Predicting and managing invasive plant impacts at the landscape to regional scale

Transcription

1 Predicting and managing invasive plant impacts at the landscape to regional scale Dr Rieks van Klinken CSIRO Entomology (Tropical Invasive Plants) + Justine Murray (CSIRO), Carl Smith, and Clive Click to edit Master subtitle style McAlpine (University of Queensland) + DCQ + QMDC

2 Why are spatial models useful? Work out what we stand to lose, where. Then: Develop policy Prioritise resources Focus searching Design management Evaluate efforts

3 Seeing the woods for the trees

4 What are we predicting? Habitat suitability: where weeds do best, given the opportunity Susceptibility: where weeds are likely to invade Consequences: how serious the impact will be Establishment Persistence Introduction Suitability Consequences Susceptibility Consequences

5 Approach: Bayesian Belief Models + GIS Some advantages: can combine a wide range of data sources promotes system understanding by decision makers relatively low cost extremely flexible supports scenario analysis (e.g. to explore consequence of changes in climate, land use or fire regimes)

6 Model system: Parkinsonia (+ + +) One of the worst weeds in Australia Widely distributed across northern Australia Diverse climates (wet-dry tropics to arid centre) Diverse habitats (uplands, riparian, wetlands) Forms dense thorny infestations

7 Building a model for parkinsonia Expert workshop to build the model for a test region Link the model with spatial data to make predictions Validate the model Policy and management implications

8 Study region: Desert Channels 702,000 km 2

9 Data sources ecological and management studies climate modelling expert opinion

10 Workshop: identify population drivers Outcome Requirements Environmental Variables (top 3-4) Parkinsonia Susceptibility A. Establishment 1 Soil Type 2 Flooding 3 Ground cover 4 Sheep browsing B. Persistence 1 Soil type 2 Flooding 3 Climate C. Introduction 1 Dispersal by water 2 Incidental dispersal

11 Workshop: Build Bayesian Belief Network Browsing (sheep) Flooding Ground cover Soil type Flooding Establishment Persistence Climate Flooded? Distance downstream Distance away Flooding Incidental Suitability Introduction Susceptibility

12 Assign probabilities (using expert opinion) Soil type (Good or poor) Environmental Variables Soil type Browsing (sheep) Establishment (High or low) Establishment High levels Low levels Good No 90 % 10 % Good Yes 30 % 70 % Poor No 10 % 90 % Poor Yes 0 % 100 % Browsing (sheep) (yes or no)

13 Link model to spatial layers (DCQ) Parkinsonia Browsing Sheep Soil Type Climate Ground Cover Flooded Flooded frequently

14 Desert Channels Region: establishment Pr (High establishment) Dense seedling establish every 5-10 yrs < > km

15 Desert Channels Region: persistence Pr (High persistence) Most adults survive, grow and reproduce Pr (High establishment) < > km

16 Desert Channels Region: suitability Pr (High suitability) Where parkinsonia has the capacity to reach high densities Pr (High establishment) Pr (High persistence) < > km

17 Desert Channels Region: introduction Where seeds are likely to arrive from known sources < > km

18 Desert Channels Region: susceptibility Pr (High Susceptibility) Risk of highly suitable habitat being invaded in a 15 yr time-frame Pr (High suitability) Pr (Introduction) < > km

19 Scale of prediction: Longreach Shire Establishment Introduction 100 km Persistence Suitability Susceptibility

20 How good are the predictions? Known distribution Key Absent Predicted distribution (50 km grid cells) Occasional Common Abundant Still validating: - predictions very good at a coarse scale and sensible at a fine scale - problems with distribution data for validation - problems with data layers (e.g. inundation maps) - still getting property scale maps

21 What does it suggest for parkinsonia? Southwest corner is a low risk Considerable infilling possible Only a small % of area highly suitable, even in highly suitable grid cells Would still like to Quantify consequences Work through consequences for management Scale up to northern Australia

22 Applying the approach to other species Requirements Parkinsonia Lippia Parthenium A. Establishment 1 Soil Type B. Persistence 2 Flooding 3 Ground Cover 4 Sheep Browsing 1 Soil type 2 Flooding 3 Climate 1 Flooding 2 Ground Cover 3 Rainfall Patterns 4 Soil Type 1 Ground Cover 2 Tree Cover 3 Soil Moisture 1 Soil Type 2 Climate (annual) 3 Disturbance 4 Cropping Management 5 Competitive ability of parthenium C. Introduction 1 Dispersal by water 1 Dispersal by water 2 Incidental dispersal 2 Incidental dispersal 3 Garden Sources 1 Dispersal by water 2 Incidental dispersal 3 Transport via Vehicle

23 Impacts of Plant Invasions Impacts Production Biodiversity Ecosystem Services E.g. decreased land values reduced carrying capacity increased management reduced flexibility in enterprise management E.g. populations species communities vegetation types E.g. erosion control water quality visual amenity nutrient dynamics +, neutral, -

24 e.g. erosion control for an ecosystem service Source: InVEST 2008

25 H ome > Online Tutorials > Examples > Parkinsonia Model Important News and Dates Web delivery? Home Login About Us Overview Features Pricing Live Sites Online Tutorials Contact Us Your Account Bringing Bayesian Networks to the world University of Queensland: DBL, Bringing Bayesian Belief Networks to the World December 2008 DBL Interactive Version 1.1 Released DBL Interactive Version 1.1 is now available. Click here to order licences for DBL Interactive. Parkinsonia Model» View This Network Overview Parkinsonia is a Weed of National Significance in Australia (» click here for more information on Parkinsonia). It is regarded as one of the worst weeds in Australia because of its invasiveness, potential for spread, and economic and environmental impacts. It is a large woody weed (see Figure 1a), that is widely distributed across northern Australia and forms dense thorny infestations. The Parkinsonia Model is an example of how a Bayesian Network can be used to capture expert knowledge about the factors influencing the susceptibility of a site to weed invasion (see Figure 2). It was developed to model the susceptibility of landscapes to Parkinsonia invasion in the Desert Channels region in central-western Queensland, Australia (see Figure 1b). Figure 1: (a) Parkinsonia (Parkinsonia aculeata) (left) and (b) the Desert Channels region in Queensland, Australia (right) (shown in blue, area of the region is 702,000 km 2 ) The model has a general structure in which there are three requirements that determine the susceptibility of a site to Parkinsonia invasion (shown in yellow in Figure 2). These are: Introduction - introduction of the weed propagules at a site Establishment - establishment of the juvenile plants and a site Survival - survival of the plants to reproductive maturity Feeding each of these requirements are a set of environmental variables (shown in green in Figure 2). For example, the factors influencing establishment are Sheep Browsing, Flood Frequency, Ground Cover and Soil Type. Figure 3: Parkinsonia Model Using the Model Open the Parkinsonia Model by clicking here. The nodes coloured green are the environmental variable nodes, from which scenarios can be selected. Use the scenario selector (left-hand frame) in the DBLi network viewer to select a scenario, then click on Update/Refresh Network. The probabilities in the Susceptibility node will update to show the probability of your site being of High, Medium or Low susceptibility. Because all of the environmental variable nodes in the Parkinsonia model are mappable, it can be linked to a Geographic Information System (GIS) and used to produce maps. Figure 3 shows maps for the Desert Channels region in central-western Queensland, Australia, where the Parkinsonia model was used to map the probability of Parkinsonia introduction, establishment, survival and susceptibility. Figure 3: Maps showing the probability of (a) Parkinsonia introduction, (b) high establishment, (c) good survival and (d) high susceptibility for the Desert Channels regional of Queensland, Australia. a) Introduction = Yes b) Establishment = High c) Survival = Good d) Susceptibility = High Probability

26 Where next with this approach? Test transferability Same species in other regions Other species in same region Get consequences working Test on very different systems Aquatic weeds Feral animals Work closely with users to ensure follow through to policy, resourcing decisions and on-ground management