4.4 Limitations and Recommendations for Future Research
While the approach developed in this manuscript successfully describes
spatiotemporal connectivity dynamics, there are several assumptions and
limitations that future work should consider. Connectivity strength
metrics used here enable the development of a continuous value between 0
and 1, however the meaning of the connectivity strength values are
dependent on the internal variation within a system and interpretation
may differ among systems. As such, we suggest that when identifying
thresholds in different connectivity states, future research should
consider a distribution of possible connectivity strength values (see
Figure 9). An additional limitation is that the empirical connectivity
functions we developed assume stationarity in the underlying floodplain
structure and fitting a single model assumes a lack of hysteresis in
functional connectivity between source and target sites between rising
and falling limbs. Our observations of hysteresis at two sites in 2018,
likely driven by beaver activity, suggests that such changes in
connectivity thresholds are likely relatively common. However, such
assumptions are equally present in all approaches reliant on static
physical datasets such single date LIDAR acquisition or field surveys
(Passalacqua et al., 2015). Therefore, we believe our approach is
valuable but for longer-term studies, but that conditions should be
monitored through time and relationships updated similar to how a rating
curve used to estimate discharge needs to be updated if the underlying
channel morphology is altered during the study period.
While our work demonstrates that aquatic microbiomes can be utilized for
inference into hydrologic connectivity, our ability to determine the
broad applicability of this technique is limited. This study was
conducted in a relatively small river-floodplain system with relatively
homogenous surrounding land cover. To apply the microbial connectivity
metric more widely, future work will need to assess how residence time
thresholds in different systems and at different scales interact with
microbial membership. Further, as investigations move to larger, more
heterogeneous landscape scales, it will be necessary to consider how to
incorporate more diverse sources of water and microbiomes into the
approach. To address this, we suggest that future work implement
finer-scale OTU-level analysis rather than a coarse community level
similarity metric, which we believe can increase the ability to detect
weaker flow paths and potentially be used as a multi-tracer to
simultaneously measure connectivity from multiple water sources.