Estimated Growth Parameters Research Articles

Yes, they're all individuals: Hierarchical models for repeat survey data improve estimates of tree growth and size

Abstract Repeat measurement surveys of tree size are used in forests to estimate growth behaviour, biomass and population dynamics. Although size is measured with error and individuals vary in their growth trajectories, current size‐based growth modelling approaches do not usually or fully account for both of these features, and therefore under‐utilise available data. We present a new method that leverages the auto‐correlation structure of repeat surveys into a hierarchical Bayesian longitudinal growth model. This new structure allows users to correct for measurement error and capture individual‐level variation in growth trajectories and parameters. To demonstrate the new method we applied it to a sample of tropical tree survey data from long‐term monitoring sites at Barro Colorado Island. We were able to reduce estimated error in size and growth, and extract individual‐ and population‐level growth parameter estimates. We used simulation to evaluate the ability of the new method to improve estimates of growth rate and size, and estimate individual and species‐level parameters. Our method substantially improved the root mean squared error (RMSE) for growth by an average of 61% compared to existing approaches using pairwise differences and reduced RMSE in estimated size RMSE compared to ‘observed’ values in simulated data. Better numerical integration methods (Runge–Kutta fourth order in comparison to Euler and midpoint) provided better estimates of parameters, but did not improve the estimation of size and growth. The choice of a positive growth function eliminated all negative increments without data exclusion. Overall, this study shows how we can gain new and improved insights on growth, using repeat forest surveys. Our new method offers improved biomass dynamics estimation through reduced error in sizes over time, coupled with novel information about within‐species variation in growth behaviour that is inaccessible with species average models, such as individual parameters for the growth function which allows for relationships between parameters to be considered for the first time.

Estimates of life-history and growth parameters of exploited fish species in lakes Edward and George: Implications on exploitation status, population dynamics, management, and conservation of native species.

Adequate knowledge is essential for responsible inland fisheries. However, many inland fisheries lack monitoring, and therefore, decision-making for fisheries management is not reliable. In this paper, we used data from surveys and literature to estimate the life-history and growth parameters of 16 exploited fish stocks in the Ugandan part of Lake Edward and Lake George (East Africa). The estimated parameters are pivotal indicators of fish stock status, particularly in data-poor fisheries. The estimated parameters included maximum length (Lmax) and mean length (Lmean) as indicators of size structure in experimental and commercial catches, coefficients of length-weight relationships, length at 50% maturity (Lm50), fecundity, von Bertalanffy parameters, total mortality (Z), and natural mortality (M). These parameters were estimated using empirical formulae, statistical methods, and analyses of length frequencies. Only two stocks of semutundu Bagrus docmak exhibited significant and increasing trends in Lmax (Lake Edward) and Lmean (Lake George). The estimates for the remaining parameters were consistent with those in FishBase and other literature resources, either for the same species or related species. This consistency indicates their reliability for application in decision-making and further assessments. Some parameters showed evidence of unsustainable fishing. For example, estimates of Lm50 for four of the assessed stocks belonging to two species (Nile tilapia Oreochromis niloticus and marbled lungfish Protopterus aethiopicus) were lower than baseline estimates in the studied waterbodies. Furthermore, the Lmean in catches for all the stocks were less than the optimum lengths (Lopt), which maximize catches with a minimal impact on biomass and size structure. No significant changes in Lmean, length-frequency distributions, and size at maturity could be attributed to the management changes implemented in 2018, probably because it is too early to observe changes in these parameters. However, there are positive signs attributable to the changes in management as shown by a high proportion of mature individuals in commercial catches for most of the stocks for which the proportion was calculated, and an increase in Lmean and Lmax for some stocks, such as B. docmak, in commercial or experimental catches. New estimates from this study will enhance decision-making and further assessments of fisheries. Routine monitoring is recommended to update and improve the estimates.

PRELIMINARY STUDIES ON REPRODUCTIVE AND GROWTH PERFORMANCE OF MUTURU CATTLE IN SOUTHWEST NIGERIA

The Muturu cattle is a shorthorn, humpless and trypanotolerant breed of cattle found in parts of Southern and North Central Nigeria. Though small in size, it is highly valued for its meat quality, ease of management and as a traditional gift during important ceremonies. However, reliable information on reproductive and growth performance of the breed is scarce, hence, a preliminary analysis of data collected between 2016 and 2023 at a Muturu cattle ranch located in the humid zone of Southwest Nigeria was carried out. Parameters analysed were birthweight, secondary sex ratio, calving interval and liveweight. Results showed birthweight of 12.05 + 0.09 kg and 11.22 + 0.13 kg for male and female calves respectively (P<0.001). Calving interval averaged 469.3+31.4 days while calving pattern was significantly influenced by season with most calvings occuring in the late dry season (62.3%) and the least in the late wet season (1.9%). In conclusion, it is recommended that further studies with larger number of animals be carried out for more precise estimates of reproductive and growth parameters in Muturu cattle.

Growth patterns of the invasive crab Cronius ruber (Brachyura, Portunidae) in the Canary Islands (Central-East Atlantic)

The growth pattern, including the moult process, relative growth, and estimation of von Bertalanffy growth parameters of the invasive non-indigenus species (NIS) Cronius ruber were studied in the Canary Islands. Despite being found in both, native and invaded areas, the life cycle and ecology of this species remain unknown. The study revealed a greater abundance of females than males, with males exhibiting larger carapace sizes and weights than females. Throughout ontogeny, relative growth was observed, with males reaching carapace widths (CW) between 54.5 and 57.8 mm and females reaching 49.9 mm CW. Allometry in the growth of the males’ chelae and females’ abdomen was consistently positive. Cronius ruber was found to be a fast-growing species, reaching the postpubertal growth phase (maturity) between 0.5 and 1 year of age in males and between 0.4 and 0.8 years in females. The intermoult period increased by more than 638 % in males and 443 % in females with age. Individuals undergo 13 moults during the first phase of life (until 30 mm CW) and then reduced to 6 moults until the end of life, concluding with a final terminal moult. The lifespan until the terminal moult is approximately 2.5 years, followed by a survival time over 1.5 years. von Bertalanffy growth parameters estimated using monthly CW-frequency data for males (L∞=97.4 mm CW, k=1.89 years−1, to=0.31 years), and for females (L∞=86.72 mm CW, k=1.91 years−1, to=0.29 years). Similar results were obtained using growth increment data.

Mining sensitive hyperspectral feature to non-destructively monitor biomass and nitrogen accumulation status of tea plant throughout the whole year

Rapid and non-destructive estimation of tea plant growth and nitrogen (N) nutrition status using hyperspectral remote sensing is crucial for precise management of tea gardens. This study aimed to mine and fuse sensitive hyperspectral features to achieve an accurate estimation of tea plant growth parameters (biomass and N accumulation) throughout the whole year. An ASD Handheld 2 sensor was used to collect canopy hyperspectral reflectance of tea plants across four periods (Period 1–4) within a year, with tea plant biomass and N accumulation indicators acquired synchronously. The measured spectral reflectance and its first derivative, and wavelet feature were extracted and used to establish quantitative relationships with tea plant growth parameters. Random forest and LASSO algorithms were employed to combine sensitive hyperspectral features and construct the biomass and N accumulation monitoring models. The results showed that wavelet features (R2 = 0.35–0.58) had a stronger correlation with tea plant biomass and N accumulation parameters compared with the measured reflectance or first derivative spectral features. Similarly, the hyperspectral indices (R2 = 0.51–0.69) derived from sensitive wavelet features performed an accurate estimation of tea plant growth parameters. Furthermore, the combination of sensitive hyperspectral indices derived from measured reflectance, first derivative, and wavelet feature using random forest (R2 = 0.67–0.76) and LASSO (R2 = 0.61–0.72) algorithms achieved the greatest accuracy for monitoring tea plant biomass and N accumulation compared with individual hyperspectral feature. Additionally, the above estimation models obtained higher accuracy in period 4 compared to periods 1–3. This study provides valuable remote sensing technical support for predicting biomass and N accumulation status of tea plant throughout the whole year.

Population structure and exploitation rate of striped snakehead (Channa striata) in Kawunganten Swamp, Cilacap, Indonesia: Important for fisheries management

Abstract. Setyaningrum N, Sugiharto, Susatyo P. 2024. Population structure and exploitation rate of striped snakehead (Channa striata) in Kawunganten Swamp, Cilacap, Indonesia: Important for fisheries management. Biodiversitas 25: 3255-3263. Snakehead (Channa striata) is a swamp fish spread in almost all Indonesian waters and has a high economic value. In addition, Bringkeng, Grugu, and Babakan Villages are included in Kawunganten Swamp areas with good fishing potential. Therefore, this study aimed to determine the population structure and the exploitation rate of Channa striata in Kawunganten Swamp Cilacap, Indonesia. The survey method with purposive random sampling was used and this study was conducted from April to July 2020. The fish samples were collected from three Bringkeng, Grugu, and Babakan Swamp stations, while the variables examined were abundance and rate of exploitation. The parameters measured included the length of Channa striata as well as the growth and mortality rate in each swamp. Abundance was analyzed using the Kruskal-Wallis test, and exploitation rate was assessed with Pauly's model using FAO-ICLARM Stock Assessment Tools II (FISAT II). The results showed that the highest abundance of Channa striata was in Bringkeng station, three times higher than in Babakan station. Bringkeng and Babakan stations were considered a fish-rearing swamp due to the dominance of adolescence-sized samples. Grugu was identified as the breeding area because many of the fish present were in the reproductive phase. Estimated growth parameters showed a maximum length (L?) of 35.18 cm, while exploitation rate of 0.28 was not considered excessive capture. Efforts to manage Channa striata were important to preserving biodiversity and ensuring the sustainability of Kawunganten Swamp ecosystem for future generations.

Population Dynamics of Mangrove Clam Pegophysema philippiana (Reeve, 1850) in Davao Region, Southeastern Mindanao, Philippines

Mangrove clam is the most sought-after bivalve in the Philippines due to its taste, size, and nutrition. Due to its economic importance, this paper aims to determine the population dynamics of mangrove clam P. philippiana harvested in gleaning sites in the Davao region using the FAO ICLARM Stock Assessment Tool (FISAT II). A total of 2493 clams collected from December 2018-December 2020 with sizes ranging from 14-84 mm SL and 2.2-178.6 g were classified according to size classes with 5 mm intervals. The length-weight relationship was computed and showed negative allometry (a = 0.002, b = 2.6205, R2 = 0.89). The estimated growth parameters using ELEFAN I was L∞ = 98.64 mm, K = 1.33 year-1, t0 = -1.07. Length converted catch curve routine estimated Z = 10.27year-1, M = 1.52 year-1, F = 8.75 year-1, E = 0.85, and backward extrapolation generated Lc50 = 34.83 mm SL which is below Lm = 65.76 mm SL. Recruitment patterns were highest during July and August 2020, and VPA showed a high F at 60 mm SL. Beverton and Holt Y/R analysis showed E0.5 = 0.328 and Emax = 0.551, yield isopleths derived from plotted Lc50/L∞ = 0.353 and E = 0.85 values were within quadrant D. The result revealed that Lm>Lc50 implies that mangrove clams gleaned in the area were small and immature. Moreover, F>Fopt and E>Emax and E>E0.5 suggest that mangrove clam fishery in the region was heavily exploited. The study concluded that the mangrove clam Pegophysema philippiana was overexploited and that a management strategy was needed in the Davao Region, Philippines.

Proposing a Bayesian hierarchical growth curve model (BHGCM) for tourism and hospitality research

Curvilinear growth trajectories of products/services are common in the tourism and hospitality industry. To fit nonlinear growth patterns with multilevel structures, this study proposed Bayesian hierarchical growth curve models (BHGCMs) in line with the increasing adoption of Bayesian analysis in tourism and hospitality academia. We provided the basic form of BHGCM along with its unique advantages. For an empirical test, this study applied several growth curves to approximate online reviews of U.S. hotels from August 2020 to January 2021. After selecting a Gompertz curve as a mean function of the GCM, a Bayesian hierarchical approach was employed to estimate growth parameters—namely, base and maximum volume of hotel reviews, inflection week, and relative growth rate—and identify their determinants. Our findings demonstrate the superiority of the proposed BHGCM in fitting the growth patterns of hotel reviews while revealing the effect of price and accumulated reviews on the parameters.

Life-history updates and stock assessment of Macrodon atricauda (teleostei, sciaenidae) from southern Brazil

The coastal demersal sciaenid fish Macrodon atricauda has been intensely fished since the 1950s along the southern Brazilian coast, resulting not only in decreasing abundance but also in long-term changes in its life history. Previous studies reported long-term increases in individual growth, size, and age at maturity. Using individual observed length-at-age, we did not observe the previously reported growth changes despite the increase in the mean length of older individuals from 1976 to 2023. These observed changes did not affect the estimation of the von Bertalanffy growth parameters, likely due to the species' faster growth at earlier ages and short lifespan. The length at first maturity remains smaller than the previous estimations from the 1970s but has not changed since the 2000s. The population dynamics assessed considering the updated life history parameter, including the size at maturity changes, showed that the species is not suffering from overfishing (i.e., F2019/FMSY = 0.17). However, the spawning stock biomass was estimated at low levels (i.e., SSB2019/SSBMSY = 0.59), indicating an overfished status highlighting the need for management action. The apparent stability of the life-history parameters observed for the species in the last four decades may be attributed to a combination of traits promoting resilience and recovery, including high fecundity, low age at maturity, fast individual growth, and a relatively short lifespan. In fact, despite decades of intense fishing, M. atricauda remains an important coastal demersal species in the landings of southern Brazil.

Hippocampal gray matter volume in young adulthood varies with adolescent alcohol use.

Adolescent substance use is linked with negative future outcomes (e.g., depression, anxiety, substance use disorder). Given that the brain undergoes significant maturation during adolescence, this developmental period may represent a time of particular vulnerability to substance use. Neuroimaging research has largely focused on heavy or binge patterns of substance use; thus, relatively less is known about the neural impact of a broader range of adolescent substance use. Characterizing the neural impact of a broader range of adolescent substance use may inform prevention and treatment efforts. The present study investigated relationships between adolescent substance use trajectories (i.e., alcohol, tobacco, and cannabis) and gray matter volume in young adulthood. Substance use was assessed in 1,594 participants at ages 11, 13, 16, and 19. Following the last assessment, 320 participants completed a single magnetic resonance imaging session to assess brain gray matter volume. Latent growth curve models were used to estimate growth parameters characterizing alcohol, tobacco, and cannabis use trajectories for each participant. These growth parameters (i.e., intercept, linear slope, and quadratic slope) were then used as predictors of gray matter volume. The gray matter volume of the hippocampus was positively associated with age 14 alcohol use (i.e., intercept) but not other trajectories (i.e., progression or acceleration) or substances (tobacco or cannabis). These results provide new insight into the neural impact of distinct adolescent alcohol, tobacco, and cannabis use trajectories, which may help to refine prevention and treatment efforts. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Age Readings and Assessment in Coastal Batoid Elasmobranchs from Small-Scale Size-Selective Fishery: The Importance of Data Comparability in Multi-Specific Assemblages

The large variation in vertebral shape and calcification observed among elasmobranch species prevents using a single method for enhancing growth bands and reading age. Further, estimating age and growth parameters can be difficult or impractical when samples are incomplete due to the bycatch of a size-selective fishery. Using a single and rapid method, age readings were obtained on the vertebrae of four batoid species, namely 53 individuals of Dasyatis pastinaca, 51 of Raja asterias, 15 of Torpedo marmorata, and 55 specimens of Torpedo torpedo, from the local small-scale trammel net fishery in the coastal waters (5–20 m depth) of the Central Tyrrhenian Sea during 2019–2021. Based on these data, a statistical routine was developed to obtain multiple estimates of age and growth parameters for incomplete samples due to size-selective fishing. The acceptable agreement between and within readers (intra and inter-reader disagreement < 5%) and the rate of increase in vertebral size with body size (differently ranked across species) demonstrated the consistency of the enhancing method. The parameters estimated by the Von Bertalanffy and Gompertz growth models matched the data available in the Mediterranean Sea for the species studied, with D. pastinaca, T. torpedo, and R. asterias showing the lowest (k = 0.05–0.12), intermediate (k = 0.112–0.19), and highest (k = 0.18–0.23) growth rates, respectively, in line with the life history traits of these species. Overall, the method proved effective both in delineating band pairs in vertebrae of different species and in reliably estimating the age and growth parameters of problematic samples due to size-selective fishing. The proposed method supports the collection of comparable demographic data from other areas where similar multi-specific assemblages are bycatch of size-selective fisheries impacting potential nursery areas and other essential habitats for elasmobranchs.

Life-history traits of an exploited skate: The short-tail yellownose skate Zearaja brevicaudata (Chondrichthyes: Rajiformes).

The short-tail yellownose skate, Zearaja brevicaudata, occurs off southern Brazil and along the Argentine continental shelf, including waters surrounding the Islas Malvinas and the upper slope, and is very frequently caught by trawl fisheries throughout its range. The age, growth, and maturity of Z. brevicaudata were investigated using 204 individuals collected by commercial fishing vessels from northern Argentina, between March 2016 and September 2019. Age was determined on a sample comprising vertebrae from 151 individuals, 60 males, ranging in size from 391 to 956 mm total length (TL), and 91 females, ranging in size from 324 to 1060 mm TL. Maximum ages determined for males and females were 22 and 29 years, respectively. A Bayesian framework was employed with a set of three candidate models to estimate growth parameters (von Bertalanffy, Gompertz, and Logistic). The von Bertalanffy model had the best fit, and the sexes showed undistinguishable growth parameters. For sexes combined, derived growth mean parameters (± S.D.) were L∞ = 1081 mm ± 64.34 mm, k = 0.09 ± 0.01 years-1, and L0 = 248 mm ± 23.52 mm. The age at maturity was estimated at 13.15 and 14.66 years for males and females, respectively.

Preliminary life history of the Critically Endangered bottlenose wedgefish Rhynchobatus australiae from Southeast Asia

The bottlenose wedgefish Rhynchobatus australiae has experienced substantial population declines throughout its range. However, there is a lack of life history information (age, growth, and maturity) available for this species to inform conservation and management efforts. A total of 48 R. australiae samples were purchased from 2 fishing ports in Singapore between July 2018-July 2019. Species identification was confirmed by mtDNA barcoding using the NADH2 region. Length of specimens ranged from 506-1645 mm total length (TL), and ages ranged from 0-11 yr. Multi-model analysis was used to estimate growth parameters using a Bayesian approach with informative priors. The von Bertalanffy model was the best fitting growth model for the combined sexes (L∞ = 2814 mm TL; L0 = 517 mm TL; k = 0.07 yr-1), for females only (L∞ = 3053 mm TL; L0 = 504 mm TL; k = 0.06 yr-1), and for males only (L∞ = 2741 mm TL; L0 = 497 mm TL; k = 0.07 yr-1). Preliminary results indicate that females and males may mature at different ages and lengths, with females (A50 = 3.25 yr; L50 = 1014 mm TL) matured younger and at smaller sizes, than males (A50 = 5.03 yr; L50 = 1197 mm TL). R. australiae has an estimated theoretical longevity of 40 and 47 yr for males and females, respectively. This study provides the first preliminary species-specific life history information for R. australiae, suggesting that this species in Southeast Asian waters is slow-growing. This information will further the biological knowledge available for this species and can be used to help design effective management and conservation measures.

Introducing exceptional growth mining-Analyzing the impact of soil characteristics on on-farm crop growth and yield variability.

Sustainable intensification of agriculture requires understanding of the effect of soil characteristics and nutrient supply on crop growth. As farms are increasing in size by acquiring small fields from various farmers, the soil characteristics and nutrient supply might be very different from field to field, while at the same time specific soil properties might limit the nutrient uptake. As a result, there might be a large number of heterogeneous reasons why crop growth varies significantly. New data analysis techniques can help to explain variability in crop growth among fields. This paper introduces Exceptional Growth Mining (EGM) as a first contribution. EGM instantiates the data mining framework Exceptional Model Mining (EMM) such that subgroups of fields can be found that grow exceptionally in terms of three growth parameters (high/low maximum growth, steep/flat linear growth and early/late midpoint of maximum growth). As second contribution, we apply EGM to a case study by analyzing the dataset of a potato farm in the south of the Netherlands. EGM consists of (i) estimating growth curves by applying nonlinear mixed models, (ii) investigating the correlation between the estimated growth parameters, and (iii) applying EMM on these growth curve parameters using a growth curve-specific quality measure. By applying EGM on the data of the potato farm, we obtain the following results: 1) the estimated growth curves represent the variability in potato tuber growth very well (R2 of 0.92), 2) the steepness of the growth curve has a strong correlation with the maximum growth and the midpoint of maximum growth, and the correlation between the midpoint of maximum growth and maximum growth is weak, 3) the subgroup analyses indicate that: high values of K correspond to high maxima; low values of K correspond to low maxima, steep growth curves', and a late midpoint of halfway growth; Mg influences the midpoint of the growth curve; values of B are higher on dry soils with high tuber growth, while low values of B are found on wet soils with high tuber growth; high values of Zn, Mn, and Fe are found in subgroups with low tuber weight, probably related to the soil's low pH. In summary, this paper introduces EGM to obtain understanding in crop response to soil properties and nutrient supply. In addition, EGM provides a way to analyze only small parts of a large dataset, such that the impact of soil factors on growth can be analyzed on a more detailed level than existing methods.

Combining spectral and texture feature of UAV image with plant height to improve LAI estimation of winter wheat at jointing stage.

Leaf area index (LAI) is a critical physiological and biochemical parameter that profoundly affects vegetation growth. Accurately estimating the LAI for winter wheat during jointing stage is particularly important for monitoring wheat growth status and optimizing variable fertilization decisions. Recently, unmanned aerial vehicle (UAV) data and machine/depth learning methods are widely used in crop growth parameter estimation. In traditional methods, vegetation indices (VI) and texture are usually to estimate LAI. Plant Height (PH) unlike them, contains information about the vertical structure of plants, which should be consider. Taking Xixingdian Township, Cangzhou City, Hebei Province, China as the research area in this paper, and four machine learning algorithms, namely, support vector machine(SVM), back propagation neural network (BPNN), random forest (RF), extreme gradient boosting (XGBoost), and two deep learning algorithms, namely, convolutional neural network (CNN) and long short-term memory neural network (LSTM), were applied to estimate LAI of winter wheat at jointing stage by integrating the spectral and texture features as well as the plant height information from UAV multispectral images. Initially, Digital Surface Model (DSM) and Digital Orthophoto Map (DOM) were generated. Subsequently, the PH, VI and texture features were extracted, and the texture indices (TI) was further constructed. The measured LAI on the ground were collected for the same period and calculated its Pearson correlation coefficient with PH, VI and TI to pick the feature variables with high correlation. The VI, TI, PH and fusion were considered as the independent features, and the sample set partitioning based on joint x-y distance (SPXY) method was used to divide the calibration set and validation set of samples. The ability of different inputs and algorithms to estimate winter wheat LAI were evaluated. The results showed that (1) The addition of PH as a feature variable significantly improved the accuracy of the LAI estimation, indicating that wheat plant height played a vital role as a supplementary parameter for LAI inversion modeling based on traditional indices; (2) The combination of texture features, including normalized difference texture indices (NDTI), difference texture indices (DTI), and ratio texture indices (RTI), substantially improved the correlation between texture features and LAI; Furthermore, multi-feature combinations of VI, TI, and PH exhibited superior capability in estimating LAI for winter wheat; (3) Six regression algorithms have achieved high accuracy in estimating LAI, among which the XGBoost algorithm estimated winter wheat LAI with the highest overall accuracy and best results, achieving the highest R2 (R2=0.88), the lowest RMSE (RMSE=0.69), and an RPD greater than 2 (RPD=2.54). This study provided compelling evidence that utilizing XGBoost and integrating spectral, texture, and plant height information extracted from UAV data can accurately monitor LAI during the jointing stage of winter wheat. The research results will provide a new perspective for accurate monitoring of crop parameters through remote sensing.

Exploring the effects of methodological choices on the estimation and biological interpretation of life history parameters for harbour porpoises in Norway and beyond.

This study investigates effects of subtle methodological choices on the estimation and biological interpretation of age, growth and reproductive parameters for harbour porpoises. The core analyses are based on a focal Norwegian data set built on samples from 134 harbour porpoises caught incidentally in gillnet fisheries along the Norwegian coast during autumn 2016 and spring 2017. Two contrasting practices for interpretation of seasonal and ontogenetic characteristics of tooth growth layer formation resulted in significant age differences among spring samples of young porpoises and for older animals across seasons. In turn, these differences affected estimates of age at maturity and asymptotic lengths, respectively. We also found significant differences in male age at maturity between two well-documented maturity criteria and between mathematical estimators of age at maturity for both sexes. Two different criteria for corpus albicans classification furthermore resulted in different patterns of ovarian corpora accumulation, which may affect some estimates of fecundity rates and contaminant loads. Both corpora accumulation patterns were also found in reanalysed data from German and Greenlandic porpoises. Based on tabulated overviews of methodological choices made in previous harbour porpoise studies, we argue that several of the issues mentioned above have wider relevance and may affect the validity of meta-analyses as a tool for estimating harbour porpoise sensitivity to extrinsic pressures. Differences in cause of death (COD) composition between data sets can have a similar effect. We demonstrate this in a meta-analysis of published harbour porpoise pregnancy rates, showing significantly higher values for trauma-killed samples compared to samples comprising mixed COD categories. COD also affected the estimated impacts of three previously analysed extrinsic predictors as well as an added predictor for vessel noise levels. We discuss the potential contributions of methodological, biological and anthropogenic factors in shaping observed regional differences in estimates of harbour porpoise life history parameters.

Growth and natural mortality of Maurolicus muelleri and Benthosema glaciale in the Northeast Atlantic Ocean

Mesopelagic fish are considered a possible future fisheries resource, but the biological sustainability of their potential exploitation has not yet been assessed. Sustainability should be evaluated at the population level, for which accurate stock-specific life-history parameters are required. Here, we use a length-based model to estimate life-history parameters related to growth and natural mortality, and their uncertainty, for the assessment of Northeast Atlantic populations of Maurolicus muelleri (Gmelin, 1789, Mueller’s Pearlside) and Benthosema glaciale (Reinhardt, 1837, glacier lantern fish). We compare three different approaches to estimate natural mortality rates and provide recommendations for future data collection and monitoring programs. For M. muelleri, we estimated an asymptotic length of 57.98 mm and a von Bertalanffy growth constant of 1.28 year−1, while for B. glaciale, we estimated an asymptotic length of 78.93 mm and a von Bertalanffy growth constant of 0.41 year−1. Estimates of natural mortality rates for M. muelleri were around 1.29 year−1 and 1.80 year−1 based on empirical formulae with the estimated growth parameters and maximum age, respectively, and around 1.51 year−1 with the length-converted catch curve method for B. glaciale estimates ranged between 0.5, 0.68, and 0.75 year−1, with the three respective methods. Due to limited data availability, the estimated uncertainty of the provided life-history parameters is large and should be considered in the evaluation of the sustainability of potential mesopelagic exploitation following the precautionary approach.

Failure location effects on slow crack growth parameter estimates

AbstractAs part of Roman Space Telescope component development, the slow crack growth (SCG) parameters of a glass were measured using constant stress rate testing of ASTM C1161 beams. Failures occurred on the faces, bevels and sides of the test specimens thereby providing an opportunity to examine the effects of failure location on SCG parameter estimates. Censoring of the data by location did not have a statistically significant effect on the slow crack growth parameter estimates. Linear and nonlinear fits were also made to various strength estimators. Nonlinear fits gave slightly lower estimates of the parameters. The main influence on parameter estimates was a low strength, infrequent flaw population. Exponential model parameters were estimated via swarm optimization.

Estimation of Agronomic Characters of Wheat Based on Variable Selection and Machine Learning Algorithms

Wheat is one of the most important food crops in the world, and its high and stable yield is of great significance for ensuring food security. Timely, non-destructive, and accurate monitoring of wheat growth information is of great significance for optimizing cultivation management, improving fertilizer utilization efficiency, and improving wheat yield and quality. Different color indices and vegetation indices were calculated based on the reflectance of the wheat canopy obtained by a UAV remote sensing platform equipped with a digital camera and a hyperspectral camera. Three variable-screening algorithms, namely competitive adaptive re-weighted sampling (CARS), iteratively retains informative variables (IRIVs), and the random forest (RF) algorithm, were used to screen the acquired indices, and then three regression algorithms, namely gradient boosting decision tree (GBDT), multiple linear regression (MLR), and random forest regression (RFR), were used to construct the monitoring models of wheat aboveground biomass (AGB) and leaf nitrogen content (LNC), respectively. The results showed that the three variable-screening algorithms demonstrated different performances for different growth indicators, with the optimal variable-screening algorithm for AGB being RF and the optimal variable-screening algorithm for LNC being CARS. In addition, using different variable-screening algorithms results in more vegetation indices being selected than color indices, and it can effectively avoid autocorrelation between variables input into the model. This study indicates that constructing a model through variable-screening algorithms can reduce redundant information input into the model and achieve a better estimation of growth parameters. A suitable combination of variable-screening algorithms and regression algorithms needs to be considered when constructing models for estimating crop growth parameters in the future.

Age estimates derived from hard parts of swordfish Xiphias gladius from the north-western Mediterranean Sea.

Accurate age estimates for fish are critical for properly understanding stock dynamics and health; this is particularly true for larger billfishes. Here we determined the most accurate aging estimation methods for swordfish (Xiphias gladius). We compared age estimates obtained from fin-ray sections, otolith sections, whole otoliths, and vertebrae collected from 87 swordfish off the east coast of Corsica. Age estimates from otolith sections were most consistently estimated across different readers (lowest average percentage error), followed by fin-ray sections, third vertebrae, and whole otoliths. When the age estimates from the otolith sections were compared with the other three age sclerochronological methods, we found the average percentage error to be lowest between the otolith section and fin-ray methods. However, age estimates from fin rays proved most useful for estimating swordfish younger than 6 years, as the fin ray-based age diverged from that of the otolith sections as the swordfish aged. Combining fin ray and otolith section techniques, we estimated the growth parameters of 1-12-year-old females (L∞ = 259.412, k = 0.113, t0 = -2.499) and 1-7-year-old males (L∞ = 175.543, k = 0.202, t0 = -2.239). We found that females grew significantly faster than males after 3 years and remained larger thereafter. Our calculated growth rates for this region of the north-western Mediterranean Sea were lower than those of the Atlantic, Pacific, and eastern Mediterranean Sea swordfish populations, and similar to growth rates recorded for the western Mediterranean Sea populations. Our study provides critical knowledge on biological-related parameters to serve as a guide for preserving the swordfish population in the Mediterranean Sea.