Information

How much can you improve performance by selective breeding?


How much can you improve performance by selective breeding? I am curious of all kinds of performance, physical as well as cognitive, but I guess the latter is harder to answer so to make the question very concrete and measurable: how much have racing horses' and dogs' performance increased the last 100 years or so? They should be good examples of what happens over many generations of selective breeding aimed at improving one specific characteristic (running fast), no? And it should be easy to measure the progress: what was the world record on 1000 m (or something like that) for horses/dogs 1920 and what is it today?

Can these numbers be translated to humans?

Are the progress among horse/dog performance slowing down or increasing?

Any arguments for why/why not cognitive ability would progress in a similar fashion if selective breeding was used and aimed at that instead of physical ability?


Mapping genes that regulate trotting performance by selective sweep mapping in a unique Nordic horse model

The hybrid origin of the Coldblooded trotter provides a unique opportunity to identify genes influencing body composition and superior athletic performance. We hypothesize that we will be able to detect a gene flow that has occurred between Standardbreds and Coldblooded trotters due to limited cross breeding and a subsequent (very) strong selection for racing performance in the Coldblooded trotters.

The Coldblooded trotter originates from the North Swedish draught horse that is a draught horse used in farming and forestry. It was not until 1960 that the Coldblooded trotter was defined as a separate breed from the North Swedish draught horse. It is well established that some cross-breeding has occurred between Standardbreds and Coldblooded trotters before obligatory paternity testing was introduced in Sweden.

A remarkable improvement in racing performance of the Coldblooded trotter has occurred during the last sixty years. We assume that this improvement is partly explained by a marked increase of some favorable genetic variants originating from Standardbreds and introduced to the Coldblooded trotter by cross-breeding. This process should leave “genetic footprints” in the genome of Coldblooded trotters in the form of chromosome segments originating from Standardbreds. We currently compare the genetic makeup of North Swedish trotters (30 top-performing individuals), North Swedish draught horses (30 randomly selected individuals) and Standardbreds (30 top-performing individuals) by analysis of whole genome re-sequence data. To identify chromosome regions under selection, we will quantify the genetic variation between breeds using simple Fst statistics. For most chromosome segments we expect that the two former breeds should be most similar, reflecting their close relationship. However, for those chromosome segments harboring favorable genetic variants originating from Standardbreds we expect that the Coldblooded trotters should share a higher similarity with the Standardbreds. Chromosome regions under strong selection are likely to harbor genes affecting morphological and physiological traits important for performance in this model system. Genes that regulate energy metabolism and other biological processes that impact racing performance may also shed light on metabolic defects and diseases in other species.


Abstract

We use regression discontinuity design to examine the effect of a system of public exam high schools, which admit students solely by pre-existing achievement, on student college entrance exam scores in Beijing, China. More selective exam schools may have higher peer quality and sometimes are equipped with more experienced teachers and better facilities. We find, however, that elite exam high schools, which are the most selective, have no effects on student test scores. We find that on average the system of exam schools improves student performance on the exam, which indicates that students benefit from attending more selective non-elite schools. The results on qualifying for college admission are consistent with our findings about test scores. Differences among schools in peer achievement, student/teacher ratio and the percentage of certificated and experienced teachers partially explain our findings self-choices of track and exam participation do not explain test scores or college admission.


3 RESULTS

3.1 Age-related patterns in breeding performance

From our analysis including birds aged 3–26 years, threshold models best represented breeding success and breeding productivity for both male and female white-tailed eagles (See Supporting Information Tables S1 and S2). Although there was a limited support for a quadratic term to be relevant in predicting female breeding success (ΔAICc 0.34) and breeding productivity (ΔAICc 1.56), the threshold model was selected to enable further comparison between sexes (Table S1).

There was clear evidence that as eagles aged, breeding success increased until approximately 16 years of age in female eagles and until 19 years in males and subsequently declined (Figure 1, Supporting Information Table S3). The variance of the random term “female individual identity” ( = 1.04, 95% CI: 0.96–1.13) explained more among individual variance than the variance of the random term “male individual identity” ( = 0.34, 95% CI: 0.29–0.38), but in both cases, the inclusion of the individual identity as a random term did not alter the overall trends. For all males equal to and older than 23 years used in our analysis (breeding records n = 10, individuals n = 3), no young were produced. The same patterns were evident for breeding productivity, which increased until 14 years old in females and 19 years old in males and also levelled-out or declined in later life (Figure 1, Supporting Information Table S3). Again, individual identity did not change the overall trends and the random term accounted for limited variance in both female ( = 0.45, 95% CI: 0.0002–0.85) and male ( = 0.23, 95% CI: 0.0002–0.92) breeding productivity. These trends were not changed when tested on only eagles that did not change partner (Figure S1).

3.2 Improvement in breeding performance during early life

Eagles showed age-related improvement in both breeding success (Estimate = 0.19, z = 7.32, 95% CI = 0.14–0.25) and breeding productivity (Posterior mean = 0.15, 95% CI = 0.11–0.19) during early life (i.e., between the age of 3 and 16 years for females and between 3 and 19 years for males). Males and females show very similar levels of improvement in breeding performance with age, with little support for an interaction between age and sex for either breeding success or productivity (Table 1 and Supporting Information Table S4). In the same way, no effects of origin on breeding performance were detected in early life (Table 1).

Model structure df logLik AICc/DIC ΔAICc/ΔDIC ω i
(i) Breeding success
Early life (n = 949) age 5 −559.71 1,129.47 0.00 0.47
age+sex 6 −559.65 1,131.38 1.91 0.18
age+origin 7 −558.81 1,131.73 2.26 0.15
age*sex 7 −559.58 1,133.27 3.80 0.07
age+sex+origin 8 −558.64 1,133.44 3.97 0.07
age*sex+origin 9 −558.60 1,135.39 5.92 0.02
age*origin 9 −558.73 1,135.64 6.17 0.02
age*origin+sex 10 −558.57 1,137.37 7.90 0.01
age*sex+age*origin 11 −558.52 1,139.33 9.86 0.00
null 4 −586.21 1,180.47 51.00 0.00
sex 5 −586.21 1,182.49 53.02 0.00
origin 6 −585.71 1,183.51 54.04 0.00
sex+origin 7 −585.70 1,185.52 56.05 0.00
Late life (n = 160) age*sex 7 −81.38 177.50 0.00 0.92
age 5 −86.42 183.24 5.74 0.05
age+sex 6 −85.96 184.48 6.98 0.03
sex 5 −89.78 189.95 12.45 0.00
null 4 −91.13 190.51 13.01 0.00
Late life subset (n = 142) age*sex 6 −77.16 166.94 0.00 0.41
age*sex+age*origin 8 −75.01 167.10 0.16 0.38
age*sex+origin 7 −77.08 168.99 2.05 0.15
age 4 −82.12 172.53 5.59 0.03
age+sex 5 −82.05 174.54 7.60 0.01
age+origin 5 −82.08 174.59 7.65 0.01
age*origin 6 −81.31 175.23 8.29 0.01
age+sex+origin 6 −81.99 176.59 9.65 0.00
null 3 −85.35 176.87 9.93 0.00
age*origin+sex 7 −81.11 177.06 10.12 0.00
sex 4 −85.04 178.36 11.42 0.00
origin 4 −85.17 178.63 11.69 0.00
sex+origin 5 −84.79 180.01 13.07 0.00
(ii) Breeding productivity
Early life (n = 898) age 6 −569.32 1,472.28 0.00 0.97
age+sex+origin 9 −569.35 1,482.53 10.25 0.01
age*sex+origin 10 −569.14 1,482.76 10.48 0.01
age*sex 8 −569.39 1,482.92 10.64 0.01
age+sex 7 −569.21 1,483.86 11.58 0.00
age+origin 8 −569.67 1,484.02 11.74 0.00
age*sex+age*origin 12 −568.36 1,484.66 12.38 0.00
age*origin 10 −568.90 1,485.13 12.85 0.00
age*origin+sex 11 −568.66 1,485.25 12.97 0.00
null 5 −586.88 1,528.60 56.32 0.00
origin 7 −586.83 1,530.12 57.84 0.00
sex 6 −587.07 1,531.02 58.74 0.00
sex+origin 8 −586.98 1,532.20 59.92 0.00
Late life (n = 211) age*sex 8 −140.27 369.33 0.00 1.00
age+sex 7 −146.05 382.94 13.61 0.00
sex 6 −147.89 383.68 14.35 0.00
age 6 −146.11 383.85 14.52 0.00
null 5 −147.62 388.08 18.75 0.00
Late life subset (n = 181) age*sex 7 −120.02 311.63 0.00 0.77
age*sex+origin 8 −119.94 314.36 2.73 0.20
age*sex+age*origin 9 −119.95 317.53 5.90 0.04
age*origin+sex 8 −125.81 329.01 17.38 0.00
age 5 −126.04 330.09 18.46 0.00
age+origin+sex 7 −125.55 330.45 18.82 0.00
age+sex 6 −125.88 330.59 18.96 0.00
age+origin 6 −125.81 330.70 19.07 0.00
age*origin 7 −125.81 331.67 20.04 0.00
null 4 −127.73 332.11 20.48 0.00
origin 5 −127.23 334.17 22.54 0.00
origin+sex 6 −127.22 334.24 22.61 0.00
sex 5 −127.61 334.91 23.28 0.00

Notes

  • AICc: Akaike's information criterion with a correction for small sample size DIC: deviance information criterion ωi: model weight.
  • Variables include age (standardized), sex (male/female) and origin (release phase 1, release phase 2, wild-bred) in early- and late-life periods of white-tailed eagles. Origin in the late-life subset contains release phase 1 and wild-bred eagles only. All models include eagle ID, year and territory ID as random terms, except in the late-life subsets where territory ID is dropped as a random term due to complete convergence between individual ID and territory ID. “*” denotes an interaction which also includes the individual terms. Top models within <2 ΔAICc/DIC are shown in bold.

3.3 Within-individual effects and selective appearance

The improvement in breeding performance in female and male white-tailed eagles could be attributed to both within- and between-individual effects (Table 2). In females, for both measures, years since first breeding attempt (i.e., within-individual trends) had a higher variable importance (standardized regression coefficients success: 0.47, 95% CI: 0.20–0.74 productivity: 0.41, 95% CI: 0.20–0.64) than between-individual changes caused by selective appearance (standardized regression coefficients success: 0.39, 95% CI: 0.08–0.70 productivity: 0.29, 95% CI: 0.03–0.54). The improvement in breeding performance in male white-tailed eagles was also predominately determined by within-individual effects (standardized regression coefficients success: 0.83, 95% CI: 0.55–1.14 productivity: 0.57, 95% CI: 0.39–0.77) compared to between-individual effects (standardized regression coefficients success: 0.07, 95% CI: −0.30 to 0.45 productivity: −0.02, 95% CI: −0.32 to 0.25). There was no indication that age-specific breeding performance was affected by whether or not it was an individual's first breeding attempt for both of the sexes (Table 2).

Estimate z Confidence intervals
Lower Upper
(i) Breeding success
Female (n = 466) Intercept −1.73 −2.70 −2.98 −0.47
Years since first attempt 0.14 3.40 0.06 0.23
Age at first breeding 0.28 2.45 0.06 0.51
First attempt −0.45 −1.27 −1.15 0.25
Male (n = 503) Intercept −1.14 −1.58 −2.56 0.27
Years since first attempt 0.20 5.60 0.13 0.27
Age at first breeding 0.06 0.40 −0.22 0.34
First attempt −0.08 −0.21 −0.79 0.64
Posterior mean Effective sample size Confidence intervals
Lower Upper
(ii) Breeding productivity
Female (n = 395) Intercept −1.51 4,000 −2.59 −0.39
Years since first attempt 0.15 4,000 0.07 0.23
Age at first breeding 0.22 4,000 0.02 0.42
First attempt −0.31 4,000 −0.91 0.26
Male (n = 503) Intercept −0.57 4,000 −1.73 0.53
Years since first attempt 0.14 4,000 0.09 0.19
Age at first breeding −0.02 4,000 −0.24 0.20
First attempt −0.16 3,685 −0.70 0.40

Note

3.4 Senescence in breeding performance

The rate of decline in breeding success and productivity in late life (senescence) differed between males and females, indicated by the inclusion of the interaction between age and sex in the top late life models (Table 1 and Supporting Information Table S4). In late life male breeding success (coefficient estimate = −1.38, 95% CIs = −2.49 to −0.27) and productivity (Posterior mean = −0.99, 95% CIs = −1.55 to −0.48) declined considerably. In contrast, female breeding success (Coefficient estimate = −0.13, 95% CIs = −0.33 to 0.06) and breeding productivity (Posterior mean = −0.06, 95% CIs = −0.16 to 0.03) showed no evidence of a decline in late life during the age span that we tested. For breeding success, but not productivity, there was evidence that origin (R1 or W) had an effect on the senescent patterns (Table 1 and Supporting Information Table S4), where eagles originating from the first release show a tendency for a steeper decline in breeding success (coefficient estimate = −0.34, 95% CIs = −0.68 to −0.09) than wild-bred eagles (coefficient estimate = −0.09, 95% CIs = −0.42 to 0.22). Although the 95% confidence intervals of these estimates overlap zero, suggesting that origin had a limited role in determining the rate of decline in breeding success during late life, when this was investigated in more detail, sex-specific trends were evident. Female R1 breeding success declined (−0.21, 95% CI: −0.23 to −0.21), but female W breeding success did not (0.10, 95% CI: −0.26 to 0.48). These differences were not evident in male eagles of different origin.

3.5 Within-individual effects and selective disappearance

There was no evidence to support that the decline in success or productivity in late life was driven by the selective disappearance in males or females. The decline in male breeding success between the ages of 19 and 22 years for a subset of individuals that did not “disappear” during this age range (regression coefficient: −1.67, 95% CI: −4.57 to −0.38) matched that for all individuals in the same age range (regression coefficient: −1.67, 95% CI: −2.91 to −0.34). In the same way, trends for age-specific female breeding success between the ages of 16 and 23 years (−0.11, 95% CI: −0.06 to 5.86) were comparable to a subset of “nondisappearing” individuals that were present throughout the same age range (−0.09, 95% CI: −0.30 to 0.10). The same was true for breeding productivity where for males (aged 19–22 years) productivity declined (Posterior mean: −0.91, 95% CI: −1.51 to −0.32) in the same fashion as the data subset with only individuals that did not disappear (Posterior mean: −1.23, 95% CI: −2.10 to −0.42). Female (aged 14–23 years) breeding productivity was also similar between all individuals (Posterior mean: −0.10, 95% CI: −0.22 to 0.01) and those which did not disappear (−0.08, 95% CI: −0.23 to 0.07). The similarities in these trends (Figure S3) support the idea that declines in breeding performance are unlikely to be driven by selective disappearance.


Discussion

Many of the sources of selection that may act to maintain a high capacity for prolonged swimming in marine threespine stickleback were relaxed when stickleback colonized freshwater streams. In this study, we examined the evolutionary outcome of relaxed selection on this whole-animal performance trait by rearing pairs of stream-resident and anadromous-marine stickleback from two locations in a common laboratory environment. We found that stream-resident stickleback from both locations have evolved a lowered capacity for prolonged swimming (measured with a Ucrit test), and that three additional wild-caught stream-resident populations also have low Ucrits. Comparisons of the performance of F1 hybrids to that of their pure parental cross-types suggested differences in the Ucrits of sympatric stream-resident and marine fish occur primarily through nonadditive genetic effects, but via different genetic mechanisms in these two populations.

We also examined the functional basis for reductions in prolonged swimming performance by measuring the direction of evolution in selected candidate traits predicted to influence swimming capacity. We found that a number of morphological (pectoral fin size and shape and body shape) and physiological traits (MMR) evolved as predicted after freshwater colonization and may contribute to evolutionary variation in swimming performance among ecotypes. However, only MMR also had a genetic basis similar to prolonged swimming capacity in both Bonsall and West Creeks. These data suggest that MMR is the most likely of our candidate traits to cause evolutionary variations in prolonged swimming performance.

EVOLUTIONARY FORCES INFLUENCING PROLONGED SWIMMING PERFORMANCE: EVIDENCE FOR SELECTION?

Neutral factors, such as mutation accumulation, often influence trait evolution after a source of selection is relaxed, but the influences of direct and indirect fitness effects may also be important ( Maughan et al. 2007 Hall and Colgrave 2008 Lahti et al. 2009 ). The observation of repeated independent evolution of reduced swimming performance in our populations of stickleback suggests a possible role for selective trait reduction following relaxation of selection. This hypothesis is supported by independent observations from other parts of the species range. For example, Tudorache et al. (2007) found that wild stream-resident threespine stickleback from Belgium have a lower prolonged swimming capacity (∼6.5 BL·s −1 ) than do sympatric anadromous-marine fish (∼8.25 BL·s −1 ). In contrast, Schaarschmidt and Jürss (2003) found that only one of two stream-resident populations from the Baltic Sea had a lower Ucrit than did marine fish from this region, but their fish were tested after reproduction and had a very low prolonged swimming capacities (Ucrits of ∼3.5–4.5 BL·s −1 , compared to our values of ∼6–10 BL·s −1 ). Our data suggest that these after reproductive fish were likely senescent (see Fig. S3). Freshwater colonizations of stickleback in Eastern Europe and the Western Pacific occurred independently ( Orti et al. 1994 ), and our findings that there is a different genetic basis for Ucrit in West and Bonsall Creeks also suggest that these colonizations may be independent. A similar loss of the capacity for prolonged swimming has also been observed in nonmigratory populations of sockeye salmon (Oncorhynchus nerka), which evolved from anadromous fish after the last glaciation ( Taylor and Foote 1991 ). These rapid (<12,000 years ago), and independent, reductions in prolonged swimming performance in stream-resident fish, often in the face of gene flow from marine populations (e.g., Hagen 1967 Jones et al. 2006 ), are consistent with a role for natural selection in the evolution of reduced swimming performance.

Selective trait reduction following a relaxation of selection is expected to be influenced by two types of factors: direct and indirect fitness effects ( Fong et al. 1995 Lahti et al. 2009 ). Direct fitness effects include the costs of trait maintenance, whereas indirect fitness effects include possible functional and genetic trade-offs with other performance traits still under selection ( Lahti et al. 2009 ). Such trade-offs among performance traits are distinct from classic life-history trade-offs ( Roff and Fairbairn 2007 ), because they cannot be alleviated by increasing resource acquisition (reviewed by Ghalambor et al. 2003 Walker 2007 ). Consistent with a role for indirect costs influencing the evolution of prolonged swimming, two performance traits hypothesized to experience strong positive directional selection in freshwater stickleback, juvenile growth rate ( Barrett et al. 2008 Marchinko 2009 ) and burst swimming performance ( Walker 1997 Bergstrom 2002 ), are negatively correlated with the capacity for prolonged swimming performance. Trade-offs between growth rate and prolonged swimming capacity have been detected in many fish (e.g., Kolok and Oris 1995 Farrell et al. 1997 Billerbeck et al. 2001 ), including threespine stickleback ( Alvarez and Metcalfe 2005 Lee et al. 2010 ), and trade-offs between burst and prolonged swimming are also found in fish ( Langerhans 2009 Oufiero et al. 2011 ). In stickleback, burst swimming performance is heritable ( Garenc et al. 1998 ), and wild stream-resident fish are superior burst swimmers, but worse prolonged swimmers than marine fish ( Taylor and McPhail 1986 ), consistent with the hypothesis of a functional trade-off.

These trade-offs between burst and prolonged swimming in fish are predicted to occur because the body shapes that maximize prolonged swimming act antagonistically on burst swimming (reviewed by Webb 1982 Weihs and Webb 1983 Blake 2004 Langerhans and Reznick 2009 ). Indeed, trade-offs in swimming performance are associated with differences in body shape in Western mosquitofish ( Langerhans 2009 ). The mechanistic basis for functional trade-offs between growth rate and prolonged swimming are not as well understood, but might be mediated by metabolic energy partitioning. For example, in Atlantic silversides faster growing northern populations have a higher SMR, and thus lower scope for aerobic activity, than do southern populations that can reach higher Ucrits ( Arnott et al. 2006 ). In this study, we found evidence for genetically based differences in body shape traits that are predicted to mediate trade-offs between burst and prolonged swimming (e.g., caudal area, caudal peduncle depth, head size), consistent with a role for trade-offs influencing the evolution of prolonged swimming. However, we not find any differences in SMR between stream-resident and marine stickleback, despite the observed higher growth rates in low-plated stickleback ( Marchinko and Schluter 2007 Barrett et al. 2009 ). Because SMR represents the sum of all metabolic processes at rest, and many energy-demanding traits vary between stream-resident and marine stickleback, extracting the effects of any one process (e.g., growth) on SMR is challenging. In addition, both burst swimming and growth are associated with lateral plate morphology in stickleback ( Bergstrom 2002 Marchinko and Schluter 2007 Barrett et al. 2009 Hendry et al. 2011 ), so determining which traits mediate performance trade-offs will require studies that control for variation in plate morphology and other correlated traits, while measuring all three performance traits.

MORPHOLOGICAL AND PHYSIOLOGICAL TRAITS CONTRIBUTING TO REDUCTIONS IN PROLONGED SWIMMING PERFORMANCE

The capacity for prolonged swimming is a whole-organismal performance trait that is influenced by a number of underlying morphological, physiological, and behavioral traits ( Walker 2010 ). Therefore, this trait exhibits the phenomenon of many-to-one mapping, as many different combinations of trait values can yield equivalent performance ( Wainwright et al. 2005 ). In addition, many of the traits that influence prolonged swimming performance also contribute to other performance traits, and thus exhibit “multi-tasking” (e.g., pectoral fins are also used for maneuvering) ( Walker 2010 ). Determining the mechanisms by which a complex trait evolves, given the many available pathways, can provide insight into the selective forces acting on organisms in the wild, and the trade-offs or facilitations that influence trait evolution ( Walker 2007 ).

We found that a number of morphological and physiological traits predicted to impact prolonged swimming have evolved in stream-resident fish, and have evolved in the direction predicted by reductions in Ucrit. The evolution of a smaller, and more rounded pectoral fin, and a less streamlined body shape agree with data from wild stream-resident and marine fish ( Taylor and McPhail 1986 Schaarschmidt and Jürss 2003 ), and these differences are also found in other freshwater threespine stickleback ecotypes that vary in prolonged swimming performance (i.e., benthic vs. limnetic [ Blake et al. 2005 ], inlet vs. lake [ Hendry et al. 2011 ] among lakes [ Walker 1997 ]), suggesting this is a common response to a relaxation of selection on prolonged swimming. In addition, we found that stream-resident fish from British Columbia have evolved a lowered MMR, which agrees with the findings of Tudorache et al. (2007) in wild stickleback from Belgium. However, the SMR of our stream-resident fish did not evolve, in contrast to the findings of Kitano et al. (2010) and Tudorache et al. (2007) both of these studies found that marine fish have significantly higher SMRs than stream-resident fish. This discrepancy among studies is likely due to methodological differences. For example, Tudorache et al. (2007) and Kitano et al. (2010) measured SMR during the day, and Kitano et al. (2010) studied fish acclimated to a different photoperiod. In addition, any differences in the amount of time fish were allowed to adjust to the stress of confinement in a metabolic chamber may have affected SMR as there is variation in the respiratory response to confinement stress among stickleback populations ( Bell et al. 2010 ). As expected based upon circadian rhythms in metabolism, our nighttime measures of SMR were lower than those of Kitano et al. (2010) and Tudorache et al. (2007) . So, while our experiments find that the SMRs of stream-resident and marine threespine stickleback do not vary in freshwater, the work of Kitano et al. (2010) and Tudorache et al. (2007) indicate that the routine or active metabolic rates during the day do differ.

We also found genetically based differences in body shape and MMR between marine populations from Bonsall and West Creeks, such that West Creek marine fish had a significantly more streamlined body shape and higher MMR. This difference could be associated with ecological differences between these two populations as West Creek marine fish travel at least 35 km down the Fraser River to reach the ocean, whereas Bonsall Creek stickleback breed only a few kilometers from the mouth of the estuary ( Hagen 1967 , T. H. Vines and A. C. Dalziel, unpubl. data). These data suggest that marine fish may have the ability to adapt to local migratory conditions, similar to populations of sockeye salmon ( Eliason et al. 2011 ). In addition, these data suggest that there may have been genetic differences in ancestral marine populations at the time of freshwater colonization. If this is the case, differences in the genetic basis for Ucrit between West Creek and Bonsall Creek F1 hybrids could be due to differences in the genetic architecture of marine populations.

To further test for associations between candidate traits and performance, we compared the patterns of inheritance of Ucrit and candidate traits. None of the traits we measured displayed evidence of maternal effects, but there was significant variation in dominance among traits, and among locations. These data allowed us to reject a strong, simple, functional linkage between body shape, pectoral fin area, pectoral fin shape and Ucrit in West Creek fish, and pectoral fin area and Ucrit in Bonsall Creek fish. Hendry et al. (2011) also found mixed support for the impact of pectoral fin size on Ucrit, further indicating that the associations between fin morphology and swimming performance might be population specific, or dependent on other unmeasured traits, such as the size of the pectoral muscles that power swimming. In our Bonsall Creek crosses, we found that body shape and fin shape were associated with Ucrit in pure and F1 hybrid crosses, indicating that these traits have a similar genetic architecture, and might be important mediators of Ucrit performance. However, these two shape traits did not have a similar genetic architecture as Ucrit in West Creek crosses, indicating that the functional architecture underlying Ucrit capacity might vary among locations. The only trait that had the same genetic basis, as did Ucrit in both populations was MMR. This strong association in both locations suggests that MMR might mediate the evolutionary variation in Ucrit found among migratory and nonmigratory threespine sticklebacks.

MMR is itself a complex trait, which is dependent on a suite of underlying traits that influence oxygen uptake at the gill, transport to the swimming muscles, and utilization in the mitochondrial electron transport chain. Therefore, changes in any step of the oxygen transport cascade, and any of the many genes that contribute to each of these underlying traits could theoretically reduce MMR (reviewed by Turner et al. 2006 Montgomery and Safari 2007 ). By investigating the underlying traits that contribute to MMR, we can gain further insight into the mechanistic basis for reductions on swimming performance, examine possible trade-offs with other performance traits, and determine if the same underlying traits contribute to evolutionary reductions in performance in multiple populations of nonmigratory stickleback.

Associate Editor: C. Peichel


DISCUSSION

The results presented in this study show that only approximately half of the seeds produced by the fruits of Bauhinia ungulata are able to germinate, and that seed germination is affected by the position in which the seed is produced. In addition, the present data revealed that the differences in vigour among offspring of B. ungulata are not random with respect to the position of ovules in the pod: the overall performance of the seeds is, on the contrary, associated with the likelihood of seed maturation. Overall, seed positions located at the basal half of the treatment fruits showed lower vigour than seeds located on the stylar half of the fruits. The effect of position on vigour is observed mainly in ovules from the two basal sections, 3 and 4 in general, seed size and tree identity are the main variables affecting vigour, especially at early stages.

There are many factors affecting seed germination, some of them being genetic differences among seeds. However, seed germination also depends on external variables such as temperature and water availability. One factor that might be affecting germination success is the absence of scarification treatments, making water uptake and root emergence harder for the seeds to complete.

When seed germination was analysed among treatments, it was found that treatments 1 and 2 had a lower percentage germination than the control, whereas treatments 3 and 4 showed values of percentage germination similar to the control fruits. In treatments 1 and 2, seeds with a low probability of abortion were selectively destroyed this reduction in competition probably allowed less-fit offspring to complete their development and reach maturity. This might explain the lower probability of germination observed. By contrast, for treatments 3 and 4, seeds with a high probability of abortion were removed therefore the reduction of competition was expected to have a lesser effect on the performance of the remaining seeds. In accordance with this expectation, the percentage germination on these treatments was not significantly different from the control fruits ( Table 1). Gutierrez et al. (1996) found similar patterns in Ulex gallii (Fabaceae) the probability of seed maturation depended on the position it occupied in the fruit. Furthermore, positions with a high probability of abortion produced lighter seeds than positions with a low probability of abortion this trait correlated with germination.

This dependence of the performance of the offspring on the position of the seed in Bauhinia was further supported when each section was analysed separately. Seeds developed in the stylar half of treated fruits gave patterns of offspring performance similar to the control fruits ( Tables 2 and 3A and B). By contrast, the performance of the seedlings produced in the basal half (sections 3 and 4) was affected by this reduction in competition. This difference in responses suggests that the reduction in fitness may be caused by an increase in the frequency of maturation of less-fit seed when the competition is relaxed, rather than by the piercing technique used to obtain the experimental conditions of reduced competition.

In Phaseolus coccineus, Rocha and Stephenson (1990, 1991) found that, when competition was relaxed allowing maturation of seeds with a high probability of abortion, these showed a lower vigour than seeds produced in positions with a low probability of abortion. Other studies have found similar results. Selective seed abortion in Cynoglossum officinale (Boraginaceae) resulted in fitter offspring with increased percentages of survival ( Melser and Klinkhamer, 2001). In Mirabilis jalapa (Nyctaginaceae), pollen performance correlated with the vigour of the offspring furthermore, selective abortion produced more vigorous progeny ( Niesenbaum, 1999). In Acacia caven (Fabaceae), the performance of seedlings was associated with the probability of maturation of fruits ( Torres et al., 2002).

In B. ungulata, it was found that the only consistent effects of treatment were on early fitness variables: days to germination and days to first leaf ( Tables 2 and 3). This is by contrast with findings in other studies. In P. coccineus, the position of the seed had a significant effect on all estimates of fitness, from seed weight and germination to flower production ( Rocha and Stephenson, 1991).

One reason why it was not possible to detect differences at later stages of seedling growth might be that the parameters of late fitness measured in B. ungulata dealt with general seedling growth. These variables might be under strong selective canalization, therefore showing less observable variation than early estimates of vigour. This also suggests that early establishment is the critical stage for the survival of the seeds. Once the seedlings become established, their growth might follow a strongly canalized allometric pattern therefore the variables we measured would not reflect any differences in vigour among offspring at later stages.

Another reason why it was not possible to detect differences at later stages of growth might be that other variables, like protection against herbivores or responses to drought, were not examined. Variation in the vigour of the offspring might affect the differential allocation of resources to these factors at post-establishment stages. These factors might be especially important, since each individual has to grow for several years before reaching a reproductive status. These factors might exert a strong selection on young plants in nature ( Gerhardt, 1993), but it might be hard to detect any differences under greenhouse conditions. Studies of seed production and survival have shown that, in the Santa Rosa National Park, the initial seed output of many tree species is highly reduced by rodent predation ( Janzen, 1967 Janzen et al., 1990 O. J. Rocha, Universidad de Costa Rica, unpubl. res.). Therefore, faster rates of germination might be constantly selected and faster-germinating seeds might have a higher probability of survival. Studies of survival and establishment under natural conditions are needed, along with long-term follow-up studies to determine to what extent the performance of the offspring is affected by the patterns of seed abortion in perennial species.

Present address: Department of Biological Sciences, Kent State University, Kent, OH 44242, USA


LS4.B: Natural Selection and LS4.C: Adaptation (MS-LS4 Biological Evolution: Unity and Diversity)

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Disciplinary Core Ideas
LS4.B: Natural Selection
&bull Natural selection leads to the predominance of certain traits in a population, and the suppression of others. (MS-LS4-4)
&bull In artificial selection, humans have the capacity to influence certain characteristics of organisms by selective breeding. One can choose desired parental traits determined by genes, which are then passed on to offspring. (MS-LS4-5)

LS4.C: Adaptation
&bull Adaptation by natural selection acting over generations is one important process by which species change over time in response to changes in environmental conditions. Traits that support successful survival and reproduction in the new environment become more common those that do not become less common. Thus, the distribution of traits in a population changes. (MS-LS4-6)

Use the Template and Resource Links to Fulfill NGSS

  1. Understand that n atural selection leads to the predominance of certain traits in a population, and the suppression of others .
  2. Understand that i n artificial selection, humans can influence certain characteristics of organisms by selective breeding. One can choose desired parental traits determined by genes, which are then passed on to offspring.
  3. Understand that adaptation by natural selection acting over generations is one important process by which species change over time in response to changes in environmental conditions.
  4. Understand that traits that support successful survival and reproduction in the new environment become more common those that do not become less common. Thus, the distribution of traits in a population changes.
  5. Understand that collection of fossils and their placement in chronological order is the fossil record and documents the existence, diversity, extinction, and change of many life forms throughout the history of life on Earth.

Essential Questions:

  1. What leads to the predominance of certain traits in a population, and the suppression of others?
  2. How can humans influence certain characteristics of organisms?
  3. How do species change over time in response to changes in environmental conditions?
  4. How do the distribution of traits in a population change?

NGSS Note: Think, question, entertain ideas.

ll. Introductory Activities to Assess Prior Knowledge

A. Brainstorming Sessions
Question: What are some examples of natural selection in animal coloring?
1. Break students down into groups of 3-4.
2. Ask students to generate a list of the different animals they know that have adaptive coloring.
3 . Discuss

Brainstorming Sessions
Question: What are some examples of artificial selection in animal selective breeding?
1. Break students down into groups of 3-4.
2. Ask students to generate a list of the the animals they know of that have been affected by artificial selection.
3 . Discuss

lll. New Knowledge - Text

A. Read about:
Adaptation and Natural Selection

B. Examples of Models (depicts the concept expressed in the reading):

Ask students to look at the models and explain how each illustrates the concepts they've read about.

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lV. Experiments, Activities, Model-making (Critical Thinking)

A. Critical Thinking Activities related to adaptation and natural selection:
Adaptation and Natural Selection Activities

B. Authentic Performance - Understanding by Design (UbD) assessment tool.
Use critical thinking to complete thse Authentic Performance Activities and deepen your understanding about the above topics.

V. Summarize Knowledge - Enduring Understandings

  1. A natomical similarities and differences between organisms living today and in the fossil record, enable the reconstruction of evolutionary history and the inference of lines of evolutionary descent .
  2. A c omparison of the embryological development of different species also reveals similarities that show relationships not evident in the fully-formed anatomy .
  3. Na tural selection leads to the predominance of certain traits in a population, and the suppression of others .
  4. A rtificial selection, humans can influence certain characteristics of organisms by selective breeding. One can choose desired parental traits determined by genes, which are then passed on to offspring.
  5. A daptation by natural selection acting over generations is one important process by which species change over time in response to changes in environmental conditions.
  6. Traits that support successful survival and reproduction in the new environment become more common those that do not become less common. Thus, the distribution of traits in a population changes.
  7. A collection of fossils and their placement in chronological order is the fossil record and documents the existence, diversity, extinction, and change of many life forms throughout the history of life on Earth.

Vl. New Generation of Science Standards (NGSS) - Middle School Life Science

Disciplinary Core Ideas

LS4.A: Evidence of Common Ancestry and Diversity
&bull The collection of fossils and their placement in chronological order (e.g., through the location of the sedimentary layers in which they are found or through radioactive dating) is known as the fossil record. It documents the existence, diversity, extinction, and change of many life forms throughout the history of life on Earth. (MS-LS4-1)
&bull Anatomical similarities and differences between various organisms living today and between them and organisms in the fossil record, enable the reconstruction of evolutionary history and the inference of lines of evolutionary descent. (MS-LS4-2)
&bull Comparison of the embryological development of different species also reveals similarities that show relationships not evident in the fully-formed anatomy. (MS-LS4-3)

LS4.B: Natural Selection
&bull Natural selection leads to the predominance of certain traits in a population, and the suppression of others. (MS-LS4-4)
&bull In artificial selection, humans have the capacity to influence certain characteristics of organisms by selective breeding. One can choose desired parental traits determined by genes, which are then passed on to offspring. (MS-LS4-5)

LS4.C: Adaptation
&bull Adaptation by natural selection acting over generations is one important process by which species change over time in response to changes in environmental conditions. Traits that support successful survival and reproduction in the new environment become more common those that do not become less common. Thus, the distribution of traits in a population changes. (MS-LS4-6)

Science and Engineering Practices

Analyzing and Interpreting Data
Analyzing data in 6&ndash8 builds on K&ndash5 experiences and progresses to extending quantitative analysis to investigations, distinguishing between correlation and causation, and basic statistical techniques of data and error analysis.
&bull Analyze displays of data to identify linear and nonlinear relationships. (MS-LS4-3)
&bull Analyze and interpret data to determine similarities and differences in findings. (MS-LS4-1)

Using Mathematics and Computational Thinking
Mathematical and computational thinking in 6&ndash8 builds on K&ndash5 experiences and progresses to identifying patterns in large data sets and using mathematical concepts to support explanations and arguments.
&bull Use mathematical representations to support scientific conclusions and design solutions. (MS-LS4-6)

Constructing Explanations and Designing Solutions
Constructing explanations and designing solutions in 6&ndash8 builds on K&ndash5 experiences and progresses to include constructing explanations and designing solutions supported by multiple sources of evidence consistent with scientific ideas, principles, and theories.
&bull Apply scientific ideas to construct an explanation for real-world phenomena, examples, or events. (MS-LS4-2)
&bull Construct an explanation that includes qualitative or quantitative relationships between variables that describe phenomena. (MS-LS4-4)

Obtaining, Evaluating, and Communicating Information

Obtaining, evaluating, and communicating information in 6&ndash8 builds on K&ndash5 experiences and progresses to evaluating the merit and validity of ideas and methods.
&bull Gather, read, and synthesize information from multiple appropriate sources and assess the credibility, accuracy, and possible bias of each publication and methods used, and describe how they are supported or not supported by evidence. (MS-LS4-5)


Pollen quantity and quality affect fruit abortion in small populations of a rare fleshy-fruited shrub

Little is known about changed rates of fruit abortion due to pollen limitation or inbreeding in small and isolated populations of flowering plants. We report on a pollination experiment with the fleshy-fruited tall-shrub Prunus mahaleb at the margin of its distributional range, where reproduction might be especially limiting. Two small and isolated populations in northern Switzerland were studied for effects of pollen quantity and pollen quality on the timing of fruit abortion. There was evidence that spontaneous self-pollination led to particularly high abortion. Fruit abortion was delayed after hand pollination, which indicates limitation of pollen quantity. Self-pollination led to earlier abortion compared with experimental pollination within or between populations. There was no significant evidence for outbreeding depression in fruit abortion. We conclude that reproduction and dispersal of P. mahaleb in central Europe might be negatively affected by pollen limitation and inbreeding effects, i.e. by both pollen quantity and quality.

Die Auswirkungen von Pollenlimitierung und Inzucht auf die Fruchtabortion in kleinen und isolierten Populationen von Blütenpflanzen sind wenig untersucht. Wir berichten über ein Bestäubungsexperiment mit dem endozoochoren Strauch Prunus mahaleb an der Verbreitungsgrenze der Art, wo sexuelle Fortpflanzung vermutlich in besonderem Maße begrenzend wirkt. Die Fruchtabortion in zwei kleinen Populationen in der Nordschweiz wurden untersucht nach experimentell variierter Pollenquantität und -qualität. Spontane Selbstbestäubung führte zu besonders hoher Abortion. Handbestäubung verzögerte die Fruchtabortion und läßt damit auf Pollenlimitierung des Fruchtansatz schließen. Selbstbestäubung verursachte frühere Abortion als Handbestäubung innerhalb oder zwischen den Populationen, während eine mögliche Auszuchtdepression in bezug auf die Fruchtabortion nicht signifikant war. Reproduktion und Ausbreitung in kleinen Populationen von P. mahaleb in Mitteleuropa werden vermutlich negativ beeinflusst von Pollenlimitierung und Inzuchteffekten, das heißt sowohl von Pollenquantität als auch von Pollenqualität.


Materials and Methods

FISH COLLECTION, FERTILIZATION, AND REARING

Common-garden reared crosses: Bonsall and West Creek stickleback

We collected fish from Bonsall and West Creeks (British Columbia, Canada Fig. 1) from May to June in 2006 and 2007. Stream and marine ecotypes hybridize in these streams ( Hagen 1967 T. H. Vines and A. C. Dalziel, unpubl. data), so we collected our fish from sites >500 m from the hybrid zone, where <1% of fish from the opposite population are found. Parents for F1 crosses were identified as either marine or stream-resident based upon morphology (see Hagen 1967 McPhail 1994 ), and their genotype at markers for two loci under differential selection: Eda, a gene that controls lateral plate morphology ( Colosimo et al. 2005 ) and sodium potassium ATPase, an ion transporter that plays a major role in ion transport ( Jones et al. 2006 Shimada et al. 2010 ), following the methods of Barrett et al. (2008) . These fish were used to produce F1 crosses by artificial fertilization following the methods of Marchinko and Schluter (2007) . In total, 23 crosses were made from Bonsall Creek parents (seven pure stream [SS], five pure marine [MM], six stream mother x marine father [SM], and five marine mother x stream father [MS] crosses) and 16 crosses were made from West Creek parents (five SS, five MM, three SM, three MS).

Locations of the threespine stickleback populations used in this study. (A) Western North America with the sampling area outlined with a gray hatched square. (B) Region within the gray hatched square, with stickleback collection sites marked with gray stars and labeled in italics.

Fish were raised in dechlorinated Vancouver tap water brought to 2 ± 0.5 ppt with Instant Ocean® sea salt. Fish ate live brine shrimp twice per day for their first month, Daphnia and bloodworms (Chironomid larvae) daily for the next 3 months, and Mysis shrimp and bloodworms from 4 months on. All individuals were fed to satiation at every feeding. Families were raised in separate tanks and split to 20 fish per tank at 2 months of age. Fish were reared at a natural photoperiod and temperatures ranging from 11–17°C until March (∼9–11 months of age). At this date fish were 3.5–4.5 cm standard length, a size generally considered to be adult (e.g., Garenc et al. 1998 ), and were individually marked with elastomer tags (Northwest Marine Technology, Shaw Island, WA). Fish were then transferred to a 15°C environmental chamber with controlled 12L:12D photoperiod to prevent them from entering a reproductive state, and acclimated to these constant conditions for at least a month. We studied swimming performance in young adults that were not yet sexually mature to reduce the effects of reproduction (e.g., Ghalambor et al. 2004 ). Marine fish migrate prior to reproduction so swimming performance is ecologically relevant at this life stage. The University of British Columbia animal care committee approved all experimental procedures (A07–0288).

Wild-caught fish: Kanaka Creek, Salmon River, and Little Campbell River stickleback

We collected adult stream-resident fish from Salmon River (as in Taylor and McPhail 1986 ), Little Campbell River, and Kanaka Creek in June 2008 (Fig. 1). Wild-caught fish were held at the same conditions as laboratory-bred adults for 1 month and remained healthy.

MEASUREMENT OF MAXIMUM PROLONGED SWIMMING SPEED: CRITICAL SWIMMING SPEED (Ucrit)

We used a critical swimming speed (Ucrit) test to measure prolonged swimming performance ( Brett 1964 ). In this test water speed is increased in a stepwise manner until a fish can no longer maintain its position in the current. Ucrit is predicted to be an ecologically relevant measure of prolonged swimming for fish that migrate, live in the open ocean, or live in high-flow streams ( Kolok 1999 Plaut 2001 ), and performance correlates with migratory difficulty among populations of salmonids (e.g., Lee et al. 2003 ).

We swam six individually labeled siblings in a Brett style 10-L swim tunnel (SWIM-10 Loligo Systems, Hobro, Denmark), at a water temperature of 15 ± 1°C and salinity of 2 ppt. Water speed was calibrated with a vane wheel flow sensor (Höntzch ZSR25, GmbH, Waiblingen, Germany). Fish swam spread throughout the flume, and were constantly observed to be sure that they did not draft. All fish were <0.25% of the cross-sectional area of the tunnel so correction for solid blocking effects was not required. The Ucrit trial followed Fangue et al. (2008) , with minor modifications. We placed fish in the tunnel and let them acclimate for 30 min at 0.5 body lengths/second (BL·s −1 ). We then performed a training test by increasing the speed at 0.5 BL·s −1 increments every 2 min until failure. Fish then recovered for 3 h. To measure Ucrit, we increased speed at 0.5 BL increments every 2 min until water velocity reached 50% of the training failure speed. Thereafter, we increased speed every 10 min until the fish could not maintain its position in the current and fell back against the end of the tunnel three times. Critical swimming speed was determined using the following formula: Ucrit=Ui+ (ti/tii·Uii), where Ui is the highest speed the fish was able to swim for a full 10 min interval (BL·s −1 ), Uii is the incremental speed increase (BL·s −1 ), ti is the time the fish swam at the final speed (min), and tii is the prescribed period of swimming per speed (10 min). We also recorded the gait transition speed (Fig. S1), and found that Ucrit was significantly repeatable over 1 month (Fig. S2).

To test for an effect of sex on Ucrit, we dissected fish and sexed them anatomically after our experiments were completed, but only 100 of the original 234 fish swum could be classified unequivocally. Of these 100 fish, 81 fish from 21 of the 39 families has at least one known male and female per family and could be used to test for the effect of sex. We did not detect any effect of sex on Ucrit (data not shown nested analysis of variance [ANOVA], F1,20= 0.067, P= 0.799), which is in agreement with Whoriskey and Wooton's (1987) previous work. Thus, we combined the sexes in all later analyses. Note that Hendry et al. (2011) did find significant effects of sex in older, sexually mature stickleback kept at a summer photoperiod.

MEASUREMENT OF MORPHOLOGICAL TRAITS PREDICTED TO INFLUENCE PROLONGED SWIMMING SPEED

Photographs of laboratory-bred stickleback (n= 234 fish six fish per family) were taken within a month of Ucrits. Fish were anesthetized with 0.2 g tricaine methanesulfonate buffered with 0.4 g sodium bicarbonate in 1 L of water. The right side of the fish was photographed with a ruler in the field of view. A second photograph was taken of the right pectoral fin maximally spread over a laminated sheet of paper. Pectoral fin area was measured by tracing an outline of the fin using Image J (Fig. 3A). We used TPSdig 2.1 ( Rohlf 2010 ) to digitize 12 landmarks onto the stickleback's body (Fig. 4A, B) and six landmarks onto the stickleback's pectoral fin (Fig. 3C). We aligned and corrected pectoral fin landmarks for differences in geometric size using tpsRelw ( Rohlf 2010 ), following Albert et al. (2008) . To analyze pectoral fin shape, we performed a linear discriminant (ld) function analysis (DFA) on the aligned x and y pectoral fin coordinates, grouping our fish into six cross-types (reciprocal hybrid crosses were pooled), with the MASS package in R ( Venables and Ripley 2002 ). We chose to use DFA so that we could determine how F1 hybrid fin shape compared the fin shape of pure stream and marine fish along the axis of variation that best distinguishes pure parental types. We also used the landmarks depicted in Figure 4A, B to measure six body shape traits predicted to mediate evolutionary variation in prolonged swimming capacity in fish (e.g., Hawkins and Quinn 1996 Walker 1997 McGuigan et al. 2003 Seiler and Keeley 2007 Langerhans 2009 Rouleau et al. 2010 ). These traits were: (1) fineness ratio (maximum body depth [landmarks 5–11] divided by standard length [landmarks 2–8]), (2) shoulder point (distance from landmark 8 to intersection with line from 5 to 11 [point of maximum depth]), (3) head depth (landmarks 6–10), (4) posterior depth at third spine (landmarks 4–12), (5) caudal peduncle depth (landmarks 1–3), and (6) caudal area (sum of area of the two triangles formed by connecting landmarks 1, 3, 12 and 3, 4, 12). Interlandmark distances were calculated by TMorphGen6c (IMP suite 2006, Zeldith et al. 2004 ). We corrected measurements for overall body size by performing a least-squared regression against mass and using residuals in all subsequent analyses. Residuals were made positive by the addition of a constant, log10 transformed, and divided by 2 for linear measures and by 3 for caudal area. We performed a DFA on the six body shape measures following our methods for pectoral fin landmarks.

(A) Representative anadromous stickleback with a pectoral fin (shaded gray) spread maximally. (B) Pectoral fin area residuals (corrected for body mass) of laboratory-bred F1 families from Bonsall and West Creek parents. Data are presented as in Figure 2A, and reciprocal hybrids from each location are pooled. Different letters indicate significant differences among cross-types (P < 0.001). (C) Landmarks used to measure pectoral fin shape are labeled 1–6 and are located at the center of the nearest arrow. Arrows multiply by 4 the changes in landmark position that occur among cross-types for pectoral fin shape linear discriminant 1 (ld1), and generally summarize changes in fin shape from a stream-resident to marine fish. (D) Plot of pectoral fin shape ld1 and ld2 scores of laboratory-bred F1 families from Bonsall (Bon) and West Creek (West) parents. Abbreviations for cross-types are presented as in Figure 2A, and reciprocal hybrids from each location are pooled (West-H and Bon-H). Different letters indicate significant differences among cross-types for pectoral fin ld1 (P < 0.00001). Different symbols indicate significant differences among cross-types for pectoral fin ld2 (P < 0.0001). Both P-values were calculated from a null distribution of randomized F-values (Fig. S5–S6).

Representative stream-resident (A) and anadromous (B) sticklebacks showing landmarks (numbered circles) used to measure the six body shape variables (from black lines connecting landmarks, see materials and methods). (C) Plot of body shape ld1 and ld2 scores for laboratory-bred F1 families from Bonsall (Bon) and West Creek (West) parents. Abbreviations and data presentation follow Figure 3D. Different letters indicate significant differences among cross-types for body shape ld1 (P < 0.00001). Different symbols indicate significant differences among cross-types for body shape ld2 (P < 0.00001). Both P-values were calculated from a null distribution of randomized F-values (Fig. S7–S8).

Reproductively mature stickleback show sexual dimorphism in body and pectoral fin shape, but there is little differentiation in these characteristics in nonbreeding fish ( Hoffmann and Borg 2006 Kitano et al. 2007 ). Our fish did not enter breeding condition, and sex did not affect Ucrit (see results), so we did not include sex as a variable in our morphological analysis.

MEASUREMENT OF STANDARD AND MAXIMUM METABOLIC RATES

We measured metabolic rate indirectly via oxygen consumption. Both standard (SMR) and maximum oxygen consumption rates (MMR) were measured on individual fish in Beamish-style swim tunnels by intermittent flow respirometry (233 mL SWIM-MINI Loligo Systems) modified for use with FOXY fiber-optic oxygen probes (Ocean Optics, Dunedin, FL). Swim tunnels were housed in an external tank to maintain temperature, and a connecting water pump could be turned on to flush the inner tunnel with fully oxygenated water from the outer tank. During measurement of oxygen consumption, the pump was turned off and the tunnel was sealed. Fish were fasted for 24–36 h pretrial, temperature was maintained at 15 ± 1°C, salinity at 2 ± 0.3 ppt, and oxygen never dropped below 75% saturation. Oxygen probes were calibrated in air and N2 gas at the start and end of every trial. If calibrations drifted by >5% the data were discarded. Background bacterial respiration was measured daily and subtracted from all measures, and tunnels were cleaned with bleach biweekly. SMR was measured overnight in the dark for 90-min intervals (with a 10 min flush with oxygenated water), at a tunnel speed of <10% of critical velocity: this speed mixed the water but allowed fish to rest at the bottom of the tunnel. The mass-specific oxygen consumption rate (μmol·g −1 wet weight·h −1 ) was calculated from the slope of the oxygen trace (recorded as partial pressure of oxygen in torr), over a 50-min period within each 90-min interval. Water oxygen content was corrected for barometric pressure, solubility of oxygen in water at 15°C and 2 ppt, and calculated based upon fish weight and respirometer volume. SMR was calculated as the average of all 50-min intervals for which no activity or stress (noted as higher oxygen consumption rates) occurred.

MMR was measured during a ramp-speed trial similar to our Ucrit protocol. Fish were acclimated in the tunnel for 30 min at a speed of <10%Ucrit while the oxygen probes stabilized. The speed was then immediately increased to 50% of Ucrit, and oxygen consumption was measured continuously for at least three 30-min intervals in which the velocity was increased continuously such that the fish was constantly increasing its swimming speed. Over a 30-min interval velocity increased on average by 15% of fish's Ucrit in 2.5–5% increments. Between intervals there was a 5-min flush to replenish oxygenated water. Oxygen consumption was measured during at least three 30-min intervals approaching, and reaching, MMR. We used a continuous increase in speed to accurately capture MMR. MMR data were not collected for one Bonsall SS family, and SMR data were not collected for four Bonsall SM and three MS crosses.

STATISTICAL ANALYSIS

All statistical analyses were conducted using R version 2.11.1 ( R Development Core Team, 2010 ). Multivariate analyses of morphology are described in the section “Measurement of morphological traits.” To test the influence of cross-type on Ucrit, pectoral fin surface area, and MMR, we used a mixed-effects model with individual nested within family (random effects), and family nested within cross-type (fixed effect) with the nlme package in R ( Pinheiro et al. 2009 ). All data met the assumptions of homogeneity of variance and normality. Tukey HSD post-hoc tests were used to detect pairwise differences using the multcomp package in R ( Hothorn et al. 2008 ). Because there was an elevated type I error rate when testing the influence of cross-type on the lds obtained from DFA, P-values for these measures were obtained from a null distribution of F-statistics. We generated this distribution by randomly assigning family groupings to pectoral fin (aligned x, y coordinates) and body shape (six traits) values 10,000 times, performing DFAs on these new data, and then comparing ld1 and ld2 values with ANOVA. The resulting F-values generated a null distribution, to which we compared our F-values to calculate a P-value (Fig. S5–S8). Because there are no major differences between the results of our nested ANOVA and ANOVA on collapsed family means (data not shown), we conducted the randomizations on the family means of our 39 cross-types, and tested the influence of cross-type on pectoral fin ld1 and ld2 and body shape ld1 and ld2 using a one-way ANOVA on family means, and not a mixed-effects model. Values for reciprocal hybrid crosses were not significantly different for any of our measures, so we collapsed these crosses in all analyses, with the exception of Ucrit (Fig. 1A).

To compare the Ucrit of our laboratory-bred stream-resident crosses (Fig. 2A) to our wild-caught stream-resident fish (Fig. 2B), we conducted a one-way ANOVA, using family means as replicates for laboratory crosses, and individual fish as replicates for wild-caught fish. We compared patterns of variation in Ucrit and candidate morphological and physiological traits to examine associations between underlying traits and performance, and did not use correlation analysis, because of the statistical problems that arise when using nonindependent F1 hybrids. We also explicitly tested for differences in dominance between West and Bonsall Creeks for Ucrit, pectoral fin surface area, pectoral fin shape ld1, body shape ld1, and MMR by modifying our original model into a genetic model with terms for additivity, dominance, location, and interactions between degree of dominance and location (y∼ location + additive + dominance + location × dominance), with the nlme package in R ( Pinheiro et al. 2009 ). Differences in dominance were tested by examining the interaction between location and dominance.

(A) Critical swimming speed (Ucrit) of laboratory-bred F1 families from Bonsall and West Creek parents. The ecotypes of the crosses’ parents (stream x stream [SS, white circles], marine x marine [MM, solid black circles], stream mother x marine father [SM, gray circles], and marine mother x stream father [MS, gray circles]) are presented on the x-axis. Data are presented as the grand means ±SEM (SEM = standard error of the mean) of all family means, but statistical tests included all individuals in a nested ANOVA model (see results text). Different letters indicate significant differences among the eight cross-types (P < 0.0001). (B) Ucrit of wild-caught stream-resident fish from three additional populations (see Fig. 1), with collection location (Sal = Salmon River, Kan = Kanaka Creek, LC = Little Campbell River) noted on the x-axis. Data are presented as means ±SEM.


Watch the video: GCSE Biology - Selective Breeding #53 (January 2022).