## 2 Archaeological Setting
### Network Analysis  
In order to better understand the relationships between species and their use by humans, we have applied a bipartite network analytical approach. By formalizing the human-environmental interactions as a network, we can quickly extract quantitative measures and visualize the connections between species and use categories. Here, we specifically employ the use of bipartite networks--which have been used in ecology to model predator-prey relationships and mutualistic networks, such as pollination networks--and are a useful analytical tool for examining the connections among species [41; 42; 43; 44]. In bipartite networks, there are two different categories of nodes, and nodes within one category cannot be connected to one another; connections can only exist between categories. In the example of plant-pollinator networks, two flowers cannot directly connect, while pollinators cannot directly connect either. Only pollinators (such as hawk moths) and plants (such as flowers) can directly connect. Our analyses therefore follow precedent in ecology, though we employ bipartite network analysis in a novel way. Specifically, we imagine that the two possible columns in the network are the species themselves as one column, and the uses that humans establish for species are the other. The uses cannot directly connect, nor can the species directly connect, but it is through the intervention of humans that these are connected. The network analysis was run in Cytoscape v. 3.7.2, employing the CentiScaPe package [45], to focus our metrics on centrality measures using the list of species and their use categories (see Supplementary Materials S2). Each species and each use category thus becomes a node in the network, but links (edges) are only allowed between species and use categories. The program was then used to visualize the results and analyze the following metrics: degree, betweenness centrality, bridging centrality, eccentricity, and eigenvector centrality. These all have specific characteristics that are shortly explained here. 1. **Degree** measures the number of direct connections between the nodes, so in this case, it shows the number of use categories per species and vice versa. 2. **Betweenness centrality** is used to measure how essential a node is to the network. In order to 'travel' through the network, certain nodes will be passed more often than others. In contrast to social and geographical networks, where betweenness centrality is often used to identify the persons or places that are in control of (parts of) the network, in our case betweenness centrality can be seen as a measure of how crucial a species is for connecting use categories and vice versa. 3. **Bridging centrality**[46] combines degree and betweenness centrality: what nodes are highly connected, as well as crucial for connecting the network? Potentially, these are the nodes that connect different clusters in the network. 4. **Eccentricity** will measure for each node the path distance to the node that is farthest away. A high eccentricity value for a node implies that the farthest node in the network is relatively near, so a node with a high eccentricity is one that is relatively central to the network. In our case, it can be used to understand what species are relatively well connected to different use categories. 5. **Eigenvector centrality**, finally, can be used to understand the hierarchy of the network by identifying the most influential nodes. Nodes with high eigenvector centrality have a large number of connections, but are also connected to other nodes to a high degree. It should be pointed out that these network metrics are designed for the analysis of one-mode networks [47; 48]. It is therefore common practice to project bipartite (two-mode) networks to one-mode for analysis of one of the node types involved, which inevitably leads to loss of information. Furthermore, the metrics for projected networks then tend to overestimate the connectedness of the nodes [49].