Akaike Information Criterion Weights Research Articles

Life Course Models of Child Maltreatment: Effects on General Psychopathology Outcomes in a Longitudinal Sample.

The study compared life course models (LCM; accumulation, recency, and sensitive period) of child maltreatment and general psychopathology in a large, national longitudinal data set of 1354 youth ages birth-16years (657 boys, 53.2% Black, 59.7% <$40K caregiver income). Previous research has supported the accumulation and recency models, albeit with shorter or fewer time periods of outcome measurement. We extend this work by modeling the impact of combined abuse and neglect allegations on a general psychopathology factor (dysregulation profile). Cross-sectional structural equation models were constructed using LCMs and tested across two-year periods from 4-16years old and compared using Akaike Information Criterion weights. The recency variable generally explained the greatest proportion of variance in psychopathology. Notably, maltreatment more proximal to the time of outcome measurement had the strongest effect, suggesting that more recent maltreatment may have stronger effects on general psychopathology. These results lend support to a recency effect of maltreatment on psychopathology outcomes, although substantive overlaps with the accumulation model are noted.

Quantification of P-glycoprotein function at the human blood-brain barrier using [18F]MC225 and PET

IntroductionP-glycoprotein (P-gp) is one of the most studied efflux transporters at the blood-brain barrier. It plays an important role in brain homeostasis by protecting the brain from a variety of endogenous and exogeneous substances. Changes in P-gp function are associated both with the onset of neuropsychiatric diseases, including Alzheimer’s disease and Parkinson’s disease, and with drug-resistance, for example in treatment-resistant depression. The most widely used approach to measure P-gp function in vivo is (R)-[11C]verapamil PET. (R)-[11C]verapamil is, however, an avid P-gp substrate, which complicates the use of this tracer to measure an increase in P-gp function as its baseline uptake is already very low. [18F]MC225 was developed to measure both increases and decreases in P-gp function.AimThe aim of this study was (1) to identify the pharmacokinetic model that best describes [18F]MC225 kinetics in the human brain and (2) to determine test-retest variability.MethodsFive (2 male, 3 female) of fourteen healthy subjects (8 male, 6 female, age 67 ± 5 years) were scanned twice (injected dose 201 ± 47 MBq) with a minimum interval of 2 weeks between scans. Each scanning session consisted of a 60-min dynamic [18F]MC225 scan with continuous arterial sampling. Whole brain grey matter data were fitted to a single tissue compartment model, and to reversible and irreversible two tissue-compartment models to obtain various outcome parameters (in particular the volume of distribution (VT), Ki, and the rate constants K1 and k2). In addition, a reversible two-tissue compartment model with fixed k3/k4 was included. The preferred model was selected based on the weighted Akaike Information Criterion (AIC) score. Test-retest variability (TRTV) was determined to assess reproducibility.ResultsSixty minutes post-injection, the parent fraction was 63.8 ± 4.0%. The reversible two tissue compartment model corrected for plasma metabolites with an estimated blood volume (VB) showed the highest AIC weight score of 34.3 ± 17.6%. The TRVT of the VT for [18F]MC225 PET scans was 28.3 ± 20.4% for the whole brain grey matter region using this preferred model.Conclusion[18F]MC225 VT, derived using a reversible two-tissue compartment model, is the preferred parameter to describe P-gp function in the human BBB. This outcome parameter has an average test-retest variability of 28%.Trial registrationEudraCT 2020-001564-28. Registered 25 May 2020.

Phthalate esters (PAEs) concentration pattern reflects dietary habitats (δ13C) in blood of Mediterranean loggerhead turtles (Caretta caretta)

Phthalic acid esters (PAEs) are classified as endocrine disruptors, but it remains unclear if they can enter the marine food-web and result in severe health effects for organisms. Loggerhead turtles (Caretta caretta) can be chronically exposed to PAEs by ingesting plastic debris, but no information is available about PAEs levels in blood, and how these concentrations are related to diet during different life stages. This paper investigated, for the first time, six PAEs in blood of 18 wild-caught Mediterranean loggerhead turtles throughout solid-phase extraction coupled with gas chromatography-ion trap/mass spectrometry. Stable isotope analyses of carbon and nitrogen were also performed to assess the resource use pattern of loggerhead turtles. DEHP (12–63 ng mL-1) and DBP (6–57 ng mL-1) were the most frequently represented PAEs, followed by DiBP, DMP, DEP and DOP. The total PAEs concentration was highest in three turtles (124–260 ng mL-1) whereas three other turtles had concentrations below the detection limit. PAEs were clustered in three groups according to concentration in all samples: DEHP in the first group, DBP, DEP, and DiBP in the second group, and DOP and DMP in the third group. The total phthalates concentration did not differ between large-sized (96.3 ± 86.0 ng mL-1) and small-sized (67.1 ± 34.2 ng mL-1) turtles (p < 0.001). However, DMP and DEP were found only in large-sized turtles and DiBP and DBP had higher concentrations in large-sized turtles. On the other hand, DEHP and DOP were found in both small- and large-sized turtles with similar concentrations, i.e. ~ 21.0/32.0 ng mL-1 and ~ 7.1/9.9 ng mL-1, respectively. Winsored robust models indicated that δ13C is a good predictor for DBP and DiBP concentrations (significant Akaike Information criterion weight, AICwt). Our results indicate that blood is a good matrix to evaluate acute exposure to PAEs in marine turtles. Moreover, this approach is here suggested as a useful tool to explain the internal dose of PAEs in term of dietary habits (δ13C), suggesting that all marine species at high trophic levels may be particularly exposed to PAEs, despite their different dietary habitats and levels of exposure.

Long-term reevaluation of spatially explicit models as a means for adaptive wildlife management.

We evaluated a 20-yr-old spatially explicit model (SEM) that predicted the spatial expansion of reintroduced Persian fallow deer in northern Israel. Using the current distribution of the deer and based on multi-model inference we assessed the accuracy of the SEM's prediction and what other factors affected the population's current distribution. If the SEM's projection was still valid, the leading model in the multi-model inference would include only the SEM's projection as an explanatory variable with a good fit. Different leading models would reveal key variables overlooked when the SEM was constructed or changes in the landscape unforeseen at the time, thus assisting adaptive management and decision-making. We assessed deer presence from camera trap encounter counts analyzed using N-mixture models. Models included various combinations of seven predictors: the 20-yr predictions of an SEM developed during the initial phases of the reintroduction, three key landscape characteristics on which the SEM was originally based but updated to reflect current conditions, distance from the release site, elevation, and the distribution of gray wolves (a predator that was absent from the area when the SEM was developed). Competing models were ranked by Akaike information criterion (AIC). Wolf distribution was the key predictor explaining the current deer distribution, appearing in all three leading models (∆AIC<2.0) and carrying 71% of the AIC weight (coefficient=-14.86 ± 5.6 [mean ±SE]). Of these three models, the SEM 20-yr prediction appeared in two, but explained only a fraction of the variance (coefficient=0.001 ± 0.08). The contribution of all other predictors was negligible. While the SEM failed to accurately predict the 20-yr deer distribution, the divergence between its projection and reality pointed to the probable cause (wolves) of this discrepancy. The inclusion of the SEM prediction in the leading models indicates that had the wolves not spread to the study area, the predictions would still have merit suggesting that long-term SEMs can potentially be robust. Long-term reevaluation of SEMs can be beneficial even if model projections fail, as the process can uncover the specific factors driving this failure, supporting adaptive management procedures.

A solution to minimum sample size for regressions.

Regressions and meta-regressions are widely used to estimate patterns and effect sizes in various disciplines. However, many biological and medical analyses use relatively low sample size (N), contributing to concerns on reproducibility. What is the minimum N to identify the most plausible data pattern using regressions? Statistical power analysis is often used to answer that question, but it has its own problems and logically should follow model selection to first identify the most plausible model. Here we make null, simple linear and quadratic data with different variances and effect sizes. We then sample and use information theoretic model selection to evaluate minimum N for regression models. We also evaluate the use of coefficient of determination (R2) for this purpose; it is widely used but not recommended. With very low variance, both false positives and false negatives occurred at N < 8, but data shape was always clearly identified at N ≥ 8. With high variance, accurate inference was stable at N ≥ 25. Those outcomes were consistent at different effect sizes. Akaike Information Criterion weights (AICc wi) were essential to clearly identify patterns (e.g., simple linear vs. null); R2 or adjusted R2 values were not useful. We conclude that a minimum N = 8 is informative given very little variance, but minimum N ≥ 25 is required for more variance. Alternative models are better compared using information theory indices such as AIC but not R2 or adjusted R2. Insufficient N and R2-based model selection apparently contribute to confusion and low reproducibility in various disciplines. To avoid those problems, we recommend that research based on regressions or meta-regressions use N ≥ 25.

Influence of dephasing on the Akaike-information- criterion distinguishing of quantum interference and Autler–Townes splitting in coherent systems

Electromagnetically induced transparency is a quantum interference (QI) effect in a coherent system, in which the similar but distinct effect of Autler–Townes splitting (ATS) without QI also happens concurrently. The Akaike information criterion (AIC) has been proven to be an efficient and objective method to discern them by evaluating their relative AIC weights for different Rabi frequencies of the coupling field. Here, we investigate in detail the influence of the dephasing effect on the AIC weights of QI and ATS, and present the transition among destructive QI, constructive QI, and ATS without QI by controlling the dephasing rates. By comparing the effects of different dephasing rates on the QI and ATS weights, we show that the field-phase-diffusion dephasing provides more feasibility than the atom-collision dephasing in control of QI and ATS. Therefore, precise and selective dephasing engineering can be realized by manipulating the linewidths and phase correlation of the fields. This indicates that various collision-related effects (e.g., collision-dephasing-induced coherences) can be experimentally studied using more controllable field-phase-diffusion dephasing instead of buffer-gas-controlling collision dephasing.

A Novel Prognostic Model Including Cytogenetic and Molecular Data in Patients with Primary and Post-Essential Thrombocythemia (ET)/Polycythemia Vera (PV) Myelofibrosis (MF)

Introduction: MF is a Philadelphia-negative myeloproliferative neoplasm (Ph-negative MPN) with an heterogeneous outcome. In 2009, Cervantes et al. published the International Prognostic Score System (IPSS) to better determine outcomes in this disease. In the last decade, several recurrently mutated genes have been described in MF, some of them associated with prognostic impact in survival. We propose a novel prognostic score that incorporates molecular and cytogenetic data in patients with MF.Methods: We analyzed clinical, cytogenetic and molecular data from 623 patients with a diagnosis of primary MF (N=445), post-PV MF (N=109) and post-ET MF (N=69). Data was extracted from medical records at time of sample collection for analysis. Mutation data was obtained by next-generation sequencing analysis, performed with either paired tumor-normal whole exome sequencing (N=46) or selected gene panel for genes associated with myeloid malignancies (N=577). The following 16 genes were analyzed in all 623 patients and were considered as the common denominator for analysis: ASXL1, CALR, DNMT3A, EZH2, FLT3, IDH1, IDH2, JAK2, KIT, KRAS, MPL, NRAS, RUNX1, TET2, TP53, WT1. RAS mutations were considered as oncogenic mutations in NRAS and/or KRAS. Molecular high risk (MHR) mutations were considered as mutations in any one of the 4 genes: ASXL1, EZH2, IDH1, IDH2 (SRSF2 mutations were not included since they were not evaluated in all cases). Cytogenetic data was stratified into 4 risk categories (based on Tam et al, Blood 2009): (1) Diploid; (2) Del(13q)/Del(20q)/Trisomy 9; (3) Abnormalities of chromosomes 5, 7, 17 and complex karyotype; (4) Other abnormalities. To develop the model, the data was split into a training dataset (N=434) and a test dataset (N=189). Variables initially included in the initial training model were those with a p-value<0.05 in the univariate analysis; subsequently, this initial model was internally validated with a bootstrap procedure for stepwise backwards selection and determination of variables to be included the final model. Score points were assigned based on the value of the hazard ratio (HR). The final model was externally validated in the test dataset. Akaike information criterion (AIC) was calculated on the final model and on the Dynamic IPSS (DIPSS) model on the full dataset, and the best model was considered the one with the lowest AIC value and the highest AIC weight.Results: In the training cohort, after a median follow-up of 30.8 months, there were 176 deaths (40.5%). The initial variables included in the multivariate Cox model were: age (>65 years), hemoglobin (<10 g/dL), white blood cell count (WBC; >25x109/L), peripheral blood blasts (>1%), presence of constitutional symptoms, sex (male vs female), platelet count (<100x109/L), type of JAK-STAT driver mutation (CALR vs JAK2 vs MPL vs Triple-negative), karyotype risk category, presence of MHR mutations and presence of RAS mutations. After bootstrapping, variables sex, peripheral blood blasts and WBC were excluded, and the final model with associated HRs is presented on table 1. Since most HRs were near 1, 1-point was given to each variable, with the exception of Triple-negative molecular status and RAS mutations (2 points each). We grouped patients based on similarity of HR into three risk categories: low (0-2 points; N=243), intermediate (3-4 points, N=141) and high (5+ points, N=50). These 3 groups were associated with significantly different survival outcomes (Figure 1): 3-years OS was 71% (95% CI 64-78%), 38% (95% CI 29-50%) and 18% (95% CI 9-37%) for low, intermediate and high risk groups (P=1.33e-13). Validating the model on the test dataset revealed significantly differences in OS among the 3 risk categories (Figure 2): 74% (95% CI 63-86%), 50% (95% CI 36-68%) and 16% (95% CI 6-39%) (P=1.42e-11). Analyzing the full dataset, the novel score model had a lower AIC than the DIPSS model (2713 vs 2722) and a higher AIC weight (0.99 vs. 0.01).Conclusion: Incorporating molecular data, including MHR mutations, karyotype and RAS mutations leads to the development of an improved prognostic model. Further validation of this model in other cohorts is necessary. The impact of novel therapies should be evaluated among the different risk categories. [Display omitted] DisclosuresRampal:Stemline: Research Funding; Celgene: Honoraria; Constellation: Research Funding; Incyte: Honoraria, Research Funding; Jazz: Consultancy, Honoraria. Verstovsek:Celgene: Membership on an entity's Board of Directors or advisory committees; Italfarmaco: Membership on an entity's Board of Directors or advisory committees; Novartis: Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Incyte: Consultancy.

Energy Expenditure Associated With Froude Number And Traditional Speed Measures In Children And Adolescents.

Energy expenditure or metabolic cost of walking is related to speed of locomotion. Leg length normalized speed measures may improve the ability to predict the metabolic cost of walking across the developmental age range. Metrics that improve the strength of the relationship between subject size, walking speed and its metabolic cost may enhance the ability to provide universal recommendations of walking behavior that may serve to improve health. PURPOSE: The purpose of this study was to compare the predictive value of speed in standard units (STD, m/s) to leg length (LL) normalized speed values: the Froude number (Fr) and Dimensionless Speed (DLS) on the metabolic cost of walking in persons 6-20 years of age. We hypothesize that Fr and DLS will improve the prediction of metabolic cost of bipedal transport in children and adolescents. METHODS: : 120 children and adolescents (50% female, 13.1 ± 4.2 years, height 155.6 ± 16.6 cm, mass 56.3 ± 22.3 kg, LL 74.1 ± 9.0 cm) participated in this study. O2 consumption was measured by a K4 portable metabolic system (COSMED). Individuals completed an incremental treadmill protocol 0.22-2.23 m/s, in 0.22m/s increments. The square root of the metabolic data (ml*kg-1*min-1) were compared with all ambulatory speed measures: STD, Fr and DLS. The Akaike information criterion (AIC) was used to compare the linear and quadratic regression models of each gait measure and square root of metabolic cost of walking, best models were selected based on AIC weights. RESULTS: : AIC values indicated that quadratic model provided a better fit for all models of speed and the metabolic cost of walking. AIC weights indicated that DLS (AIC = -489.59) was 315 and 2.45 * 106 times better than Fr (AIC = -478.08) and STD (AIC = -460.16), predicting the metabolic cost of locomotion. CONCLUSIONS: The DLS method of scaling gait speed to leg length offers significant improvements in the ability to predict metabolic cost of locomotion in children and adolescents ages 6-20. Improving the ability to predict metabolic cost from gait parameters may enhance the ability to provide public health recommendations based on objectively measured gait parameters. Funded by Eunice Kennedy Shriver National Institute of Child Health and Human Development: 1R21HD073807-01A1

Ecogeographical variation of 12 morphological traits withinPinus tabulaeformis: the effects of environmental factors and demographic histories

Aims More data are needed about how genetic variation (GV) and environmental factors influence phenotypic variation within the natural populations of long-lived species with broad geographic distributions. To fill this gap, we examined the correlations among environmental factors and phenotypic variation within and among 13 natural populations of Pinus tabulaeformis consisting of four demographically distinct groups within the entire distributional range. Methods Using the Akaike's Information Criterion (AIC) model, we measured 12 morphological traits and constructed alternative candidate models for the relationships between each morphological trait and key climatic variables and genetic groups. We then compared the AIC weight for each candidate model to identify the best approximating model for ecogeographical variation of P. tabulaeformis. The partitioning of variance was assessed subsequently by evaluating the independent variables of the selected best models using partial redundancy analysis. Important Findings Significant phenotypic variation of the morphological traits was observed both within individual populations and among populations. Variation partition analyses showed that most of the phenotypic variation was co-determined by both GV and climatic factors. GV accounted for the largest proportion of reproductive trait variation, whereas local key climatic factors (i.e. actual evapotranspiration, AET) accounted for the largest proportion of phenotypic variation in the remaining investigated traits. Our results indicate that both genetic divergence and key environmental factors affect the phenotypic variation observed among populations of this species, and that reproductive and vegetative traits adaptively respond differently with respect to local environmental conditions. This partitioning of factors can inform those making predictions about phenotypic variation in response to future changes in climatic conditions (particularly those affecting AET).

Model averaging and muddled multimodel inferences.

Three flawed practices associated with model averaging coefficients for predictor variables in regression models commonly occur when making multimodel inferences in analyses of ecological data. Model-averaged regression coefficients based on Akaike information criterion (AIC) weights have been recommended for addressing model uncertainty but they are not valid, interpretable estimates of partial effects for individual predictors when there is multicollinearity among the predictor variables. Multicollinearity implies that the scaling of units in the denominators of the regression coefficients may change across models such that neither the parameters nor their estimates have common scales, therefore averaging them makes no sense. The associated sums of AIC model weights recommended to assess relative importance of individual predictors are really a measure of relative importance of models, with little information about contributions by individual predictors compared to other measures of relative importance based on effects size or variance reduction. Sometimes the model-averaged regression coefficients for predictor variables are incorrectly used to make model-averaged predictions of the response variable when the models are not linear in the parameters. I demonstrate the issues with the first two practices using the college grade point average example extensively analyzed by Burnham and Anderson. I show how partial standard deviations of the predictor variables can be used to detect changing scales of their estimates with multicollinearity. Standardizing estimates based on partial standard deviations for their variables can be used to make the scaling of the estimates commensurate across models, a necessary but not sufficient condition for model averaging of the estimates to be sensible. A unimodal distribution of estimates and valid interpretation of individual parameters are additional requisite conditions. The standardized estimates or equivalently the t statistics on unstandardized estimates also can be used to provide more informative measures of relative importance than sums of AIC weights. Finally, I illustrate how seriously compromised statistical interpretations and predictions can be for all three of these flawed practices by critiquing their use in a recent species distribution modeling technique developed for predicting Greater Sage-Grouse (Centrocercus urophasianus) distribution in Colorado, USA. These model averaging issues are common in other ecological literature and ought to be discontinued if we are to make effective scientific contributions to ecological knowledge and conservation of natural resources.

Effects of landscape covariates on the distribution and detection probabilities of mammalian carnivores

Mammalian carnivores are affected by various anthropogenic disturbances near urban environments. Urban expansion and increased anthropogenic activity near and in preserved habitats may cause shifts in the current spatial distributions of those species. To predict the effects of future land-use changes on mammalian carnivores, we modeled their current occurrence across former Fort Ord Army Base as a function of urban proximity and road/trail density. We collected detection/nondetection data for domestic dogs, coyotes (Canis latrans), gray foxes (Urocyon cinereoargenteus), raccoons (Procyon lotor), striped skunks (Mephitis mephitis), and bobcats (Lynx rufus) using scent stations. We analyzed our data with likelihood-based occupancy modeling and used evidence ratios based on Akaike information criterion weights to infer the effect of each variable on occurrence and detection probabilities for each species. We used the estimated weighted model coefficients of the predictive variables to create current and future species distribution maps given proposed landscape changes in the study area. Domestic dogs were more likely to use areas closer to the urban edge, while gray foxes showed a preference toward inland areas. Detection probability was highest in areas closer to the urban edge for striped skunks and in areas with high road/trail densities for raccoons. Our results suggest that the distribution of domestic dogs will most likely expand with future development, while those of gray foxes will contract. We predict that future land-use changes outside of preserved areas will have an adverse effect on gray foxes within the protected areas.

Truth, models, model sets, AIC, and multimodel inference: A Bayesian perspective

Statistical inference begins with viewing data as realizations of stochastic processes. Mathematical models provide partial descriptions of these processes; inference is the process of using the data to obtain a more complete description of the stochastic processes. Wildlife and ecological scientists have become increasingly concerned with the conditional nature of model-based inference: what if the model is wrong? Over the last 2 decades, Akaike's Information Criterion (AIC) has been widely and increasingly used in wildlife statistics for 2 related purposes, first for model choice and second to quantify model uncertainty. We argue that for the second of these purposes, the Bayesian paradigm provides the natural framework for describing uncertainty associated with model choice and provides the most easily communicated basis for model weighting. Moreover, Bayesian arguments provide the sole justification for interpreting model weights (including AIC weights) as coherent (mathematically self consistent) model probabilities. This interpretation requires treating the model as an exact description of the data-generating mechanism. We discuss the implications of this assumption, and conclude that more emphasis is needed on model checking to provide confidence in the quality of inference. Published 2015. This article is a U.S. Government work and is in the public domain in the USA.

The relative importance of biotic and abiotic processes for structuring plant communities through time

Summary The question of the relative importance of biotic interactions versus abiotic drivers for structuring plant communities is highly debated but largely unresolved. Here, we investigate the relative importance of mean July air temperature, nitrogen (N) availability and direct plant interactions in determining the millennial‐scale population dynamics through the Holocene (10 700–5200 cal. years bp) for four temperate tree taxa in the Scottish Highlands. A variety of dynamic population models were fitted to our palaeoecological time‐series data to determine the mechanism(s) by which each driver affected the population biomass dynamics of Betula (birch), Pinus (pine), Alnus (alder) and Quercus (oak). Akaike information criterion weights identified the best model(s) for describing the relationship between each population and driver. The relative importance of these drivers was then assessed by the ability of each model to predict the observed population biomass dynamics. We also measured the change in goodness‐of‐fit of each model over time. We found that models of intra‐ and interspecific plant interactions described the plant population dynamics better than temperature‐ or N‐dependent population growth models over the 5000‐year study period. The best‐fitting models were constant over time for pine, alder and oak. However, the plant–N availability and plant–temperature models provided a progressively better fit to the birch data when temperatures rose and N availability declined, suggesting increasing importance of these abiotic factors coincident with changing conditions. Synthesis. Multiple mechanistic models were applied to palaeoecological data to infer the most likely processes driving millennial‐scale plant biomass dynamics in a woodland ecosystem. Direct plant interactions provided a better explanation for population biomass dynamics than growing season temperature or N availability over the full study period. The relative importance of all drivers we assessed here varied by species and – in the case of birch – over time in response to warming and reduced N availability.

Age and growth of the round stingray Urotrygon rogersi, a particularly fast-growing and short-lived elasmobranch.

We examined the age and growth of Urotrygon rogersi on the Colombian coast of the Eastern Tropical Pacific Ocean by directly estimating age using vertebral centra. We verified annual deposition of growth increments with marginal increment analysis. Eight growth curves were fitted to four data sets defined on the basis of the reproductive cycle (unadjusted or adjusted for age at first band) and size variables (disc width or total length). Model performance was evaluated using Akaike's Information Criterion (AIC), AIC weights and multi-model inference criteria. A two-phase growth function with adjusted age provided the best description of growth for females (based on five parameters, DW∞ = 20.1 cm, k = 0.22 yr–1) and males (based on four and five parameters, DW∞ = 15.5 cm, k = 0.65 yr–1). Median maturity of female and male U. rogersi is reached very fast (mean ± SE = 1.0 ± 0.1 year). This is the first age and growth study for a species of the genus Urotrygon and results indicate that U. rogersi attains a smaller maximum size and has a shorter lifespan and lower median age at maturity than species of closely related genera. These life history traits are in contrast with those typically reported for other elasmobranchs.

Predictive mapping of Meadow Pipit density using integrated remote sensing data and an atlas of vascular plants dataset

Capsule The highest densities of Meadow Pipits in Central Europe are found in lowland and upland wet meadows.Aims To create a large-scale predictive model of Meadow Pipit density.Methods We analysed factors affecting the density of the Meadows Pipit in Poland using data from 777 × 1 km study plots and a set of 22 environmental variables, including agriculture intensification and habitat-specific plant species as classifiers of meadow types. Predictors were selected using variation inflation factor, then related to species density data using generalized additive models.Results The best-supported model included 11 variables and was clearly better (Akaike information criterion weight = 0.47) than other models. The density of the Meadow Pipit reaches its highest levels on large areas of extensively used wet meadows as well as pastures where livestock graze and which show high photosynthetic activity in April.Conclusion Some aspects of the environment that were not identified from remote sensing data were vital for determining relatively high density. Conservation efforts for preserving Meadow Pipit populations should focus on maintaining wet meadows and extensively grazed pastures. Given the results, the Meadows Pipit may be classified as a good indicator of traditional agriculture.

Effects of isolation and environmental variables on fish community structure in the Brazilian Amazon Madeira-Purus interfluve

Due to the existence of terrestrial barriers to freshwater fish dispersion, it is believed that its distribution is strongly associated with historical factors related to the formation of the habitats they occupy. By the other hand, some studies reveal the influence of abiotic conditions (such as size of water bodies, pH, conductivity) on the composition of fish fauna occurring in small streams. This study aimed to investigate whether drainage basins, because catchment boundaries are potential barriers to fish dispersion, or the physical structure and physico-chemical characteristics of water have a greater influence on fish community structure in small streams. We sampled 22 streams belonging to five drainage basins in the Madeira-Purus interfluve. Fish were caught with dip nets and a small trawl, and data were simultaneously obtained on structural characteristics of the streams and physico-chemical characteristics of the water. Community composition was analyzed using Non-Metric Multidimensional Scaling (NMDS), and variables related to structural and physico-chemical characteristics were summarized by Principal Component Analysis (PCA). Two explanatory models relating faunal composition to environmental factors were constructed: the first using only continuous variables and the second including the drainage basin as a categorical variable. The Akaike Information Criterion (AIC) and AIC weight were used to select the best model. Although structural and physico-chemical variables significantly contributed to explaining faunal composition, the model including the drainage basin was clearly the better of the two models (more than 90% support in the data). The importance of drainage basins in structuring fish communities in streams may have significant consequences for conservation planning in these environments.

Large trees drive forest aboveground biomass variation in moist lowland forests across the tropics

AbstractAimLarge trees (d.b.h. ≥ 70 cm) store large amounts of biomass. Several studies suggest that large trees may be vulnerable to changing climate, potentially leading to declining forest biomass storage. Here we determine the importance of large trees for tropical forest biomass storage and explore which intrinsic (species trait) and extrinsic (environment) variables are associated with the density of large trees and forest biomass at continental and pan‐tropical scales.LocationPan‐tropical.MethodsAboveground biomass (AGB) was calculated for 120 intact lowland moist forest locations. Linear regression was used to calculate variation in AGB explained by the density of large trees. Akaike information criterion weights (AICc‐wi) were used to calculate averaged correlation coefficients for all possible multiple regression models between AGB/density of large trees and environmental and species trait variables correcting for spatial autocorrelation.ResultsDensity of large trees explained c. 70% of the variation in pan‐tropical AGB and was also responsible for significantly lower AGB in Neotropical [287.8 (mean) ± 105.0 (SD) Mg ha−1] versus Palaeotropical forests (Africa 418.3 ± 91.8 Mg ha−1; Asia 393.3 ± 109.3 Mg ha−1). Pan‐tropical variation in density of large trees and AGB was associated with soil coarseness (negative), soil fertility (positive), community wood density (positive) and dominance of wind dispersed species (positive), temperature in the coldest month (negative), temperature in the warmest month (negative) and rainfall in the wettest month (positive), but results were not always consistent among continents.Main conclusionsDensity of large trees and AGB were significantly associated with climatic variables, indicating that climate change will affect tropical forest biomass storage. Species trait composition will interact with these future biomass changes as they are also affected by a warmer climate. Given the importance of large trees for variation in AGB across the tropics, and their sensitivity to climate change, we emphasize the need for in‐depth analyses of the community dynamics of large trees.

Landscape Requirements of Caucasian Grouse (Lyrurus mlokosiewiczi) in Arasbaran Region, East Azerbaijan, Iran

Abstract The Caucasian Grouse (Lyrurus mlokosiewiczi) is of conservation concern because it has been declining at least since the 1980s and has disappeared from parts of its range. We quantified landscape composition and configuration around Caucasian Grouse lek sites and random points within the Arasbaran Region in northeastern Azerbaijan, Iran at multiple scales (200-, 500-, and 1,000-m radii circles). We developed averaged logistic regression models based on Akaike Information Criterion weights for each scale. Habitat suitability models indicated good discrimination based on the receiver-operating characteristic (ROC) criteria (three averaged-models had ROC values >0.8), indicating landscape pattern can explain distribution of leks reasonably well. Unconditional, model-averaged estimates and 95% confidence intervals for beta estimates of variables included within buffer-width models suggest leks in the 200-m buffer width are in areas with lower standard deviation in patch area. Lek sites in the 500-m b...

Modeling the influence ofPeromyscus leucopusbody mass, sex, and habitat on immatureDermacentor variabilisburden

Immature (larvae and nymph) tick burden on rodents is an important determinant of adult tick population size and understanding infectious disease dynamics. The objective of this research was to build a descriptive model for immature Dermacentor variabilis burden on Peromyscus leucopus. Mice were live-trapped on two permanent grids in an old field and an early successional forest every other month between April and October, 2006-2009. Negative binomial regression was used to examine the association between immature D. variabilis burden and the host related variables of host habitat, body mass, and/or sex. The model containing all three variables had the lowest Akaike's Information Criterion (AIC), corrected AIC (AICc), and greatest AICc weight. Immature D. variabilis burden was positively associated with mice with higher body mass, male mice, and those captured in the field habitat. These data are consistent with studies from other tick-rodent systems and suggest that single factor models do not describe host burden. Variables other than those that are related to the host may also be important in describing the tick burden on rodents. The next step is to examine variables that affect tick development rate and questing behavior.

Variation in elk response to roads by season, sex, and road type

AbstractDespite the near universal recognition that roads negatively affect wildlife, the mechanisms that elicit animal responses to roads are often ambiguous or poorly understood. We conducted a multi‐year, multi‐season study to assess the relative influence of roads on elk (Cervus elaphus) in a human‐dominated landscape in South Dakota. We evaluated the effects of habitat covariates including security cover, forage quality, distance to roads (primary, secondary, and tertiary), and visibility from roads at the home range scale. We radio‐collared 28 elk (21 adult females and 7 adult males) and calculated seasonal (winter, spring, summer, and autumn) utilization distributions (UDs). We assigned habitat covariates to use percentiles within the UDs (1% increments; from 1 to 98 percentiles) and used spatially explicit mixed linear regression to model the relationship between use percentile and habitat covariates. For each season and sex, we evaluated 15 candidate models and used Akaike's Information Criterion weights (ωi) to identify top‐ranking models. We plotted influential coefficients from these models with 95% confidence intervals to examine the magnitude of effects. Our analysis revealed fundamental differences in response to roads, by road type, between sexes, and across seasons. Male elk established home ranges near roads devoid of vehicle traffic in winter, spring, and autumn. In summer, coinciding with peak vehicle traffic levels, male elk reduced their use of habitat that was both visible from and close to primary roads. Female elk subherds similarly responded to primary roads in spring and autumn, during times of year when they were calving and mating, respectively. In spring and summer, female elk subherds selected habitat near roads that were closed to vehicle traffic. Forage quality and security cover were influential in the periphery (&gt;50th use percentile) of elk home ranges, whereas road covariates were more influential towards the core of elk home ranges. This analysis further demonstrates the utility of visibility from road metrics and suggests that the retention of vegetation structures that screen visibility potential from roads could be important components of elk management strategies. © 2012 The Wildlife Society.