Global hotspots of traded phylogenetic and functional diversity.

Wildlife trade is a multibillion-dollar industry1 targeting a hyperdiversity of species2 and can contribute to major declines in abundance3. A key question is understanding the global hotspots of wildlife trade for phylogenetic (PD) and functional (FD) diversity, which underpin the conservation of evolutionary history4, ecological functions5 and ecosystem services benefiting humankind6. Using a global dataset of traded bird and mammal species, we identify that the highest levels of traded PD and FD are from tropical regions, where high numbers of evolutionary distinct and globally endangered species in trade occur. The standardized effect size (ses) of traded PD and FD also shows strong tropical epicentres, with additional hotspots of mammalian ses.PD in the eastern United States and ses.FD in Europe. Large-bodied, frugivorous and canopy-dwelling birds and large-bodied mammals are more likely to be traded whereas insectivorous birds and diurnally foraging mammals are less likely. Where trade drives localized extinctions3, our results suggest substantial losses of unique evolutionary lineages and functional traits, with possible cascading effects for communities and ecosystems5,7. Avoiding unsustainable exploitation and lost community integrity requires targeted conservation efforts, especially in hotspots of traded phylogenetic and functional diversity.

The ecological drivers and consequences of wildlife trade.

Wildlife trade is a key driver of extinction risk, affecting at least 24% of terrestrial vertebrates. The persistent removal of species can have profound impacts on species extinction risk and selection within populations. We draw together the first review of characteristics known to drive species use - identifying species with larger body sizes, greater abundance, increased rarity or certain morphological traits valued by consumers as being particularly prevalent in trade. We then review the ecological implications of this trade-driven selection, revealing direct effects of trade on natural selection and populations for traded species, which includes selection against desirable traits. Additionally, there exists a positive feedback loop between rarity and trade and depleted populations tend to have easy human access points, which can result in species being harvested to extinction and has the potential to alter source-sink dynamics. Wider cascading ecosystem repercussions from trade-induced declines include altered seed dispersal networks, trophic cascades, long-term compositional changes in plant communities, altered forest carbon stocks, and the introduction of harmful invasive species. Because it occurs across multiple scales with diverse drivers, wildlife trade requires multi-faceted conservation actions to maintain biodiversity and ecological function, including regulatory and enforcement approaches, bottom-up and community-based interventions, captive breeding or wildlife farming, and conservation translocations and trophic rewilding. We highlight three emergent research themes at the intersection of trade and community ecology: (1) functional impacts of trade; (2) altered provisioning of ecosystem services; and (3) prevalence of trade-dispersed diseases. Outside of the primary objective that exploitation is sustainable for traded species, we must urgently incorporate consideration of the broader consequences for other species and ecosystem processes when quantifying sustainability.

Repetitions in Reserve Is a Reliable Tool for Prescribing Resistance Training Load.

ABSTRACT: Lovegrove, S, Hughes, L, Mansfield, S, Read, P, Price, P, and Patterson, SD. Repetitions in reserve is a reliable tool for prescribing resistance training load. J Strength Cond Res 36(10): 2696-2700, 2022-This study investigated the reliability of repetitions in reserve (RIR) as a method for prescribing resistance training load for the deadlift and bench press exercises. Fifteen novice trained men (age: 17.3 ± 0.9 years, height: 176.0 ± 8.8 cm, body mass: 71.3 ± 10.7 kg) were assessed for 1 repetition maximum (1RM) for deadlift (118.1 ± 27.3 kg) and bench press (58.2 ± 18.6 kg). Subsequently, they completed 3 identical sessions (one familiarization session and 2 testing sessions) comprising sets of 3, 5, and 8 repetitions. For each repetition scheme, the load was progressively increased in successive sets until subjects felt they reached 1-RIR at the end of the set. Test-retest reliability of load prescription between the 2 testing sessions was determined using intraclass correlation coefficient (ICC) and coefficient of variation (CV). A 2-way analysis of variance with repeated measures was used for each exercise to assess differences in the load corresponding to 1-RIR within each repetition scheme. All test-retest comparisons demonstrated a high level of reliability (deadlift: ICC = 0.95-0.99, CV = 2.7-5.7% and bench press: ICC = 0.97-0.99, CV = 3.8-6.2%). Although there were no differences between time points, there was a difference for load corresponding to 1-RIR across the 3 repetition schemes (deadlift: 88.2, 84.3, and 79.2% 1RM; bench press: 93.0, 87.3, and 79.6% 1RM for the 3-, 5-, and 8-repetition sets, respectively). These results suggest that RIR is a reliable tool for load prescription in a young novice population. Furthermore, the between-repetition scheme differences highlight that practitioners can effectively manipulate load and volume (repetitions in a set) throughout a training program to target specific resistance training adaptations.

Estimating Repetitions in Reserve in Four Commonly Used Resistance Exercises.

ABSTRACT: Hughes, LJ, Peiffer, JJ, and Scott, B. Estimating repetitions in reserve in four commonly used resistance exercises. J Strength Cond Res XX(X): 000-000, 2020-This study aimed to determine the accuracy and reliability of estimating repetitions in reserve (RIR) across the squat, bench press, overhead press, and prone row exercises, using both free-weight and Smith machine modalities. Twenty-one trained men attended the laboratory on 14 occasions. They were assessed for 1 repetition maximum (1RM) for the squat, bench press, prone row, and overhead press exercises and subsequently completed 6 RIR testing sessions using 65, 75, and 85% 1RM. In these trials, subjects indicated when they reached 2 RIR (i.e., perceive they could only perform 2 more repetitions), before continuing the set to failure. The same process was then replicated using the alternative equipment modality. To determine accuracy of 2-RIR estimates, 1-sample t-tests assessed differences between 2 and the actual number of repetitions completed after subjects indicated they had reached 2 RIR. Intraclass correlation coefficients were used to determine the reliability of test-retest RIR estimated. There were no clear differences in the accuracy or reliability of estimating RIR between free-weight and Smith machine exercises. Load, however, proved an important factor with the highest accuracy associated with RIR estimations performed when using 85%, followed by 75 and 65% 1RM, respectively. When using loads of 75 and 65% 1RM, it was increasingly likely that individuals would underestimate RIR by >1 repetition, which would practically lead to an undesired reduction in training volume. These results highlight that although estimates of 2 RIR may be accurate and reliable in heavy load resistance training (≥85% 1RM), practitioners should be wary of using this measure with lighter loads.

Estimating Repetitions in Reserve for Resistance Exercise: An Analysis of Factors Which Impact on Prediction Accuracy.

Mansfield, Sean, K, Peiffer, Jeremiah, J, Hughes, Liam, J, and Scott Brendan, R. Estimating repetitions in reserve for resistance exercise: an analysis of factors which impact on prediction accuracy. J Strength Cond Res XX(X): 000-000, 2020-The purpose of this study was to examine the influence of knowing the load being lifted on the accuracy of repetitions-in-reserve (RIR) estimates, during both moderate- (60% 1 repetition maximum [RM]) and heavy-load (80% 1RM) exercise. Twenty trained men (age: 25.9 ± 4.5 years, height: 181 ± 7 cm, body mass: 86.5 ± 13.7 kg) were assessed for 1RM in bench press (98.4 ± 16.4 kg) and prone row (72.0 ± 11.7 kg), before being randomized into control (i.e., informed of the load; n = 10) or blinded (noninformed; n = 10) conditions. Subjects then completed 2 protocols in a randomized order: 3 sets at 80% 1RM and 3 sets at 60% 1RM. During each set of these protocols, subjects were asked to estimate their RIR before continuing the set to failure. Differences in estimated and actual RIR between sets and conditions were determined via 3-way repeated measures analysis of variance for the 60 and 80% 1RM protocols independently. No differences in RIR accuracy were observed between blinded vs nonblinded conditions. Repetitions-in-reserve estimates were lower than actual RIR for the first set of both exercises in 60 and 80% protocols (p ≤ 0.007, effect size [ES]: 1.30-2.89 [moderate-large]) and for set 2 of the 80% bench press protocol (p = 0.046, ES: 0.39 [small]). Knowing the load during resistance exercise or the %1RM of the load lifted did not influence the estimates of RIR. The ability to accurately determine RIR in the 60 and 80% 1RM protocols improved from sets 1-3, indicating that estimation of RIR is enhanced when an individual is estimating RIR at a closer point to actual failure.

Load-velocity relationship 1RM predictions: A comparison of Smith machine and free-weight exercise.

This study aimed to determine differences in the validity and reliability of 1RM predictions made using load-velocity relationships in Smith machine and free-weight exercise. Twenty well-trained males attended six sessions, comprising the Smith machine and free-weight squat, bench press, prone row and overhead press. Load-velocity relationship-based 1RM predictions were performed using minimal velocity threshold (1RMMVT), load at zero velocity (1RMLD0) and force-velocity (1RMFV) methods, with 5- or 7-loads. Measured 1RM did not differ from 1RMMVT or 1RMLD0 for any of the Smith machine exercises, while it was higher than 1RMFV for all exercises except the prone row. For the free-weight variations all 1RM predictions differed from measured 1RM for the squat and overhead press, while measured and predicted 1RM did not differ in the bench press and prone row. No differences were observed between 7-and 5-load predictions. 1RMMVT was the most reliable and valid of the methods. Smith machine exercises resulted in more reliable predictions than free weight exercises. 1RMMVT provides valid and reliable predictions for the Smith machine, squat, bench press, prone row and overhead press and free-weight bench press and prone row. Practitioners must be aware of the poor validity of free-weight squat and overhead press predictions.

Using a Load-Velocity Relationship to Predict One repetition maximum in Free-Weight Exercise: A Comparison of the Different Methods.

Hughes, LJ, Banyard, HG, Dempsey, AR, and Scott, BR. Using a load-velocity relationship to predict one repetition maximum in free-weight exercise: a comparison of the different methods. J Strength Cond Res 33(9): 2409-2419, 2019-The purpose of this study was to investigate the reliability and validity of predicting 1 repetition maximum (1RM) in trained individuals using a load-velocity relationship. Twenty strength-trained men (age: 24.3 ± 2.9 years, height: 180.1 ± 5.9 cm, and body mass: 84.2 ± 10.5 kg) were recruited and visited the laboratory on 3 occasions. The load-velocity relationship was developed using the mean concentric velocity of repetitions performed at loads between 20 and 90% 1RM. Predicted 1RM was calculated using 3 different methods discussed in existing research: minimal velocity threshold 1RM (1RMMVT), load at zero velocity 1RM (1RMLD0), and force-velocity 1RM methods (1RMFV). The reliability of 1RM predictions was examined using intraclass correlation coefficient (ICC) and coefficient of variation (CV). 1RMMVT demonstrated the highest reliability (ICC = 0.92-0.96, CV = 3.6-5.0%), followed by 1RMLD0 (ICC = 0.78-0.82, CV = 8.2-8.6%) and 1RMFV (ICC = -0.28 to 0.00, CV = N/A). Both 1RMMVT and 1RMLD0 were very strongly correlated with measured 1RM (r = 0.91-0.95). The only method which was not significantly different to measured 1RM was the 1RMLD0 method. However, when analyzed on an individual basis (using Bland-Altman plots), all methods exhibited a high degree of variability. Overall, the results suggest that the 1RMMVT and 1RMLD0 predicted 1RM values could be used to monitor strength progress in trained individuals without the need for maximal testing. However, given the significant differences between 1RMMVT and measured 1RM, and the high variability associated with individual predictions performed using each method, they cannot be used interchangeably; therefore, it is recommended that predicted 1RM is not used to prescribe training loads as has been previously suggested.

Using Load-Velocity Relationships to Quantify Training-Induced Fatigue.

Hughes, LJ, Banyard, HG, Dempsey, AR, Peiffer, JJ, and Scott, BR. Using load-velocity relationships to quantify training-induced fatigue. J Strength Cond Res 33(3): 762-773, 2019-The purpose of this study was to investigate using load-velocity relationships to quantify fluctuations in maximal strength (1 repetition maximum [1RM]), which occur as a result of training-induced fatigue. The 19 well-trained men (age: 24.3 ± 2.9 years, height: 180.1 ± 5.9 cm, body mass: 84.2 ± 10.5 kg, and squat 1RM: 151.1 ± 25.7 kg) who were recruited for this study attended 5 sessions. After baseline strength testing, individual load-velocity relationships were established using mean concentric velocity during visits 2, 4, and 5, with visit 3 consisting of a bout of fatiguing exercise (5 sets of squats performed to muscular failure with 70% 1RM). Predicted 1RM values were calculated using the minimal velocity threshold (1RMMVT), load at zero velocity (1RMLD0), and force-velocity (1RMFV) methods. Measured 1RM, maximal voluntary contractions, and perceived muscle soreness were used to examine the effects of fatigue in relation to the predicted 1RM scores. The 1RMMVT and 1RMLD0 demonstrated very strong and strong correlations with measured 1RM during each of the sessions (r = 0.90-0.96 and r = 0.77-0.84, respectively), while no strong significant correlations were observed for the 1RMFV. Further analysis using Bland-Altman plots demonstrated substantial interindividual variation associated with each method. These results suggest that load-velocity-based 1RM predictions are not accurate enough to be used for daily training load prescription, as has been previously suggested. Nevertheless, these predictions are practical to implement during an individual's warm-up and may be useful to indicate general fluctuations in performance potential, particularly if used in conjunction with other common monitoring methods.