<｜begin▁of▁sentence｜>### Instruction:
Imagine we're working to manage a new conservation area, where various nodes in our ecosystem are interconnected. We have three key habitats: the meadow (0), the forest (1), and the wetlands (2). Each habitat has a directional relationship with one another, signifying a natural flow of resources or species. These relationships are as follows: the meadow supports the forest, the forest supports the wetlands, and the wetlands, in turn, support the meadow. From an ecological perspective, we can assign each of these relationships a uniform impact value of 1.0, illustrating an equal exertion of support in these one-way interactions.

To better understand the stability and interdependency of this ecological network, we aim to construct a directed Laplacian matrix that reflects these habitat interconnections. Utilizing NetworkX software, could you guide us through the conversion of these ecological dynamics into a digital format suitable for computational analysis, represented as a directed Laplacian matrix?
### Response:
import networkx as nx
from networkx.linalg.laplacianmatrix import directed_laplacian_matrix

# Create a DiGraph
G = nx.DiGraph()
G.add_weighted_edges_from([(0, 1, 1.0), (1, 2, 1.0), (2, 0, 1.0)])

# Compute the directed Laplacian matrix
L = directed_laplacian_matrix(G, nodelist=[0, 1, 2], weight='weight', walk_type=None, alpha=0.9)

print(L)
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