Life Table Data: Field-based, partial life table data for immature stages of Bemisia tabaci on cotton in Maricopa, Arizona, USA. Data were generated on approximately 200 individual insects per cohort with 2-5 cohorts per year for a total of 44 cohorts between 1997 and 2010. Data provide the marginal, stage-specific rates of mortality for eggs, and 1st, 2nd, 3rd, and 4th instar nymphs. Mortality is characterized as caused by inviability (eggs only), dislodgement, predation, parasitism and unknown. Detailed methods can be found in Naranjo and Ellsworth 2005 (Entomologia Experimentalis et Applicata 116(2): 93-108). The method takes advantage of the sessile nature of immature stages of this insect. Briefly, an observer follows individual eggs or settled first instar nymphs from natural populations on the underside of cotton leaves in the field with a hand lens and determines causes of death for each individual over time. Approximately 200 individual eggs and nymphs are observed for each cohort. Separately, densities of eggs and nymphs are monitored with standard methods (Naranjo and Flint 1994, Environmental Entomology 23: 254-266; Naranjo and Flint 1995, Environmental Entomology 24: 261-270) on a weekly basis.
Matrix Model Data: Life table data were used to provide parameters for population matrix models. Matrix models contain information about stage-specific rates for development, survival and reproduction. The model can be used to estimate overall population growth rate and can also be analyzed to determine which life stages contribute the most to changes in growth rates. Resources in this dataset:Resource Title: Matrix model data from Naranjo, S.E. (2017) Retrospective analysis of a classical biological control program. Journal of Applied Ecology. File Name: MatrixModelData.xlsxResource Description: Life table data were used to provide parameters for population matrix models. Matrix models contain information about stage-specific rates for development, survival and reproduction. The model can be used to estimate overall population growth rate and can also be analyzed to determine which life stages contribute the most to changes in growth rates. Resource Title: Data Dictionary: Life table data. File Name: DataDictionary_LifeTableData.csvResource Title: Life table data from Naranjo, S.E. (2017) Retrospective analysis of a classical biological control program. Journal of Applied Ecology. File Name: LifeTableData.xlsxResource Description: Field-based, partial life table data for immature stages of Bemisia tabaci on cotton in Maricopa, Arizona, USA. Data were generated on approximately 200 individual insects per cohort with 2-5 cohorts per years for a total of 44 cohorts between 1997 and 2010. Data provide the marginal, stage-specific rates of mortality for eggs, and 1st, 2nd, 3rd, and 4th instar nymphs. Mortality is characterized as caused by inviability (eggs only), dislodgement, predation, parasitism and unknown. Detailed methods can be found in Naranjo and Ellsworth 2005 (Entomologia, Experimentalis et Applicata 116: 93-108). The method takes advantage of the sessile nature of immature stages of this insect. Briefly, an observer follows individual eggs or settled first instar nymphs from natural populations on the underside of cotton leaves in the field with a hand lens and determines causes of death for each individual over time. Approximately 200 individual eggs and nymphs are observed for each cohort. Separately, densities of eggs and nymphs are monitored with standard methods (Naranjo and Flint 1994, Environmental Entomology 23: 254-266; Naranjo and Flint 1995, Environmental Entomology 24: 261-270) on a weekly basis. Resource Title: Life table data from Naranjo, S.E. (2017) Retrospective analysis of a classical biological control program. Journal of Applied Ecology. File Name: LifeTableData.csvResource Description: CSV version of the data. Field-based, partial life table data for immature stages of Bemisia tabaci on cotton in Maricopa, Arizona, USA. Data were generated on approximately 200 individual insects per cohort with 2-5 cohorts per years for a total of 44 cohorts between 1997 and 2010. Data provide the marginal, stage-specific rates of mortality for eggs, and 1st, 2nd, 3rd, and 4th instar nymphs. Mortality is characterized as caused by inviability (eggs only), dislodgement, predation, parasitism and unknown. Detailed methods can be found in Naranjo and Ellsworth 2005 (Entomologia, Experimentalis et Applicata 116: 93-108). The method takes advantage of the sessile nature of immature stages of this insect. Briefly, an observer follows individual eggs or settled first instar nymphs from natural populations on the underside of cotton leaves in the field with a hand lens and determines causes of death for each individual over time. Approximately 200 individual eggs and nymphs are observed for each cohort. Separately, densities of eggs and nymphs are monitored with standard methods (Naranjo and Flint 1994, Environmental Entomology 23: 254-266; Naranjo and Flint 1995, Environmental Entomology 24: 261-270) on a weekly basis.

Life-table sources, parameters, and results for 50 mammal populations.

We use Monte Carlo methods which draw parameters from Bayesian posterior distributions to generate a distribution of population size estimates and trajectories, thus giving managers a fuller accounting of the uncertainty in the population status. We then propagate this population estimate and its associated uncertainty into a model using Monte Carlo methods to assess the impact of fishing bycatch on the species. We show that the population is below the recovery goal of 3,000 adults. The current total population estimate (including juveniles) is approximately 10,000 fish. Our model finds that fishing bycatch pressure reduces an otherwise assumed stable population by a median value of 0.4% per year, which could impede the recovery of the species. Fisheries bycatch is only one of many threats this population faces, and future work is needed to assess how other threats, such as spawning habitat alteration through dams and water diversions, may affect this population’s trajectory. The framework presented here is suitable for further data integration or modular expansion to incorporate the cumulative effects of challenges facing green sturgeon recovery. Methods We assessed the number of spawning green sturgeon adults in annual surveys of the Sacramento River, CA, USA from the Irvine Finch Boat Ramp (river kilometer 320, just west of Chico) up to Redding (river kilometer 480) (Fig. 1). Acoustic tag data and egg mat studies have confirmed that this is the extent of the spawning grounds (Poytress et al. 2013; Thomas et al. 2014). We surveyed any site along that section of the river with depths greater than 5 m (Erickson et al. 2002). Generally, we see green sturgeon in approximately 40 sites. This section of the river provides the vast majority (effectively all) of the southern DPS green sturgeon spawning locations. 2.3 Spawner survey A detailed description of the methods is published by Mora et al. (2015) and is only briefly described here. The survey has taken place continuously since 2010. There are three phases of the survey conducted over three weeks in mid-June. In phase one, a survey crew drifts downstream over the deepest parts of the channel with a depth sounder. The crew contour maps areas of the river with depths greater than 5 m using a sonar system. The survey generally finds approximately 70 areas with a depth over 5 m within the study area (Fig. 1). The crew marks locations with spawner observations in the last 5 years for an automatic revisit in phase three. In phase two, the crew uses a Dual frequency IDentification SONar (DIDSON; Sound Metrics, Bellevue, Washington) video camera to scan for green sturgeon during 3 passes at sites without spawners in the previous 5 years. In phase three, the crew visits all sites where sturgeon have been seen in the past 5 years as well as any new ones added during phase two. At each site, the crew makes 7 passes recording DIDSON footage. The DIDSON footage from phase three is reviewed in random order three times and counts are combined into an estimate of the number of sturgeon at each site location. The sum of counts from all sites is the total number of spawners observed. 2.4 Life table, IPM, and sensitivity analysis 2.4.1 Literature parameters Both the IPM and life table models need some parameters describing demographics, behavior, and physiology. We took a subset of these parameters directly from the literature (Appendix S1). All these parameters are for the northern DPS green sturgeon as similar data is unavailable for the southern population. 3.4.2 Length vs. age relationship We used the age vs length data for southern DPS green sturgeon on the Sacramento River from supplement 1 of Ulaski and Quist (2021). We fit these data with Bayesian regression in R using JAGS (packages used rjags, purr, ggplot, dplyr, patchwork, viridis, minpack.lm, ggextra, mcmcplots, and furrr) (Plummer 2003; R Core Team 2015; RStudio Team 2015; Wickham 2016b; Elzhov et al. 2016; Wickham 2016a; Curtis 2018; Wickham et al. 2018; Garnier 2018; Attali & Baker 2019; Pedersen 2019; Vaughan & Dancho 2021). The model and priors are as follows:

L ~ normal(Μ_L, Τ) Μ_L = L_∞ (1-e^((-k(A-t_0)))) L_(∞ ) ~ normal(μ = 190 cm, τ = 0.05 (1/cm)) k ~ gamma(ϕ = 1, θ = 0.2 (1/yrs)) t_0 ~ normal(μ = -3 yrs, τ = 0.001 (1/yrs)) Τ ~ gamma(ϕ = 0.001, θ = 0.001 (1/cm))

Eq. 1

where L is the length, ML is the mean of the length distribution, T is the precision of the length distribution, L∞ is the asymptotic length, k is the growth coefficient, A is age in years, and t0 is agee at zero length. Priors are loosely informed by data from northern green sturgeon (Adair et al. 1983; Farr et al. 2002). We ran three MCMC chains with 1000 adaptation steps and 20000 burn-in steps and saved 10000 samples per chain at 90% thinning. Each chain had random starting values based on draws from the prior distributions. All chains converged based on visual inspection or running means. We compared these results to fits for the northern DPS of green sturgeon as previous population estimates used that data (Beamesderfer et al. 2007; Mora et al. 2018). 3.4.3 Annual survival Appendix S1 presents an estimate of mortality based on a catch curve analysis from a fishery in the Columbia Estuary. We used the telemetry data from the sturgeon on our system to calculate annual mortality. We then used these two values to bookend the estimate of annual mortality in the life table model and IPM. We used data from the BARD for all green sturgeon tags from 2007-2018. We only used data where length was labeled as either total length or fork length. We converted all lengths to fork lengths and all lengths reported in this manuscript are fork lengths. We used the mean parameters from Eq. 1 to convert the lengths to ages. We grouped the data in 5-year bins to reduce noise. Instantaneous mortality is equal to the slope of the change in counts with age after natural-log transformation. We calculated the slope of the descending arm and converted it from instantaneous mortality to annual survival using (annual survival) = exp(-instantaneous mortality) (Ricker 1975). Subsequent calculations involving survival drew mortality from a uniform distribution over the range between the Columbia River and this estimate. 3.4.4 Probability of being an adult Rather than using published ages of maturity or the maturity curve implied by the Beamesderfert al. (2007) cohort model, we based the timing of maturity on data specific to the southern population. We calculated the probability that fish of a certain length are adults (i.e. potential spawners) using the same raw data set from the BARD. We flagged fish as adults if they were marked as “mature”, “adult,” or “eggs” (meaning they were caught with eggs) or if detections were above river kilometer 320 (the bottom of the spawning ground) (note the BARD uses a different river kilometer 0, thus river kilometer 320 equates to 410 in the BARD). Only tagging-year records were used so that length and maturity data were contemporaneous. We grouped individuals by sex into females and others (males and unknown). We obtained two separate estimates of the probability of maturation with length using Bayesian logistic regression. The first estimate was a sex-specific hierarchical model fit divided between females and others in which the sexes shared a common slope but had separate intercepts. We used the female parameters from this model in the sensitivity analysis calculation because that analysis needed fecundity. The second estimate was a fit with all the data for use in the population estimate, which needed the total number of spawners. The models are as follows:

P ~ Bernoulli(Μ_A) Μ_A = (1+e^(-(b_0+b_1 L)))^(-1) b_(1 ) ~ normal(μ = 0 (1/cm), τ = 10^(-12) cm) b_0 ~ normal(μ = 0, τ = 10^(-12))

Eq. 2

where P is the probability of being an adult, MA is the mean of the probability distribution, L is the length, b0 is the intercept and b1 is the slope. Thus, in the hierarchical model, females and others share the b1 term but have separate b0 terms. We used broad normal priors (Eq. 2). We ran three chains with 1000 adaptation steps and 20000 burn-in steps and saved 10000 samples at 90% thinning per chain. Each chain had random starting values based on draws from the prior distributions. All chains converged. We compared these results to fits for the northern population of green sturgeon as previous population estimates used that data (Beamesderfer et al. 2007; Mora et al. 2018). 3.4.5 Spawning interval distribution The population estimate portion of the IPM needs the distribution of spawning intervals for all adults and the sensitivity analysis requires the average spawning interval of females. To calculate these data, we took all detections from the BARD above river kilometer 320 for months during the spawning season (March - September). We removed any detections from the same year the fish was tagged as well as any detections for fish without a detected outmigration between upstream records. For each fish, we then found both the interval between tagging and the first return and, if available, the interval between the first and second return. We divided these into two groups (all fish and females). We then constructed a distribution showing the fraction of fish that have a return interval greater than each interval and calculated the average return interval. We compared these results to fits for the northern population of green sturgeon as previous population estimates used that data (Beamesderfer et al. 2007; Mora et al. 2018). 3.4.6 Calculate the fraction of the population that is adults and spawners We then sampled from the estimated distributions of survival, maturation, and spawning interval described above to make 10,000 life tables (the average parameter values converged at approximately 8,000 samples) from which we calculated the fraction of the population that is

Population parameters of Lobiopa insularis fed ripe strawberry fruits.

A characteristic equation of an annual plant matrix model and solutions of the implicit differentiation of this characteristic equation with respect to the lower-level parameters that make up matrix elements.

Land use is likely to be a key driver of population dynamics of species inhabiting anthropogenic landscapes, such as farmlands. Understanding the relationships between land use and variation in population growth rates is therefore critical for the management of many farmland species. Using 24 years of data of a declining farmland bird in an integrated population model, we examined how spatiotemporal variation in land use (defined as habitats with "Short" and "Tall" ground vegetation during the breeding season) and habitat‐specific demographic parameters relates to variation in population growth taking into account individual movements between habitats. We also evaluated contributions to population growth using transient life table response experiments which gives information on contribution of past variation of parameters and real‐time elasticities which suggests future scenarios to change growth rates. LTRE analyses revealed a clear contribution of Short habitats to the annual variation in population growth rate that was mostly due to fledgling recruitment, whereas there was no evidence for a contribution of Tall habitats. Only 18% of the variation in population growth was explained by the modeled local demography, the remaining variation being explained by apparent immigration (i.e., the residual variation). We discuss potential biological and methodological reasons for high contributions of apparent immigration in open populations. In line with LTRE analysis, real‐time elasticity analysis revealed that demographic parameters linked to Short habitats had a stronger potential to influence population growth rate than those of Tall habitats. Most particularly, an increase of the proportion of Short sites occupied by Old breeders could have a distinct positive impact on population growth. High‐quality Short habitats such as grazed pastures have been declining in southern Sweden. Converting low‐quality to high‐quality habitats could therefore change the present negative population trend of this, and other species with similar habitat requirements.

Tables of life-history parameters associated with population models depicted in Figs. 1 and 2.

Daughter life table analysis data presented by parental convergence status and experimental trial (individually and averaged). Although the effect of parental convergence status on R0 and r (but not Tc) are inconsistent across two experimental trials, the average effect was similar. Abbreviations: R0, cumulative reproductive rate; Tc, generation time; r, intrinsic rate of increase. (XLSX)

There are two Tables. Table S1 Concentrations and characteristic parameters of REEs in paddy soils; and Table S2 Concentrations and characteristic parameters of REEs in potential sources.

Candidate covariates for each generalized linear model (GLM) of key parameters in the white pine-Clark’s nutcracker analysis (Fig 2): q = per-minute probability of non-detection, σ = scale parameter of the half-normal distribution, λN = expected abundance of nutcrackers, and λW = expected value of a time-varying whitebark seed proxy (Table 3).

For older patients with displaced femoral neck fractures, in which primary osteosynthesis is usually not indicated, there are three primary prosthetic options—bipolar hemiarthroplasty (BHA), single-bearing total hip arthroplasty (SB-THA), and dual-mobility THA (DM-THA). However, the optimal choice for managing displaced femoral neck fractures remains controversial. Accordingly, this study aimed to evaluate the cost-effectiveness of BHA, SB-THA, and DM-THA in active older patients with displaced femoral neck fractures. A decision tree combined with a Markov model was employed to analyze the cost and quality-adjusted life years (QALYs) of BHA, SB-THA, and DM-THA for the management in the Japanese healthcare system. By simulating the five-year trajectory of a 75-year-old woman treated for a displaced femoral neck fracture, the cost-effectiveness of the three surgical options was evaluated. One-way sensitivity analysis and probabilistic sensitivity analysis (PSA) were used to assess parameter uncertainty. Additionally, two scenario analyses were conducted for other settings. The treatment was considered to be cost-effective when the incremental cost-effectiveness ratio (ICER) was below the 5,000,000 yen/QALY threshold. Compared with BHA, SB-THA exhibited higher costs but greater health benefits, resulting in an ICER of 1,499,440 yen/QALY. DM-THA offered additional health benefits compared with SB-THA, with an ICER of 4,145,777 yen/QALY. One-way sensitivity analysis revealed some influential parameters. PSA indicated that the probability of DM-THA, SB-THA, and BHA being cost-effective was 40.1%, 38.5%, and 21.4%, respectively. SB-THA was more cost-effective than BHA in patients aged 65–85 years, while DM-THA was more cost-effective than SB-THA in patients aged 65–75 years. The results suggest that SB-THA is a cost-effective alternative to BHA for displaced femoral neck fractures in active older patients, whereas DM-THA is more cost-effective than SB-THA in relatively younger patients. It is, therefore, recommended that orthopedic surgeons select the most appropriate surgical option based on the individual patient’s physiological age.

The relevant four principal components (PCs) are given in bold font. Without the present method, only PCs #1 - #3 with eigenvalues > 1 [11,12] could be validly retained. The set of three principal allowed to show that all different pain measures shared an important common source of variance (PC1) pain evoked by cold stimuli, with or without sensitization by topical menthol application, by blunt pressure or by electrical stimuli (5 Hz sine waves) shared a common source of variance (PC2), and a further common source of variance e was shared by pain evoked by heat stimuli, with or without sensitization by topical capsaicin application, or by punctate mechanical pressure. However, with applying the here reported method, PC4 can now be also be retained, which singles out heat pain corresponding to the different pathophysiology underlying heat perception.Component loadings for a previously reported real-life example of a principal component analysis performed on the intercorrelation matrix among eight pain threshold measurements ([3]; for comparison, see Table 2 in that publication).

RR = risk ratio; Asmp = Assumption; SSA-Sub Saharan and Southern Africa, SEA-South and South-East Asia;1Rates of access reported in Gomes et al. [2];2Parameter values varied by 50% above and below the base case value;3Parameter values varied by 50% below the base case value only, to reflect lower treatment failure rates in Gomes et al. [2];495% confidence interval reported in Gomes et al. [2];5Parameter values varied between estimates reported in the two sources;6Artemether is used alongside quinine in SEA, hence the cost of first-line parenteral antimalarial treatment in this region was assumed to be an average of the cost of quinine treatment and artemether treatment [10];7National policies for treatment of uncomplicated falciparum malaria vary by country, Average costs were calculated for each region based on region-wide antimalarial drug policy as reported in the 2008 WMR [10];8Region-specific life tables were used to estimate life expectancy conditional on survival. Japanese life tables were used within a sensitivity analysis.

Here, LUAD patient tumor samples are divided into low and high infiltration groups for immune cell infiltration, low and high expression groups for gene and miRNA expression, and low and high age groups for LUAD cancer patient age. Reference values used for multivariate Cox-proportional hazard model analysis for each factor are shown in the table.