasive species modelling
• Outputs are comparable across species (i.e. a scores are directly comparable across species, unlike other models like Maxent)
• Easy to interpret (proportion of ensembles/models that indicate a location is suitable)
• Allows users to account for covariate uncertainty by fitting low-dimensional models (e.g. two variables), where predictors are randomly selected from a candidate set.
• Allows users to control the proportion of occurrences used in each model (via bootstrapping), improving model robustness and reducing overfitting by averaging across many resampled datasets.

A key advantage of this approach is that it does not require background or pseudo-absence data, thereby avoiding a major source of bias and uncertainty in invasive species modelling.

Limitations

• Restricted to continuous covariates
• May smooth sharp ecological thresholds. Range bagging defines species suitability using broad environmental ranges (min–max bounds), which can blur abrupt ecological limits (e.g. frost tolerance or critical temperature thresholds). This effect can be amplified when ensembling across many models, as combining multiple slightly different ranges tends to smooth boundaries further, potentially overestimating suitability near environmental edges where a species would not persist.

Climatch

Climatch is a climate-matching method that identifies locations with environmental conditions similar to those observed across a species’ known distribution (Crombie et al., 2008). It compares key climatic variables (e.g. temperature and rainfall) between known occurrence locations and a target landscape, and calculates a similarity score based on standardised differences across these variables using distance-based metrics in environmental space.

These similarity scores are then aggregated to indicate how closely the climate at each location matches that of the species’ known range, with higher scores indicating greater climatic suitability. Because it relies on presence data and does not require fitting complex statistical models, Climatch provides a transparent and computationally efficient approach for screening potential distributions of invasive species.

Advantages

• Does not require background or pseudo-absence data, thereby avoiding a major source of bias and uncertainty in invasive species modelling
• Can be applied when only a small number of occurrence records are available
• Identifies regions of similar climate to locations of established populations
• Comparable across species

Limitations

• Does not model interactions among variables
• Produces similarity rather than probability
• Less suitable for fine-scale or complex modelling

SDM Extrapolation

When applying species distribution models (SDMs), it is important to assess whether predictions are being made within the range of environmental conditions used to fit the model, or whether they are extrapolating into novel conditions. Extrapolation can reduce model reliability, as predictions are made beyond the observed relationships between species and their environment. This is particularly important in biosecurity applications, where models are often transferred across regions (e.g. from native to invasive ranges) or used under future climate scenarios.

Several diagnostic tools exist to identify where extrapolation is occurring and to characterise its nature. Two commonly used approaches are Multivariate Environmental Similarity Surfaces (MESS) and Extrapolation Detection (ExDet).

MESS and ExDet

MESS (Multivariate Environmental Similarity Surface) maps quantify how similar environmental conditions at each location are to those observed in the model training data (Elith et al., 2010):

• Positive values → conditions are within the range of the training data
• Negative values → at least one variable falls outside the training range (i.e. extrapolation)

ExDet (Extrapolation Detection) provides a more detailed assessment by identifying both the presence and type of extrapolation (Mesgaran et al., 2014):

• ExDet ≥ 0 → conditions fall within the fitted environmental space
• ExDet < 0 → extrapolation into novel environmental conditions

Importantly, ExDet distinguishes between two types of environmental novelty:

• NT1 (Univariate novelty) – at least one environmental variable lies outside the range observed in the training data
• NT2 (Combinatorial novelty) – all variables fall within their observed ranges individually, but occur in novel combinations not represented in the training data

The mapped ExDet surface reflects the most extreme form of novelty at each location (i.e. the minimum of NT1 and NT2), highlighting where model predictions should be interpreted with caution.

Biotic Suitability

Biotic suitability reflects whether the living components of an environment can support the establishment and persistence of a threat. This includes the availability of required hosts, food sources, and habitat structure, as well as interactions with other species such as competitors, predators, or facilitators. In many cases, biotic suitability acts as a key constraint on establishment even where climatic conditions are favourable, as a species must be able to locate and utilise suitable resources to survive and reproduce.

In practice, biotic suitability is often approximated using spatial datasets that describe habitat or resource availability. Common data sources include detailed land use and vegetation maps, distributions of host species or commodities, and remotely sensed indicators such as vegetation cover or greenness. At finer spatial scales, country/region-specific datasets (e.g. high-resolution land use classifications, agricultural production layers, or host crop distributions) are particularly valuable, as they can capture the heterogeneity in habitat and resource availability that is critical for establishment.

Biosecurity Commons has a wealth of datasets that can be used to inform biotic suitability (especially in the Australian context). Examine our curated datasets here.

Pest Arrival and Propagule Pressure

Establishment of an invasive species requires not only suitable environmental conditions, but also the arrival of viable individuals. This is commonly described as propagule pressure—the likelihood or expected number of contamination events entering a region. Propagule pressure is widely recognised as a key determinant of establishment risk, as higher rates of arrival increase the probability that at least one introduction event results in a self-sustaining population.

In biosecurity systems, propagule pressure is driven by pathways of entry (e.g. passengers, cargo, mail, or natural dispersal), and reflects both how often contamination events occur and whether those events are capable of establishing. To capture this, Biosecurity Commons represents pest arrival using two complementary components: leakage limits and viability limits.

Leakage and Viability Limits

Leakage limits describe the expected number of contamination events that bypass border controls for a given pathway. These are typically expressed as lower and upper bounds on the number of post-border leakage events per year, reflecting uncertainty in pathway risk and incorporating information from interception data (Camac et al. 2024a), pathway volume, and expert judgement (Hemming et al. 2017).

Viability limits describe the probability that a given contamination event is capable of establishing. This accounts for factors such as survival along the pathway, the size and condition of the introduced population, and whether individuals arrive in a state that allows successful establishment. Again, these limits can be informed by interception data (that assesses survivability and population sizes) and can be supplemented by expert elicitation (Hemming et al. 2017).

Within Biosecurity Commons, users specify both lower and upper bounds for leakage and viability parameters, along with an associated confidence level. This allows uncertainty in pathway risk to be explicitly represented, rather than relying on single point estimates. By defining plausible ranges and confidence, the platform can propagate this uncertainty through to estimates of arrival likelihood and establishment risk.

Together, these parameters separate two critical components of pest arrival risk:

• Leakage – how often contaminated material enters the system
• Viability – how likely those events are to result in a viable introduction

This distinction is important because not all pathways that contribute to leakage necessarily contribute equally to establishment risk. For example, some pathways may have relatively high leakage rates but low viability due to high mortality or small propagule sizes, resulting in a low overall contribution to establishment potential. Conversely, pathways with lower leakage but higher viability may represent a greater risk of successful establishment.

Dispersing Pathway-Specific Risk

Once propagule pressure has been estimated for each pathway, risk must be spatially distributed across the landscape. This is achieved by allocating pathway-specific arrival likelihoods to locations based on spatial weighting functions that reflect how goods, people, or natural dispersal processes move post-entry.

Distributing pathway-specific risk requires approximating how contaminated carriers (e.g. people, goods, or natural vectors) move after entering a region. Ideally, these movements would be informed by detailed empirical tracking data; however, such data are rarely available at the spatial and temporal scales required. As a result, pathway dispersal is typically approximated using a combination of available datasets and simple, interpretable weighting functions.

At a minimum, pathway analyses require information on: (1) the volume of pathway carriers (e.g. passengers, cargo, or mail), (2) the likelihood that a carrier contains a viable threat, (3) the probability that contaminated carriers bypass border controls, and (4) how those carriers disperse post-border. The final component—post-border dispersal—is often the most uncertain, and is typically approximated using spatial proxies that reflect human movement, demand, or environmental processes. 

Common data sources and assumptions used to distribute pathway risk include:

• Population density – used to approximate where people, goods, and services are concentrated, and therefore where many pathways (e.g. returning residents, mail, imported goods) are likely to terminate.
• Tourist accommodation density – used to represent where international visitors are likely to congregate shortly after arrival.
• Distance from ports or airports – incorporated via distance-decay functions to reflect decreasing likelihood of arrival with increasing distance from points of entry.
• Transport and trade data – such as container destination data or commodity flow information, where available, to more directly represent movement pathways.
• Land use and agricultural activity – used to distribute pathways linked to farming systems (e.g. fertiliser, machinery), often coupled with measures such as farm density or production intensity.

In practice, these datasets are combined into pathway-specific weighting functions that describe how arriving pathway units are distributed across the landscape. These functions allocate the proportion of arrivals expected at each location based on plausible movement patterns. For example, passenger-based pathways may be modelled using a combination of distance from airports and population density, while tourism-related pathways may combine distance decay with accommodation density. More broadly, these weighting functions are designed to reflect realistic post-entry behaviour, such as the tendency for goods to move toward areas of high demand, or for travellers to remain close to entry points shortly after arrival. There is empirical support for the use of such proxies, with studies consistently showing that factors such as land use, road density, and human population density are strongly associated with first detections of exotic threats, even after accounting for potential survey bias (e.g. Dodd et al. 2016).

Although simplified, these approaches provide a transparent and flexible way to approximate post-border dispersal, enabling the construction of spatially explicit propagule pressure maps that can be integrated with abiotic and biotic suitability to estimate overall establishment likelihood.

Mathematical Framework

The workflow models arrival and establishment as a sequence of probabilistic processes, explicitly incorporating uncertainty in both pathway leakage and viability. Users provide lower and upper bounds for each parameter, and these bounds are used to define probability distributions that are then propagated through the workflow.

Leakage (Entry Events)

Plain language: Users specify lower and upper bounds for the annual number of leakage events for pathway k. These bounds are used to estimate the centre and spread of a log-normal distribution for the expected annual entry rate, λ_k. A Poisson distribution is then used to simulate the actual number of entry events in a given year. Note that the 1.96 refers the number of standard deviations that associated with the level of confidence set. In this case, we assume the default confidence level of 0.95.

What this is doing: Rather than assuming a single fixed number of arrivals, the model treats leakage as uncertain. The lower and upper bounds define a plausible range for annual entry events, and the simulation captures year-to-year variability around that uncertain rate.

Viability (Successful Introductions)

Plain language: Users also specify lower and upper bounds for the probability that a leakage event is viable. These bounds are converted onto the logit scale to define a distribution for pathway viability, p_k. The number of viable introductions is then modelled as a binomial draw from the total number of entry events. Note that the 1.96 refers the number of standard deviations that associated with the level of confidence set. In this case, we assume the default confidence level of 0.95.

What this is doing: This separates how often contaminated material enters the system from how often those entry events are actually capable of establishing. It allows the model to account for uncertainty in pathway survival, propagule size, and other biological constraints on establishment.

Spatial Allocation

Plain language: The viable introductions for pathway k are then distributed across space using a pathway-specific weight, w_i,k, which describes the proportion of pathway units expected to arrive at location i. This equation calculates the probability that at least one viable introduction arrives in cell i.

What this is doing: Some cells are more likely to receive arrivals than others because of where people travel, where goods are moved, or how pathways disperse after entry. The weighting layer allocates arrival pressure spatially, and the equation converts that into the probability of one or more viable arrivals reaching a cell.

Combining Pathways

Plain language: Multiple pathways may contribute to arrival risk at the same location. This equation combines the pathway-specific probabilities to estimate the probability that one or more viable introductions occur in cell i across all pathways.

What this is doing: Even if each pathway is individually unlikely, the combined arrival probability can become much higher when several independent pathways contribute risk to the same location.

Final Establishment

Plain language: Establishment at a location depends on three things aligning: viable arrival, suitable abiotic conditions, and suitable biotic conditions.

What this is doing: This final step integrates the three core barriers to establishment into a single estimate of establishment likelihood. A location can only have high establishment potential when arrival pressure is non-negligible and both the physical and living environment are suitable.

For more comprehensive descriptions of this method please consult Camac et al. (2020) and the extension report Camac at al. (2021)

Common Mistakes and Best Practice

References

• Camac, J. S., Baumgartner, J. B., Robinson, A., & Elith, J. (2020). Developing pragmatic maps of establishment likelihood for plant pests.
• Camac, J. S., Baumgartner, J. B., Hester, S., Subasinghe, R., & Collins, S. (2021). Using edmaps & Zonation to inform multi-pest early detection surveillance designs.
• Camac, J. S. (2024). Detect: Designing post-border surveillance schemes. In Hester et al. (Eds.), Biosecurity: A Systems Perspective. Taylor & Francis.
• Camac, J.S., Cantele, M., Pham, V.H., Li, C., Robinson, A., Kompas, T. (2024a) Forecasting trade and biosecurity risk under climate change. CEBRA project 21B. Technical

  Report for the Department of Agriculture, Fisheries and Forestry.

• Crombie, J., Brown, L., Lizzio, J., & Hood, G. (2008). Climatch user manual.
• Drake, J.M. (2015) Range bagging: a new method for ecological niche modelling from presence-only data. J R Soc Interface; 12 (107): 20150086.
• Dodd, A.J., McCarthy, M.A., Ainsworth, N. & Burgman MA. (2016) Identifying hotspots of alien plant naturalisation in Australia: approaches and predictions. Biol Invasions 18, 631–645.
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• Hemming V, Burgman MA, Hanea AM, McBride MF, Wintle BC. (2018) A practical guide to structured expert elicitation using the IDEA protocol. Methods Ecol Evol; 9:169–180.
• Hulme, P.E. (2009) Trade, transport and trouble: managing invasive species pathways in an era of globalization. Journal of Applied Ecology, 46: 10-18.
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