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The world’s “100 worst invasive species” were listed in 2000. The list is taxonomically diverse, often cited (typically for single-species studies), and its species are frequently reported in global biodiversity data bases. We acted on the principle that these notorious species should be well-reported to help answer two questions about global biogeography of invasive species (i.e., not just their invaded ranges): (1) “how are data distributed globally?” and (2) “what predicts diversity?” We collected location data for each of the 100 species from multiple data bases; 95 had sufficient data for analyses. For question (1), we mapped global species richness and cumulative occurrences since 2000 in (0.5 degree)2 grids. For question (2) we compared alternative regression models representing non-exclusive hypotheses for geography (i.e., spatial autocorrelation), sampling effort, climate, and anthropocentric effects. Reported locations of the invasive species were spatially-biased, leaving large gaps on multiple continents. Accordingly, species richness was best explained by both anthropocentric effects not often used in biogeographic models (Government Effectiveness, Voice & Accountability, human population size) and typical natural factors (climate, geography; R2 = 0.87). Cumulative occurrence was strongly related to anthropocentric effects (R2 = 0.62). We extract five lessons for invasive species biogeography; foremost are the importance of anthropocentric measures for understanding invasive species diversity patterns and large lacunae in their known global distributions. Despite those knowledge gaps, advanced models here predict well the biogeography of the world’s worst invasive species for much of the world.

As a result of the pet trade, Africa’s Nile monitor (Varanus niloticus) is now established in North America (Florida). This generalist carnivore is a potential threat to native wildlife, requiring proactive measures to effectively prevent further spread into novel regions. To determine regions at risk, we create and compare alternative ensemble species distribution models (SDMs) using a model selection approach (with 10 possible modeling algorithms grouped according to assumptions). The ensemble SDMs used presence and environmental data from both native (Africa) and nonnative (Florida) locations. The most predictive consensus SDMs for native and native + nonnative data sets (TSS = 0.87; Sensitivity = 93%; Specificity = 94%) were based on the boosted regression tree (BRT), classification tree analysis (CTA), and random forest (RF) modeling algorithms with all environmental predictor variables used. The global Nile monitor SDMs predict strong habitat suitability in tropical and subtropical regions in the Americas, the Caribbean, Madagascar, Southeast Asia, and Australia. Florida Nile monitor populations are less likely to spread into the Neotropics than if pets now in the Southwest USA are released intentionally or accidentally. Management options to avoid this spread into vulnerable regions are to actively prohibit/regulate Nile monitors as pets, enforce those restrictions, and promote exotic pet amnesty programs. The model selection approach for ensemble SDMs used here may help improve future SDM research

Purpose: Mental fatigue causes decreases in aspects of athletes' performance. Elite coaches commonly undertake cognitively demanding tasks and are seemingly at similar risk of subsequent performance impairment. However, elite sport coaches' experiences of mental fatigue, alongside other markers of psychobiological stress, have yet to be quantified. Methods: Three elite coaching and performance staff (2 women and 1 man) provided 100-mm visual analog scale ratings of mental fatigue, physical fatigue, readiness to perform, and salivary samples for later cortisol (sCort) and alpha-amylase (sAA) analysis. Data were obtained on the same morning each week across a 16-week preseason. Data were subset by individual coach for descriptive and repeated-measures correlational analyses. Results: Fluctuating mental fatigue was observed over the 16 weeks (min–max; coach 1 = 25–86 AU; coach 2 = 0–51 AU; and coach 3 = 15 − 76 AU). Elevated levels of mental fatigue were reported at multiple time points, with individual variability observed. sCort (in nanomoles per liter), sAA (in micromoles per liter), and sAA:sCort indicated that coaches experienced psychophysiological stress (min–max; coach 1 sCort = 8.42–17.31, sAA = 52.40–113.06, sAA:sCort = 3.20–12.80; coach 2 sCort = 4.20–9.70, sAA = 158.80–307.20, sAA:sCort = 21.10–61.70; and coach 3 sCort = 6.81−19.66, sAA = 86.55–495.85, sAA:sCort = 4.90–35.50). A significant inverse relationship between mental fatigue and readiness to perform (r = −.44 [−.64 to −.17], P =.002) was identified. Conclusions: Elite sport coaches report elevated instances of mental fatigue during a preseason training period. Those involved in elite sports should act to understand the presence and potential subsequent impacts of staff mental fatigue and consider management or mitigation strategies. Optimization of the cognitive performance of coaches and performance staff presents as a potential source of competitive advantage. [ABSTRACT FROM AUTHOR]

Purpose: This study examined the relationships between training workloads, game workloads, and match performance in an elite netball team. Methods: Ten elite female netball athletes were monitored over a complete season. Training and game external workloads were determined through inertial movement units and expressed as absolute PlayerLoad (PL) and change of direction (COD). Monthly workload and training efficiency index were also calculated, which used internal workloads (session rating of perceived exertion and summated heart-rate zones). Game performance was assessed through a performance analysis statistic algorithm called NetPoints. To account for the influence of team game workloads on each other, the average workload for midcourt positions (avgMC) was calculated for each game. Data for each athlete were transformed into z scores, and linear mixed modeling was used to build models to examine the relationships between workloads and game performance. Results: Monthly PL, training efficiency index PL, and avgMC PL were statistically significant (P <.05) and positively related to game PL (z = 0.20–0.35, P <.001–.02). For game COD, statistically significant positive relationships were found between monthly COD (z = 0.29 [0.11], P =.01) and avgMC COD (z = 0.21 [0.09], P =.03). The models for NetPoints found significant negative relationships with monthly PL (z = 0.46 [0.12], P <.001) and COD (z = −0.36 [0.11], P =.01). Conclusions: Higher monthly workloads are related to higher game workload; however, they are also related to decreases in match performance. Therefore, netball practitioners should consider that increases to training workload in a 4-week period prior to a game can influence game workloads and performance. [ABSTRACT FROM AUTHOR]

Purpose: Mental fatigue is emerging as an important consideration for elite sporting performance, yet it is rarely monitored. The present study assessed changes in mental fatigue in professional team-sport athletes across 2 seasons and examined the relationship between mental fatigue and other athlete self-report measures of well-being. Methods: Elite netballers contracted to all teams competing in Australia's premier professional netball competition during the 2018 and 2019 seasons (N = 154) participated. Using 5-point Likert scales, mental fatigue, fatigue (physical), tiredness, sleep quality, stress, mood, and motivation were assessed daily across 2 seasons composed of 14 round and finals series. Results: The ratings of mental fatigue significantly changed during both seasons. In 2018, lower ratings of mental fatigue were reported in round 1 versus 3, 4, 6, 8, and 14; round 7 versus 6; and round 6 versus 10 (P <.05). In 2019, lower ratings of mental fatigue were identified for round 1 versus 3, 9, 10 to 14, and semifinal; round 2 versus 10 to 13; and 5 versus 10 to 12 (P <.05). Ordinal regression revealed significant differences between mental fatigue and physical fatigue (P <.001), tiredness (P <.001), stress (P <.001), mood (P <.001), and motivation (P <.05). Conclusions: The present study found mental fatigue to significantly fluctuate across a season in elite netballers. Moreover, perceived mental fatigue differed from physical fatigue, tiredness, stress, mood, and motivation. The data impress the need for mental fatigue to be included as an independent measure of athlete well-being. Monitoring of mental fatigue can allow practitioners to implement strategies to manage its influence on performance. [ABSTRACT FROM AUTHOR]

Purpose: The aim of this study was to map responses over 32 hours following high-load (HL) and moderate-load (ML) half-squat priming. Methods: Fifteen participants completed control, HL (87% 1RM), and ML (65% 1RM) activities in randomized, counterbalanced order. Countermovement jump (CMJ), squat jump (SJ), saliva testosterone, saliva cortisol, and perceptual measures were assessed before and 5 minutes, 8 hours, 24 hours, and 32 hours after each activity. Results are presented as percentage change from baseline and 95% confidence interval (CI). Cliff delta was used to determine threshold for group changes. Results: SJ height increased by 4.5% (CI = 2.2–6.8, Cliff delta = 0.20) 8 hours following HL. CMJ and SJ improved by 6.1% (CI = 2.1–7.8, Cliff delta = 0.27) and 6.5% (CI = 1.2–11.8, Cliff delta = 0.30), respectively, 32 hours after ML. No clear diurnal changes in CMJ or SJ occurred 8 hours following control; however, increases of 3.9% (CI = 2.9–9.2, Cliff delta = 0.26) and 4.5% (CI = 0.9–8.1, Cliff delta = 0.24), respectively, were observed after 32 hours. Although diurnal changes in saliva hormone concentration occurred (Cliff delta = 0.37–0.92), the influence of priming was unclear. Perceived "physical feeling" was greater 8 hours following HL (Cliff delta = 0.36) and 32 hours after ML and control (Cliff delta = 0.17–0.34). Conclusions: HL priming in the morning may result in small improvements in jump output and psychophysiological state in the afternoon. Similar improvements were observed in the afternoon the day after ML priming. [ABSTRACT FROM AUTHOR]

High intensity interval training (HIIT) has been shown to consistently elicit rapid and significant adaptations in a number of physiological systems, across many different healthy and clinical populations. In addition, there is increasing interest in how some acute, yet transient responses to high intensity exercise potentially reduce the risks of particular diseases. Recent work has shown that discrete, brief bouts of high intensity exercise (termed 'exercise snacks') can improve glucose control and vascular health and thus counter the negative cardiometabolic consequences of prolonged, uninterrupted periods of inactivity. In this brief review, we advance the case, using evidence available from pre‐clinical studies in the exercise oncology literature, that brief, frequently completed bouts of high intensity exercise embedded within an individual's overall daily and weekly physical activity schedule, may transiently impact the tumour microenvironment and improve the health outcomes for those who have been diagnosed and treated for cancer. [ABSTRACT FROM AUTHOR]