<｜begin▁of▁sentence｜>### Instruction:
Imagine we're examining a novel microbial ecosystem, where various microorganisms are interacting in a hierarchical fashion. The ecosystem can be represented as a directed graph, mirroring the flow of influence or nutrient transfer among the species. The connections in this ecosystem are akin to the edges in our graph, which include interactions such as (0, 1), (0, 2), (1, 3), (1, 4), (2, 5), where the organism represented by '0' is at the top of this particular hierarchy.

In order to study the propagation of a substance or signal through this microbial network starting from our keystone species '0', we'd like to map out a simplified model of these interactions, akin to a Breadth-First Search (BFS) tree emanating from our origin species. Using NetworkX within our data analysis tools, we could generate and visualize this BFS tree. Could you assist us in identifying the succession of interactions in this tree, which essentially are the direct connections between organisms as the signal spreads through the network? This would enable us to better understand the immediate relationships and potential pathways of influence or nutrient flow within the ecosystem.
### Response:
import networkx as nx

# Create a directed graph
G = nx.DiGraph()
G.add_edges_from([(0, 1), (0, 2), (1, 3), (1, 4), (2, 5)])

# Generate a BFS tree starting from source node 0
T = nx.bfs_tree(G, source=0)

# Output the edges of the BFS tree
print(list(T.edges()))
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