Ecological Archives A025-068-A1

Vanessa M. Adams and Samantha A. Setterfield. 2015. Optimal dynamic control of invasions: applying a systematic conservation approach. Ecological Applications 25:11311141. http://dx.doi.org/10.1890/14-1062.1

Appendix A. Detailed information of habitat suitability model for gamba grass used in this study.

We compiled all available spatial data sets of environmental variables that could be associated with habitat suitability. We then investigated the trends in gamba grass density across predictor ranges. Gamba grass density maps were compiled based on aerial surveys undertaken between 2008 and 2012 The density was recorded over 250 m grids as a measure of percent cover in one of five classes: 1 = absent, 2 < 1%, 3 = 1–10%, 4 = 10–50%, 5 > 50%. We maintained these density classes for the purpose of modeling habitat suitability but condensed categories 2 and 3 into a single category ('scattered') for the purposes of our simulation modelling and cost estimates.

For our study region the available data layers that were consistently mapped across the region and appropriate for inclusion in a habitat suitability model were: land systems (12 classes), soil type (6 classes), vegetation class (6 classes), streams and elevation (Fig. A1, Table A1).

FigA1

Fig. A1. Gamba grass distribution and predictors used for habitat suitability modeling. (A) Mapped density distribution across region. (B) Distance in meters to stream. (C) Slope in degrees. (D) Vegetation type. (E) Land Systems type. (F) Soil type.


 

Table A1. Summary of predictors used.

Variable

Description

Mean and range

Distance to stream

Distance to stream. Surrogate measure of soil moisture.

418 (0-3301)

Slope

Slope. Measure of terrain.

2.73 (0-17.73)

Land system class

1=elevated plateaux surfaces
2=rugged quartz sandstone plateaux and hills
3=alluvial floodplains
4=lateritic plateaux
5=limestone plains and rises
6=basalt plains and rises
7=granite plains and rises
8=sandstone hills
9=granite hills
10=sandstone plains and rises
11=lateritic plains
12=coastal floodplains

N/A

Vegetation class

1=Eucalypt Open Forest
2=Melaleuca Forests and Woodlands
3=Cleared/bare
4=Tropical Eucalypt Woodlands and Grasslands
5=Eucalypt Woodlands
6=Eucalypt Open Woodlands

N/A

Soil class

1=Kandasols, massive earths
2=Hydrosols
3=Kandasols, yellow duplex
4=Tenosols, loams
5=Rudosols
6=Tenosols, sands

N/A

 

We summarized predictor variables across all density classes to examine data trends. Density of gamba grass declines with distance from stream and higher slopes (Table A2).

Table A2. Summary of distance and slope for survey data.

Density class

Average of distance (m)

Max of distance (m)

Average of slope (degrees)

Max of Slope (degrees)

1

457

3293

2.04

29.50

2

483

2458

2.01

19.75

3

475

2317

2.25

20.10

4

425

2317

1.91

17.98

5

354

1852

1.91

17.38

 

Density of gamba grass is positively correlated with cleared land, eucalypt woodlands (Fig. A2), hydrosols (Fig. A3), sandstone and granite land systems (Fig. A4).


FigA2

Fig. A2. Gamba grass survey sample response to vegetation type.


 

FigA3

Fig. A3. Gamba grass survey sample response to soil type.


 

FigA4

Fig. A4. Gamba grass survey sample response to land system type.


 

We used a boosted regression tree model to model gamba grass suitability. We converted density classes into a continuous variable by using the average cover for each class (1 = 0, 2 = 0.01, 3 = 0.10, 4 = 0.5 , 5 = 1). We used a 5,000 point sub-sample of our total survey data resampled from the data to remove oversampling of some of the predictors so that the sub-sample reflected the distribution of predictors across the study region. We masked out the 'coastal floodplains' land system as we did not have adequate sampling for this land system and experiments show that this is unsuitable for gamba grass due to annual flooding. We ran the boosted regression tree in R using all predictors at a learning rate of 0.01, and a tree complexity of 5 and ran for 2500 trees. The model explained 40% of deviance and had a cross validation correlation of 0.5451. The model had a poor predictive power for high cover classes, with predicted percentage cover ranged from 0 to 0.52. This is likely due to a lack of fine scale predictors that varied between density class 4 and 5 and a lower percentage of sample sites with density class 5. The predicted percent cover is provided in Fig. A5.

 

FigA5

Fig. A5. Predicted percent cover for gamba grass across the study region.


 

The response functions are provided in Fig. A6 and A7 and reflect the data trends found in Figs. A2–A4.

 

FigA6

Fig. A6. Predictor response.


 

FigA7

Fig. A7. Response curves


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