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Read the following paper: Epps et al. 2018Complete a single annotated bibliography entry for this paper, as described in the Research Assignment instructions. Be sure to follow the example given and look at the rubric. Instead of relating this to your topic statement, describe how this paper relates to lecture and readings from this week. You only need to include the citation, keywords, and annotation, not a topic statement.In addition to the annotation, include:one thing you found interesting or surprisingone question you have about the paper. This could be about methods, the study system, etc.Here are the Example:Vestigial wings and the evolution of flightlessness in Galapagos Island cormorants (Phalacrocorax harrisi)Keywords: Vestigiality, flightlessness, Galapagos Island cormorantsMcNab, B.K., 1994. Energy conservation and the evolution of flightlessness in birds. The American Naturalist, 144(4), pp.628-642. (Links to an external site.)Links to an external site.The author examined the hypothesis that energy conservation contributes to the evolution of flightlessness in birds by comparing the factors correlated with basal metabolic rate in flighted and flightless rails and ducks. They found support for their hypothesis in flightless birds, except penguins, which use their wings for locomotion in water. This paper suggests that a lack of selective pressure for flight (lack of predators) combined with a high metabolic demand to maintain flight capability, contributes to vestigiality (reduction of wings and pectoral muscles) in environments where resources are limited.This week material will be :Describe how slight differences in fitness can change the frequencies of alleles within a population over timeExplain why natural selection cannot drive dominant alleles to fixation within a populationExplain how selection can act to either reduce or maintain allelic variation within a populationInterpret graphs representing genetic distance among populationskeywords: dispersal, genetic monitoring, habitat fragmentation, roadsCitation: Epps, C.W., Crowhurst, R.S., Nickerson, B.S.,2018. Assessing changes in functional connectivity in a desert bighorn sheep metapopulation after two generations. Mol Ecol, 27: 2334– 2346. check the citation.

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Received: 29 August 2017
Revised: 25 March 2018
Accepted: 26 March 2018
DOI: 10.1111/mec.14586
Assessing changes in functional connectivity in a desert
bighorn sheep metapopulation after two generations
Clinton W. Epps
| Rachel S. Crowhurst | Brandon S. Nickerson
Department of Fisheries and Wildlife,
Oregon State University, Corvallis, Oregon
Clinton W. Epps, Department of Fisheries
and Wildlife, Oregon State University,
Corvallis, OR.
Email: [email protected]
Determining how species move across complex and fragmented landscapes and interact with human-made barriers is a major research focus in conservation. Studies estimating functional connectivity from movement, dispersal or gene flow usually rely on
a single study period and rarely consider variation over time. We contrasted genetic
structure and gene flow across barriers for a metapopulation of desert bighorn sheep
Present address
Brandon S. Nickerson, Swinomish Indian
Tribal Community, La Conner, Washington.
Funding information
U.S. Geological Survey; National Park
Service; California Department of Fish and
Wildlife; California Chapter of The Wild
Sheep Foundation; Community Foundation;
Oregon State University; National Science
(Ovis canadensis nelsoni) using genotypes collected 2000–2003 and 2013–2015.
Based on the recently observed but unexpected spread of a respiratory pathogen
across an interstate highway previously identified as a barrier to gene flow, we
hypothesized that bighorn sheep changed how they interacted with that barrier, and
that shifts in metapopulation structure influenced gene flow, genetic diversity and
connectivity. Population assignment tests, genetic structure and genetic recapture
demonstrated that bighorn sheep crossed the interstate highway in at least one location in 2013–2015, sharply reducing genetic structure between two populations, but
supported conclusions of an earlier study that such crossings were very infrequent or
unknown in 2000–2003. A recently expanded population established new links and
caused decreases in genetic structure among multiple populations. Genetic diversity
showed only slight increases in populations linked by new connections. Genetic
structure and assignments revealed other previously undetected changes in movements and distribution, but much was consistent. Thus, we observed changes in both
structural and functional connectivity over just two generations, but only in specific
locations. Movement patterns of species should be revisited periodically to enable
informed management, particularly in dynamic and fragmented systems.
dispersal, genetic monitoring, habitat fragmentation, roads
2010). In combination with assessments of structural connectivity,
determining how species interact with barriers and move across frag-
Determining functional connectivity, or how species move through
mented landscapes has improved the ability to mitigate the impact
landscapes (Rudnick et al., 2012), has been a major focus in land-
of such landscape features on wildlife (Clevenger & Waltho, 2005).
scape ecology (Betts, Gutzwiller, Smith, Robinson, & Hadley, 2015)
To investigate whether, and where, individuals cross barriers or
and landscape genetics (Manel & Holderegger, 2013). Empirical esti-
human-modified habitats, researchers have employed radiotelemetry,
mates of functional connectivity are vital for effective management
GPS collars generating high-resolution spatial data, behavioural
of species in the face of habitat fragmentation and climate change
experiments (Moriarty et al., 2015) and remote cameras at potential
(Creech, Epps, Monello, & Wehausen, 2014; Knowlton & Graham,
crossing points (Gagnon, Dodd, Ogren, & Schweinsburg, 2011).
© 2018 John Wiley & Sons Ltd
Molecular Ecology. 2018;27:2334–2346.
Landscape or population genetic approaches are also widely used for
sheep generations (assuming 6 years/generation, Coltman et al.,
inferring functional connectivity, particularly where species are small-
2003) after the 2000–2003 study, an outbreak of respiratory disease
bodied and difficult to monitor with telemetry (Spear & Storfer,
associated with the respiratory pathogen Mycoplasma ovipneumoniae
2008), dispersal or long-distance movements are thought to be rare
(Besser et al., 2008), hereafter M. ovi, was detected in the Old Dad
(Davis, Murray, Fitzpatrick, Brown, & Paxton, 2010) or studies
Peak population in the central Mojave Desert. Several months later,
encompass large landscapes (Cushman, McKelvey, Hayden, &
the same strain was detected south of Interstate 40 in the Marble
Schwartz, 2006; Epps, Wehausen, Bleich, Torres, & Brashares, 2007).
Mountains (T. Besser, Washington State University, and California
Both GPS collar and landscape genetic data have served as the basis
Department of Fish and Wildlife [CDFW], unpublished data), sug-
for developing connectivity or movement models (Chetkiewicz &
gesting stepwise contact by bighorn sheep had occurred across
Boyce, 2009; Creech et al., 2014). Such models have proved funda-
intervening regions, including across the interstate. Avenues for such
mental for managing species on fragmented landscapes (Hilty,
crossing could include pushing through fencing and crossing at sur-
Lidicker, & Merenlender, 2006) and are preferred for predicting link-
face level, despite heavy traffic, or using washes bridged by the
ages among habitat patches (Rudnick et al., 2012).
interstates but also fenced and typically occurring on flatter ground
Studies aimed at understanding interactions with barriers or ani-
rarely used by bighorn sheep. While transmission of respiratory dis-
mal movement in general are, however, often based on a “snapshot”
ease can occur through contact with even a single individual (Besser
of patterns on a particular landscape over a few years. Movement
et al., 2014), this observation raised questions of considerable import
models based on direct observation of animal movements, as by
for conservation of these metapopulations. Specifically: (i) did big-
GPS telemetry, usually reflect 2–6 years of data (Kertson, Spencer,
horn sheep begin crossing barriers within the last two generations,
Marzluff, Hepinstall-Cymerman, & Grue, 2011). Genetic patterns
or alternately, (ii) did the spread of the disease indicate that previous
integrate movements over longer and variable timescales (Epps &
genetic analyses were unable to detect ongoing but occasional
Keyghobadi, 2015), but genetic investigations of the effects of barri-
movements across barriers? Additionally, how dynamic are estimates
ers or fragmented landscapes are almost always based on a single
of genetic structure and genetic diversity across time points?
estimate of genetic structure. The stability of patterns and processes
In this study, we contrast population genetic structure in a
inferred from any empirical movement analysis is rarely considered,
dynamic desert bighorn sheep metapopulation across two genera-
yet movement or dispersal behaviours themselves may vary over
tions. By sampling the same populations ~12 years apart with the
time due to changes in factors such as resource availability (Bowler
same genetic markers, we attempt to determine whether the interac-
& Benton, 2005, 2009), parasite load (Debeffe et al., 2014) or popu-
tion of this large mammal with anthropogenic barriers has changed,
lation density (Plumb, White, Coughenour, & Wallen, 2009). Thus,
evaluate the degree of change in genetic structure and genetic diver-
models generated in a particular place and time might not capture
sity across populations and infer sources of recently recolonized or
behaviours under different conditions or newly learned behaviours.
expanded populations. We hypothesized that changes in interpopula-
Although some studies compare models of movement and connec-
tion movement patterns of bighorn sheep have occurred since the
tivity derived from different types of data, very few studies appear
2000–2003 study, including new connections formed by expanding
to have examined changes in movements or movement behaviours
populations and crossing of anthropogenic barriers, leading to
on decadal timescales using the same type of data.
changes in both structural and functional connectivity in localized
Desert bighorn sheep (Ovis canadensis nelsoni) in the Mojave
portions of the study area. Specifically, we predicted that popula-
Desert of California are a case study of a species experiencing both
tions separated by Interstate 40 would show decreased genetic dif-
natural and anthropogenic habitat fragmentation. Bighorn sheep in
ferentiation in 2013–2015 compared to 2000–2003. We also
this region exist in metapopulations (Bleich, Wehausen, & Holl,
predicted that some individuals would be fully or partly assigned
1990; Schwartz, Bleich, & Holl, 1986), with local populations of
genetically to populations on the other side of the interstate barrier
<25–250 individuals that experience frequent extinction and colo- in 2013–2015, but not during 2000–2003, indicating that cross- nization events (Abella et al., 2011; Epps, McCullough, Wehausen, interstate movements were rarer or undetected at the earlier time, Bleich, & Rechel, 2004; Epps, Wehausen, Palsboll, & McCullough, and that pattern would be reflected in first-generation migrants as 2010). Populations occur in small, sometimes isolated mountain well. We further predicted that recently established populations in ranges separated by desert flats and bajadas (alluvial fans), as well as two locations would increase high gene flow linkages among popula- fenced interstate highways and other potential anthropogenic barri- tions. Finally, we consider the implications of this study for studies ers (Bleich, Wehausen, Ramey, & Rechel, 1996). Systematic investi- assessing functional connectivity at a single point in time. gation of population genetic structure from 2000 to 2003 and a review of known intermountain movements revealed that gene flow and thus movement of individuals between populations was strongly influenced by distance and topography, and that fenced interstate highways appeared to act as complete barriers (Epps et al., 2005, 2 | METHODS 2.1 | Study area 2007). Subsequent investigations have treated such barriers as This study took place in the southern Mojave and central Mojave impermeable (Creech et al., 2014). Yet, in 2013, roughly two bighorn Desert metapopulations of desert bighorn sheep (Torres, Bleich, & 2336 | EPPS ET AL. Wehausen, 1994) in southeastern California (Figure 1). Those popu- Dad Peak were translocated to the nearby North Bristol population lations were genetically sampled in 2000–2003 (hereafter, Time in 1992 to mitigate an apparent population extinction in the mid- Point 1 or TP1) by Epps et al. (2005), Epps, Palsboll, Wehausen, 20th century (Wehausen, 1999). However, by the time of the sam- Roderick, and McCullough (2006). In 2013–2015 (hereafter, Time pling at TP1, apparently only a few transient males remained (Epps, Point 2, or TP2), we resampled 13 populations in the core of the Bleich, Wehausen, & Torres, 2003). Epps et al. (2005, 2006) study area. This sampling area was centred on the recent respiratory disease outbreak first detected at Old Dad Peak in Mojave National Preserve in 2013 (CDFW, unpublished 2.2 | Genetic sampling data), as well as one apparently newly colonized population in the We used faecal samples as a primary source of DNA in TP2, collected South Soda Mountains (Figure 1; Abella et al., 2011). Populations in by visiting water sources during summer months when bighorn sheep the resurvey spanned a gradient of genetic diversity and isolation at are dependent on water and collecting opportunistically at other TP1 (Epps et al., 2005). Interstate 40, a four-lane divided highway times of the year. We sampled at the same locations as in Epps et al. fenced on both sides, separated four southern populations from the (2005) and collected faecal samples up to several weeks in age; if remainder of the bighorn sheep populations considered in this study wet, samples were dried before storing at room temperature. We pro- (Figure 1). All populations in the study area were native (i.e., never cessed pellets and extracted DNA using a modified version of the augmented by translocation), except that bighorn sheep from Old AquaGenomic Stool and Soil protocol (Multitarget Pharmaceuticals F I G U R E 1 Desert bighorn sheep populations genetically sampled at two time points (2000–2003 and 2013–2015, white polygons) in the Mojave Desert of California, with other nearby populations drawn with black outlines, and shaded topographic relief. The South Soda Mountains population, an apparent recent colonization, was sampled only in 2013–2015. Interstate highways are depicted with dashed lines. Average assignments of individuals from desert bighorn sheep populations in 2000–2003 and 2013–2015 (k = 5) from Program STRUCTURE are shown colourcoded by proportional assignment to cluster by population (circles) and by individual (Granite Mountains [GR], where each vertical bar reflects an individual). In 2000–2003, no individuals bordering I-40 were assigned to populations on the opposite side, whereas in 2013–2015, five individuals in the Granite Mountains were at least 40% assigned to the populations south of I-40 (blue cluster). Individual assignments for all populations are presented in Figure S3. CL, Clipper Mountains; GR, Granite Mountains; HA, Hackberry Mountains; KD, Cady Mountains; MA, Marble Mountains; NB, North Bristol Mountains; NE, Newberry/Ord/Rodman Mountains; OD, Old Dad Peak/Marl/Kelso Mountains; OE, Indian Spring/Club Peak; PI, Piute Range; PR, Providence Range; SS, South Soda Mountains; WO, Wood Mountains. Polygons modified from Creech et al. (2014) EPPS | ET AL. 2337 LLC, Colorado Springs, CO; see details in Appendix S1). We also used recaptured in more than one population within a time point, for sub- DNA extracted from blood of 159 bighorn sheep captured as part sequent analyses, we used each genotype only in the population in of an ongoing demographic study (2013–2015). Capture protocols which it was first detected. Because desert bighorn sheep in this were approved by the National Park Service IACUC (ACUP area can live up to ~20 years (J. Wehausen, personal communication, #PWR_MOJA_Epps.Powers DesertBighorn_2013). Whole blood was November 21, 2016), we also tested for matching genotypes collected in EDTA tubes and spun at 4,0009g for 10 min to separate between the data sets from the two time points. We recorded any the buffy coat. In 16 cases, DNA was also obtained from ear tips such matches but retained matching genotypes in data sets for both removed from carcasses. We extracted DNA using a Qiagen DNeasy time points. Blood and Tissue Kit (Qiagen Inc, Valencia, CA, USA) and 30 mg of dried tissue or 200 ll of buffy coat. 2.4 | Assessing changes in genetic structure and detecting migrants 2.3 | Genotyping, markers, individual identification and marker evaluation To ascertain changes in connectivity, including whether bighorn sheep We used 16 variable microsatellite loci to characterize genetic diver- recapture (above), estimates of genetic structure, assignment tests and sity and genetic structure at both time points (Table S1; tests for first-generation migrants (i.e., F0, Paetkau, Slade, Burden, & Appendix S1). Samples at TP1 were genotyped by Epps et al. (2005, Estoup, 2004; hereafter referred to as migrants). For genetic structure, 10 loci) and Nickerson (2014, remaining 6 loci). We checked consis- after removing loci with evidence of selection at both time points tency of allele size identification for markers used at both time (Appendix S1), we used FSTAT (Goudet, 1995) to estimate pairwise points by rerunning 16 individuals (to provide a wide diversity of FST (Weir & Cockerham, 1984) between all populations at each time allele sizes) selected across 12 populations from TP1 under labora- point and estimated 95% confidence intervals by bootstrapping across moved across Interstate 40 at either time point, we used genetic tory conditions used in TP2 analyses, determining appropriate size loci for comparisons of interest. We subtracted pairwise FST values at corrections, and correcting allele sizes to match those in TP2. Reac- TP1 from those at TP2 (hereafter, ΔFST) to rank changes in genetic tion conditions and thermocycling profiles for PCR, genotyping structure among populations and compared high gene flow linkages methods, genotype matching and testing for Hardy–Weinberg equi- (FST ≤ 0.05, Epps et al., 2010) at both time points as an index of mean- librium and linkage disequilibrium are described in Appendix S1. ingful changes in patterns of connectivity. Further, we evaluated pair- Three of the microsatellite markers were linked to genes related to wise FST for each population to itself between time points to estimate immune system function in other bovids (BL4, associated with the inter- within-population genetic changes, using 1,000 permutations over loci (Schneider, Roessli, & Excoffier, 2000) to assess difference feron gamma gene involved in parasite resistance; Coltman, Wilson, in Pilkington, Stear, & Pemberton, 2001, TGLA387, linked to the MHC from zero. To further evaluate potential error in FST estimates resulting gene complex; Maddox et al., 2001, and TCRBV62, linked to genes for from variation in sample size, we selected three populations represent- T-cell receptors; Buitkamp, Schwaiger, & Epplen, 1993), but have also ing a gradient of low to high genetic structure and randomly subsam- been employed as neutral microsatellite markers in systems where they pled individuals over a range of sample sizes, estimating pairwise FST exhibited no evidence of selection (Johnson, Mills, Wehausen, Stephen- and generating 95% quantiles from 5,000 replicates at each sample son, & Luikart, 2011; Luikart et al., 2011). Therefore, we used size increment (see Figure S1 for full description). LOSITAN (Antao, Lopes, Lopes, Beja-Pereira, & Luikart, 2008; Beaumont & ARLEQUIN We used STRUCTURE (Pritchard, Stephens, & Donnelly, 2000) to Nichols, 1996) to test all microsatellites for positive and balancing selec- infer individual assignments at both time points in a single analysis tion within each time point. We conducted tests using both stepwise combining all data at both time steps, using all loci including any and infinite allele mutation models, using 1,000,000 iterations, approxi- under selection. We used this approach to reduce impact of varia- mated mean neutral FST by removing potential selected loci (Antao tion in sample sizes within populations across time steps. We exam- to select the subsample size for each ined individual assignments (q values for each individual to each test. We computed 99% confidence intervals for neutral expectations; cluster) within each time point to infer presence of migrants or off- loci falling outside those intervals were considered to be potentially spring of migrants among clusters, including across Interstate 40, influenced by natural selection (Luikart et al., 2011). Because markers after estimating assignments (detailed in Appendix S1). et al., 2008) and allowed LOSITAN under selection can enhance assignment of individuals to source popu- We used GENECLASS2 (Piry et al., 2004) to test for migrants among lations (Ogden & Linacre, 2015), all markers were retained for STRUCTURE all populations at each time point, including those separated by ... Purchase answer to see full attachment

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