Genetic recapture identifies long-distance breeding dispersal in Greater Sage-Grouse (<i>Centrocercus urophasianus</i>)

Cross, Todd B.; Naugle, David E.; Carlson, John C.; Schwartz, Michael K.

doi:10.1650/condor-16-178.1

Cited by 15 publications

(25 citation statements)

References 52 publications

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“…From genetic recapture methods and clustering of genetic diversity, 15 km was identified as the median breeding dispersal distance among leks for both sexes (Cross et al. ) and therefore supported our decision (based on GPS data) to use 15 km for both Nevada and Wyoming. Using a smaller threshold distance for sage‐grouse would have resulted in many small clusters (e.g., <15 leks/polygon) with insufficient sample sizes to support population trend analyses at finer cluster scales.…”

Section: Discussionsupporting

confidence: 62%

“…, Cross et al. ), migratory movements with stopovers have been observed near 20 km (Dunn and Braun , Connelly et al. ) and up to ~240 km (Cross et al.…”

Section: Discussionmentioning

confidence: 99%

“…For example, individuals will disperse through lowquality seasonal habitat, enabling gene flow, but they will not use the habitat as a resource (Dickson et al 2005, Chetkiewicz and Boyce 2009, Braunisch and Suchant 2010, Wasserman et al 2010, Reding et al 2013. Genetic clustering and the evaluation of genetic structure/dispersal can support the development of a hierarchical monitoring framework by understanding species' dispersal distances (Cross et al 2017). However, the selection of analytical methods and genetic markers used when sampling genotypes is dependent on the spatial genetic structure, the duration and state of the landscape features, and the species' life history (Anderson et al 2010, Clark et al 2010.…”

Section: Advantages Of the Hierarchical Clusters Over Previously Devementioning

confidence: 99%

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Designing multi‐scale hierarchical monitoring frameworks for wildlife to support management: a sage‐grouse case study

et al. 2019

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Population monitoring is integral to the conservation and management of wildlife; yet, analyses of population demographic data rarely consider processes occurring across spatial scales, potentially limiting the effectiveness of adaptive management. Therefore, we developed a method to identify hierarchical levels of organization (i.e., populations) to define multiple spatial scales, specifically intended to help guide appropriate conservation and management actions. This approach can support mobile species with high site fidelity where surveys occur on birthing/breeding grounds or migratory stopovers. Our approach used a graph‐based clustering algorithm (Spatial K'luster Analysis by Tree Edge Removal) that explicitly included habitat selection information at multiple scales and further refined with constraint‐based rules. We applied these concepts to greater sage‐grouse leks (breeding grounds), a species of conservation concern, in two different ecological contexts (Nevada and Wyoming, USA). The constraint‐based rules accounted for inter‐lek movement distances based on literature and field studies in Nevada from 2012 to 2016, included methods to support a spatially balanced monitoring design, and identified barriers to movements among leks based on resistance surfaces. We evaluated the performance of our hierarchical clusters in Nevada using independent data from radio‐marked sage‐grouse, and we found the finest‐scaled cluster level captured ~90% of sage‐grouse movements and mid‐level scales captured ~97–99% of movements. We expected comparable performance for Wyoming, where we lacked radio‐marked sage‐grouse for an evaluation, because genetic studies estimate similar dispersal distances to our ~15 km inter‐lek movement distance in Nevada. For sage‐grouse and other mobile species with high site fidelity, our approach to defining these frameworks could prove valuable for conservation and management applications, such as improving estimation of scale‐dependent population trends and guiding the prescription of management actions at spatial scales that align with identified threats. Specific to sage‐grouse, our analysis sets the stage for designing a monitoring framework that relies on comparison of short‐ and long‐term population trends across our defined spatial scales and identifies and disentangles factors driving local (e.g., habitat quality) and regional (e.g., climate) population changes, thereby supporting scale‐dependent management and research needs for adaptive management practices.

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Section: Discussionsupporting

confidence: 62%

“…, Cross et al. ), migratory movements with stopovers have been observed near 20 km (Dunn and Braun , Connelly et al. ) and up to ~240 km (Cross et al.…”

Section: Discussionmentioning

confidence: 99%

Section: Advantages Of the Hierarchical Clusters Over Previously Devementioning

confidence: 99%

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Designing multi‐scale hierarchical monitoring frameworks for wildlife to support management: a sage‐grouse case study

et al. 2019

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“…Variation in the importance of a predictor can be related to its abundance and distribution, but this does not seem to be the case here, as mean values were similar across all zones. In MZ I, where individuals have been shown to move far distances (Cross et al., 2017; Newton et al., 2017), isolation by distance appeared to drive differentiation. None of the landscape predictors were very strong in MZ III, a zonal boundary spanning multiple populations and/or habitat–population relationships.…”

Section: Discussionmentioning

confidence: 99%

“…Sage-grouse have clustered distributions (Doherty et al, 2016), particularly during the breeding season when individuals attend centralized breeding leks where the majority of our samples were collected. Additionally, sage-grouse can have substantial withinand interseasonal movement distances (Cross, Naugle, Carlson, & Schwartz, 2017;Fedy et al, 2012), so we expected little differentiation between spatially proximate leks and individual samples (Row et al, 2015). Thus, we used a clustered (i.e., "group-based") approach to evaluate genetic differentiation as this likely best represents the ecology of the species at the spatial scale of this study and the sampling scheme (i.e., multiple samples from each lek).…”

Section: Genetic Differentiation Within Management Zonesmentioning

confidence: 99%

Quantifying functional connectivity: The role of breeding habitat, abundance, and landscape features on range‐wide gene flow in sage‐grouse

Row

Doherty

Cross

et al. 2018

Evolutionary Applications

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Functional connectivity, quantified using landscape genetics, can inform conservation through the identification of factors linking genetic structure to landscape mechanisms. We used breeding habitat metrics, landscape attributes, and indices of grouse abundance, to compare fit between structural connectivity and genetic differentiation within five long‐established Sage‐Grouse Management Zones (MZ) I‐V using microsatellite genotypes from 6,844 greater sage‐grouse (Centrocercus urophasianus) collected across their 10.7 million‐km2 range. We estimated structural connectivity using a circuit theory‐based approach where we built resistance surfaces using thresholds dividing the landscape into “habitat” and “nonhabitat” and nodes were clusters of sage‐grouse leks (where feather samples were collected using noninvasive techniques). As hypothesized, MZ‐specific habitat metrics were the best predictors of differentiation. To our surprise, inclusion of grouse abundance‐corrected indices did not greatly improve model fit in most MZs. Functional connectivity of breeding habitat was reduced when probability of lek occurrence dropped below 0.25 (MZs I, IV) and 0.5 (II), thresholds lower than those previously identified as required for the formation of breeding leks, which suggests that individuals are willing to travel through undesirable habitat. The individual MZ landscape results suggested terrain roughness and steepness shaped functional connectivity across all MZs. Across respective MZs, sagebrush availability (<10%–30%; II, IV, V), tree canopy cover (>10%; I, II, IV), and cultivation (>25%; I, II, IV, V) each reduced movement beyond their respective thresholds. Model validations confirmed variation in predictive ability across MZs with top resistance surfaces better predicting gene flow than geographic distance alone, especially in cases of low and high differentiation among lek groups. The resultant resistance maps we produced spatially depict the strength and redundancy of range‐wide gene flow and can help direct conservation actions to maintain and restore functional connectivity for sage‐grouse.

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The genetic network of greater sage‐grouse: Range‐wide identification of keystone hubs of connectivity

Cross

Schwartz

Naugle

et al. 2018

Ecology and Evolution

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Genetic networks can characterize complex genetic relationships among groups of individuals, which can be used to rank nodes most important to the overall connectivity of the system. Ranking allows scarce resources to be guided toward nodes integral to connectivity. The greater sage‐grouse (Centrocercus urophasianus) is a species of conservation concern that breeds on spatially discrete leks that must remain connected by genetic exchange for population persistence. We genotyped 5,950 individuals from 1,200 greater sage‐grouse leks distributed across the entire species’ geographic range. We found a small‐world network composed of 458 nodes connected by 14,481 edges. This network was composed of hubs—that is, nodes facilitating gene flow across the network—and spokes—that is, nodes where connectivity is served by hubs. It is within these hubs that the greatest genetic diversity was housed. Using indices of network centrality, we identified hub nodes of greatest conservation importance. We also identified keystone nodes with elevated centrality despite low local population size. Hub and keystone nodes were found across the entire species’ contiguous range, although nodes with elevated importance to network‐wide connectivity were found more central: especially in northeastern, central, and southwestern Wyoming and eastern Idaho. Nodes among which genes are most readily exchanged were mostly located in Montana and northern Wyoming, as well as Utah and eastern Nevada. The loss of hub or keystone nodes could lead to the disintegration of the network into smaller, isolated subnetworks. Protecting both hub nodes and keystone nodes will conserve genetic diversity and should maintain network connections to ensure a resilient and viable population over time. Our analysis shows that network models can be used to model gene flow, offering insights into its pattern and process, with application to prioritizing landscapes for conservation.

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Genetic recapture identifies long-distance breeding dispersal in Greater Sage-Grouse (Centrocercus urophasianus)

Cited by 15 publications

References 52 publications

Designing multi‐scale hierarchical monitoring frameworks for wildlife to support management: a sage‐grouse case study

Designing multi‐scale hierarchical monitoring frameworks for wildlife to support management: a sage‐grouse case study

Quantifying functional connectivity: The role of breeding habitat, abundance, and landscape features on range‐wide gene flow in sage‐grouse

The genetic network of greater sage‐grouse: Range‐wide identification of keystone hubs of connectivity

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