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Impact of Species-Specific Dispersal and Regional Stochasticity On Landscape Ecol (2012) 27:405–416 DOI 10.1007/s10980-011-9683-2 RESEARCH ARTICLE Impact of species-specific dispersal and regional stochasticity on estimates of population viability in stream metapopulations Mark S. Poos • Donald A. Jackson Received: 7 June 2011 / Accepted: 3 November 2011 / Published online: 13 November 2011 Ó Springer Science+Business Media B.V. 2011 Abstract Species dispersal is a central component of decay models provided qualitatively similar rankings metapopulation models. Spatially realistic metapopu- of viable patches; however, there were differences of lation models, such as stochastic patch-occupancy several orders of magnitude in the estimated intrinsic models (SPOMs), quantify species dispersal using mean times to extinction, from 24 and 148 years to estimates of colonization potential based on inter- 362 and [100,000 years, depending on the popula- patch distance (distance decay model). In this study tion. We also found that the rate of regional stochas- we compare the parameterization of SPOMs with icity had a dramatic impact for the estimate of species dispersal and patch dynamics quantified directly from viability, and in one case altered the trajectory of our empirical data. For this purpose we monitored two metapopulation from viable to non-viable. The diver- metapopulations of an endangered minnow, redside gent estimates in time to extinction times were likely dace (Clinostomus elongatus), using mark-recapture due to a combination species-specific behavior, the techniques across 43 patches, re-sampled across a dendritic nature of stream metapopulations, and the 1 year period. More than 2,000 fish were marked with rate of regional stochasticity. We demonstrate the visible implant elastomer tags coded for patch location importance of developing comparative analyses using and dispersal and patch dynamics were monitored. We species- and patch-specific data when determining found that species-specific dispersal and distance quantitative estimates for mean time to extinction, which in the case of redside dace, were highly sensitive to different estimates of dispersal. Electronic supplementary material The online version of this article (doi:10.1007/s10980-011-9683-2) contains Keywords Metapopulations Á Dispersal Á Population supplementary material, which is available to authorized users. viability analysis Á Stochastic patch-occupancy M. S. Poos (&) Á D. A. Jackson models Á Parameter estimates Department of Ecology and Evolutionary Biology, University of Toronto, 25 Harbord Street, Toronto, ON M5S 3G5, Canada Introduction e-mail: [email protected]; [email protected] Species dispersal is a central component in the study of Present Address: spatially structured populations. At a landscape scale, M. S. Poos population viability strongly depends on individual Great Lakes Laboratory for Fisheries and Aquatic Sciences Fisheries and Oceans Canada, 867 Lakeshore dispersal allowing re-colonisation of empty habitats or Road, Burlington, ON L7R 4A6, Canada patches (Hanski 1999). For this reason, species 123 406 Landscape Ecol (2012) 27:405–416 dispersal is considered the ‘glue’ for maintaining local 2005). The easiest approach to describe colonization populations within a network of suitable habitats potential (i.e. patch accessibility) is as a function of (Hansson 1991). The degree of dispersal has an impact distance between a starting patch to a target patch and on local population dynamics, on gene flow and on the ability of species to disperse (Hanski 1994; Hanski adaptation to local conditions. For example, low et al. 1996; Heinz et al. 2005). This relationship can be dispersal can foster isolation and local adaptations quantified in several ways; however, most often this (Resetarits et al. 2005). Alternatively, high species estimation is done by assuming that colonization dispersal can have a stabilizing effect on metapopu- potential declines exponentially with distance (i.e. lation dynamics (Hanski 1999). distance decay; Hanski 1994; Vos et al. 2001; Frank Many species with spatially structured populations and Wissel 2002). It is uncertain how well the are in decline, and population viability models provide assumption of distance decay can model species- a statistical evaluation of species viability to inform specific dispersal (Heinz et al. 2005). Whether simple management decisions (Frank and Wissel 1998; formulae are adequate in describing species- and Akc¸akaya 2000). Metapopulation viability analyses patch-specific movement in metapopulation models provide a spatially realistic evaluation of the local remains an open question (Heinz et al. 2005; Marsh population structure (Hanski 1999; March 2008). By 2008). quantifying patch dynamics, metapopulation viability The objective of this study is to assess the impact of analyses can be used to better understand the impor- species-specific dispersal on estimates of metapopu- tance of ecological processes such as species specific lation viability. For our assessment, we conducted a dispersal, patch quality and landscape influences detailed mark-recapture survey of metapopulation (Moilanen and Hanski 1998), and to inform conser- dynamics of the endangered fish, redside dace vation management through evaluation of minimum (Clinostomus elongatus), in the Greater Toronto Area, amount of habitat or population size needed to Ontario, Canada. Redside dace are habitat specialists maintain viability (Hanski 1999; Robert 2009). preferring headwater pool habitats (COSEWIC 2007). Understanding how species-specific dispersal has Prior to our study, there were no data available for the potential to alter metapopulation viability can help dispersal of redside dace; however, it was commonly inform management decisions. One popular type of believed that due to their habitat preference that metapopulation viability analysis are stochastic patch- movement of redside dace would be highly restricted occupancy models (i.e. SPOMs), which have been (COSEWIC 2007). Previous observations of move- used extensively to model the viability of spatially ments by Koster (1939) suggest redside dace disperse structured populations (Hanski 1999; Moilanen 1999). to neighboring pools, and to nearby riffles for SPOMs are comparable to other spatially realistic spawning. Such observations are in agreement with models (Kindvall 2000; Ovaskainen and Hanski congener species such as the closely related rosyside 2004), and have been used in studies of species with dace (C. funduloides), where dispersal was shown to conservation concern, such as capercaillie (Grimm be limited to between 10–20 m (Hill and Grossman and Storch 2000), the American pika and Glanville 1987). fritillary and silver spotted skipper butterflies (Hanski 1999). As SPOMs provide a simplification over traditional population-viability analyses (Akc¸akaya Methods and Sjo¨gren-Gulve 2000), they do not require demo- graphic or stage data, but only occupancy, coloniza- We studied the metapopulation dynamics of redside tion and extinction rates, which can be easily dace by monitoring dispersal of tagged individuals on estimated from empirical data (Hanski 1994; Hanski monthly intervals during a one-year period. For this 1999; Moilanen 1999; Grimm et al. 2004; Moilanen study, each location was sub-divided into two areas: 2004). intensively monitored sites where individuals were The influence of dispersal on metapopulation tagged and extended sites which were beyond those viability is often analyzed using some approximation areas, which were monitored for tagged fish (Fig. 1). of colonization potential (Verboom et al. 1993; Hanski As meta-populations can be defined in a number of and Gilpin 1997; Frank and Wissel 2002; Heinz et al. ways (Hanski 1999), we define a metapopulation as an 123 Landscape Ecol (2012) 27:405–416 407 Fig. 1 Study sites on B23 Rouge River, Ontario where (A) (A) B22 L20 B20 redside dace (Clinostomus B17 B21 elongatus) dispersal and B16 L18 B15 B18 B19 patch dynamics were L19 L16 L15 B14 B13 monitored. Study locations: L17 L14 B12 L13 (A) Leslie Tributary, and L11 B11 L12 B8 B10 (B) Berczy Creek, were sub- L10 B7 B9 divided into extensive sites B4 (black), where redside dace L9 B6 L8 B5 B3 were tagged with a color- L7 B2 coded visual implant L6 B1 L5 elastomer tag, and extended L4 sites (grey), which were L3 L2 monitored for tag movement. Bottom right L1 Rouge River, Ontario is shown relative to the Great ON, Canada Lakes, and northeastern United States NY, U.S.A. assemblage of local populations inhabiting spatially sampling events conducted at each site per time distinct habitat patches (Moilanen and Hanski 1998). period. At each pool, redside dace were implanted Redside dace live primarily in clear, well-defined with visual implant elastomer (VIE) tags color coded pools (COSEWIC 2007); therefore each spatially for their location (Plates 1, 2). Elastomer tags were distinct pool segregated by a well-defined riffle (e.g. chosen because they had good tag retention and a passable, but natural migratory barrier) was selected negligible effects on survival, growth and behavior as a habitat patch. when used on other species (Walsh and Winkelman We studied metapopulation dynamic of redside 2004). Tags were injected subcutaneously near the dace at two locations on the Rouge River, including anal fin on the ventral surface (Plates 1, 2). All redside one location on Leslie Tributary, and the other dace were held in well-oxygenated flow-through bins location on Berczy Creek (Fig. 1). We choose these for 2–4 h to monitor for potential physiological
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