Knowledge of ecological responses to changes in the environment is vital to design appropriate measures for conserving biodiversity. Experimental studies are the standard to identify ecological cause-effect relationships, but their results do not necessarily translate to field situations. Deriving ecological cause-effect relationships from observational field data is, however, challenging due to potential confounding influences of unmeasured variables. Here, we present a causal discovery algorithm designed to reveal ecological relationships in rivers and streams from observational data. Our algorithm (a) takes into account the spatial structure of the river network, (b) reveals the complete network of ecological relationships, and (c) shows the directions of these relationships. We apply our algorithm to data collected in the US state of Ohio to better understand causes of reductions in fish and invertebrate community integrity. We found that nitrogen is a key variable underlying fish and invertebrate community integrity in Ohio, likely negatively impacting both. We also found that fish and community integrity are each linked to one physical habitat quality variable. Our algorithm further revealed a split between physical habitat quality and water quality variables, indicating that causal relations between these groups of variables are likely absent. Our approach is able to reveal networks of ecological relationships in rivers and streams based on observational data, without the need to formulate a priori hypotheses. This is an asset particularly for diagnostic assessments of the ecological state and potential causes of biodiversity impairment in rivers and streams.

This dataset covers the ecological integrity of fish and invertebrate assemblages in rivers in Ohio, based on a dataset covering measurements at 1,826 biomonitoring sites sampled between 2000 and 2007. The integrity of the fish community is captured by the Index of Biotic Integrity (IBI). The IBI is constructed by comparing the fish community at a given site to an undisturbed reference community, located in a river of similar size and in a similar region. Reference communities are obtained from sites with minimal human influence based on expert judgment. The IBI is composed of 12 sub-metrics indicative of various aspects of community integrity, including the total number of species, number of individuals, and the proportion of top predators, with each sub-metric getting a score of 1, 3, or 5. A low score represents a high deviation from the reference site (Fausch et al., 1984). Similarly, the Invertebrate Community Index (ICI) combines 10 sub-metrics including the total number of taxa and the percentage of tolerant organisms in comparison to a reference community. For the ICI, the possible scores for each sub-metric are 0, 2, 4, or 6. The IBI ranges from 12 to 60 whereas the ICI ranges from 0 to 60.

Fausch, K. D., Karr, J. R., and Yant, P. R. (1984). Regional application of an index of biotic integrity based on stream fish communities. Trans. Am. Fish. Soc. 113, 39–55. doi: 10.1577/1548-86591984113<39:RAOAIO<2.0.CO;2

Missing values indicate missing measurements. Variable names are explained in the readme file and in Mielke et al. (2021). The dataset is further described by Zijp et al. (2017).

Mielke, K. P. , Schipper, A.M., Heskes, T., Zijp, M.C., Posthuma, L., Huijbregts, M.A.J., and Claassen, T. (2021). Discovering Ecological Relationships in Flowing Freshwater Ecosystems. Front. Ecol. Evol. 9:782554. doi: 10.3389/fevo.2021.782554

Zijp, M. C., Huijbregts, M. A. J., Schipper, A. M., Mulder, C., and Posthuma, L. (2017). Identification and ranking of environmental threats with ecosystem vulnerability distributions. Sci. Rep. 7:9298. doi: 10.1038/s41598-017-09573-8