Pest Management Plan

Predator Control strategy
#1

I have been asked to draw up a pest management plan for our Waikanae Estuary Care Group, and would be interested in seeing what other groups have come up with. Any help gratefully received.

Chris Munn

#2

I don’t have my own yet, but happy to share some of my research so far!
There’s a heap of info here (and a lot more if you want)

Tips for Pest Control Group - https://www.doc.govt.nz/nature/pests-and-threats/predator-free-2050/community-trapping/top-tips-for-beginner-pest-control-groups/

Doc Community group info - https://www.doc.govt.nz/nature/pests-and-threats/predator-free-2050/community-trapping/top-tips-for-beginner-pest-control-groups/

Doc community impact tips - https://www.doc.govt.nz/get-involved/run-a-project/community-project-guidelines/assess-the-impact-of-the-project/

Landcare pest control decision helper - a guided questionnaire that generates a document you could print for community consultation or reports - https://pestdss.landcareresearch.co.nz/

Some of the links below (mostly regional management plans) are fairly long-winded but they are thorough and even cherry-picking through the contents page may give you some good ideas:

Wellington region Pest managment landing page - http://www.gw.govt.nz/biosecurity/

1 Like
#3

I prefer going to professionals. Good luck!

P.S
https://www.aucklandpestcontrolnz.kiwi/

#4

You could consider using the Regional Council’s ecosystem plans as a template: Peka Peka is just north of the Waikanae and Waitohu is further up the coast. You could cut out the first couple sections of govt jargon and remove the bits about invasive plant control and you would have a pretty simple format.

#5

To create a complete example, you’ll need actual data from a pest-finding device. Unfortunately, I can’t provide real data, but I can demonstrate how to generate synthetic data and visualize it on a map using Python libraries.

Let’s create a simple example using synthetic data:

Synthetic Data Generation:

pythonCopy code

import geopandas as gpd
import numpy as np

# Create synthetic pest data (random points within a specified region)
np.random.seed(42)
num_points = 50
min_x, max_x = 170.5, 170.8
min_y, max_y = -45.95, -45.85

pest_data = gpd.GeoDataFrame(geometry=gpd.points_from_xy(
    np.random.uniform(min_x, max_x, num_points),
    np.random.uniform(min_y, max_y, num_points)
))

# Save the synthetic data to a GeoJSON file
pest_data.to_file("synthetic_pest_data.geojson", driver="GeoJSON")

Visualization:

pythonCopy code

import geopandas as gpd
import matplotlib.pyplot as plt

# Load the synthetic pest data
pest_data = gpd.read_file("synthetic_pest_data.geojson")

# Plot the pest data on a map
world = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))

fig, ax = plt.subplots(figsize=(10, 8))
world.boundary.plot(ax=ax, linewidth=1)
pest_data.plot(ax=ax, color='pink', marker='o', markersize=50, alpha=0.7)
plt.title('Pest Detection in Dunedin Forest')
plt.show()

In this example, we generate synthetic pest data as random points within a specified geographical region and visualize them on a map. You can replace this synthetic data with real data from your pest-finding device by loading the actual GeoJSON file.
all rights reserved to Christopher Russell (c)