Setting spatial conservation priorities despite incomplete data for characterizing metapopulations

Abstract

Management of spatially structured species poses unique challenges. Despite a strong theoretical foundation, practitioners rarely have sufficient empirical data to evaluate how populations interact. Rather, assumptions about connectivity and source-sink dynamics are often based on incomplete, extrapolated, or modeled data, if such interactions are even considered at all. Therefore, it has been difficult to evaluate whether spatially structured species are meeting conservation goals. We evaluated how estimated metapopulation structure responded to estimates of population sizes and dispersal probabilities and to the set of populations included. We then compared outcomes of alternative management strategies that target conservation of metapopulation processes. We illustrated these concepts for Chinook salmon (Oncorhynchus tshawytscha) in the Snake River, USA. Our description of spatial structure for this metapopulation was consistent with previous characterizations. We found substantial differences in estimated metapopulation structure when we had incomplete information about all populations and when we used different sources of data (three empirical, two modeled) to estimate dispersal, whereas responses to population size estimates were more consistent. Together, these findings suggest that monitoring efforts should target all populations occasionally and populations that play key roles frequently and that multiple types of data should be collected when feasible. When empirical data are incomplete or of uneven quality, analyses using estimates produced from an ensemble of available datasets can help conservation planners and managers weigh near-term options. Doing so, we found trade-offs in connectivity and source dominance in metapopulation-level responses to alternative management strategies that suggest which types of approaches may be inherently less risky.

Not just a migration problem: Metapopulations, habitat shifts, and gene flow are also important for fishway science and management

“…The latter could be estimated from stable isotope or genetic analyses, or predicted using existing fish dispersal models (Radinger, Kail, & Wolter, ; see below) if no direct empirical assessment is possible. Alternatively, modelling may be exploratory, for example, focusing on the uncertainty of patch‐level dispersal probabilities in order to assess the sensitivity of predictions (Fullerton et al, ). Graph theoretical models do not give explicit information on subpopulation viability, although more isolated subpopulations (i.e., with lower I ) are expected to be at higher risk of local extinction, particularly if they are already small (Figure b).…”

Stray Rates of Natural‐Origin Chinook Salmon and Steelhead in the Upper Columbia River Watershed

“…Surprisingly few estimates of straying by natural‐origin Chinook Salmon Oncorhynchus tshawytscha and steelhead have been published despite their importance for understanding the metapopulation dynamics of these fish and their potential utility for informing expectations about the stray rates of hatchery‐origin salmon and steelhead (Quinn ; Keefer and Caudill ; Fullerton et al. ). Dispersal rate was found to be very important in the metapopulation structure of modeled Chinook Salmon populations in the Snake River basin (Fullerton et al.…”

“…Dispersal rate was found to be very important in the metapopulation structure of modeled Chinook Salmon populations in the Snake River basin (Fullerton et al. ); however, the authors acknowledged that they had few empirical data with which to estimate dispersal rates among populations. Due to the difficulty in capturing, tagging, and recapturing sufficient numbers of wild juveniles, studies on the stray rates of natural‐origin fish are lacking.…”

Scaling fish metacommunities in stream networks: synthesis and future research avenues

“…Such theoretical metacommunity models (Fig. 5) may provide the template for future empirical research, similarly to former metapopulation models (Schlosser and Angermeier 1995), which, although generated intense scientific debate (see e.g., Rieman and Dunham 2000, Schtickzelle and Quinn 2007, Falke and Fausch 2010, contributed largely to understanding the spatial distribution of fish populations in stream networks (Fullerton et al 2011, Fullerton et al 2016. In fact, the different approaches may effectively complement each other for understanding the organization of metacommunities in spatially heterogeneous stream habitats.…”

“…This approach could be adopted to stream networks as well. However, more long-term data on the population dynamics and movement patterns of stream fish in more complex networks (for an approach see Fullerton et al 2016) would be essential to parameterize models for metacommunity dynamics (Jacobsen and Peres-Neto 2010, Erős and Campbell-Grant 2015). A combination of spatial occupancy data with more detailed information on dispersal and demography of community constituting species (for an excellent study, see Falke et al 2015) could help to distinguish between metacommunity types in different landscapes (see Fig.5) and to determine critical scales of metacommunity dynamics.…”