Analysis of fine rooting below skid trails using linear and generalized additive models

2009 ◽  
Vol 39 (11) ◽  
pp. 2047-2058 ◽  
Author(s):  
Jürgen Schäffer ◽  
Klaus von Wilpert ◽  
Edgar Kublin
Keyword(s):  
Linear Models ◽  
Fine Root ◽  
Formal Model ◽  
Generalized Additive Models ◽  
Response Surfaces ◽  
Additive Models ◽  
Compacted Soils ◽  
Continuous Response ◽  
Model Following ◽  
Depth Gradients

Soil compaction caused by forest machinery changes the basic conditions for root propagation below skid trails. In consequence, lower fine-root densities have to be expected under wheel tracks compared with other skid trail strata that experience no direct traffic. Explorative data analysis of fine-root densities below a skid trail revealed that the fundamental assumptions for linear modelling were violated. Using a generalized linear model following a Poisson distribution with a log link function for the predictor variables together with an exponential covariance function to cope with spatial autocorrelation, the formal model criteria were met. In contrast to the linear models, generalized additive models provide flexible surface estimators that enable us to model continuous response surfaces. In addition, generalized additive models allow for the calculation of confidence intervals for the estimated density surface and for the use of inferential statistics, such as comparisons between depth gradients of fine rooting at distinct transect locations or depth layers. These model characteristics improve the possibility to recognize differences and to evaluate fine-root disturbances below skid trails without integrating uncertain strata information. They also enhance the options for determining the duration of time that is necessary to restore the rooting capacity on formerly compacted soils.

scholarly journals The Evolution of Data Sharing Practices in the Psychological Literature

2021 ◽  
Author(s):  
Judith Neve ◽  
Guillaume A Rousselet
Keyword(s):  
Data Sharing ◽  
Linear Models ◽  
Generalized Additive Models ◽  
Additive Models ◽  
Future Research ◽  
Science Data ◽  
Editorial Policies ◽  
Psychological Science ◽  
The Impact

Sharing data has many benefits. However, data sharing rates remain low, for the most part well below 50%. A variety of interventions encouraging data sharing have been proposed. We focus here on editorial policies. Kidwell et al. (2016) assessed the impact of the introduction of badges in Psychological Science; Hardwicke et al. (2018) assessed the impact of Cognition’s mandatory data sharing policy. Both studies found policies to improve data sharing practices, but only assessed the impact of the policy for up to 25 months after its implementation. We examined the effect of these policies over a longer term by reusing their data and collecting a follow-up sample including articles published up until December 31st, 2019. We fit generalized additive models as these allow for a flexible assessment of the effect of time, in particular to identify non-linear changes in the trend. These models were compared to generalized linear models to examine whether the non-linearity is needed. Descriptive results and the outputs from generalized additive and linear models were coherent with previous findings: following the policies in Cognition and Psychological Science, data sharing statement rates increased immediately and continued to increase beyond the timeframes examined previously, until reaching close to 100%. In Clinical Psychological Science, data sharing statement rates started to increase only two years following the implementation of badges. Reusability rates jumped from close to 0% to around 50% but did not show changes within the pre-policy nor the post-policy timeframes. Journals that did not implement a policy showed no change in data sharing rates or reusability over time. There was variability across journals in the levels of increase, so we suggest future research should examine a larger number of policies to draw conclusions about their efficacy. We also encourage future research to investigate the barriers to data sharing specific to psychology subfields to identify the best interventions to tackle them.

scholarly journals An investigation of human vs. technology-induced variation in catchability for a selection of European fishing fleets

2011 ◽  
Vol 68 (10) ◽  
pp. 2252-2263 ◽  
Author(s):  
Stéphanie Mahévas ◽  
Youen Vermard ◽  
Trevor Hutton ◽  
Ane Iriondo ◽  
Angélique Jadaud ◽  
...  
Keyword(s):  
Linear Models ◽  
Generalized Additive Models ◽  
Technical Information ◽  
Fishing Effort ◽  
Additive Models ◽  
Induced Variation ◽  
Fishing Fleets ◽  
The Impact ◽  
Selection Of ◽  
Experience Effect

Abstract Mahévas, S., Vermard, Y., Hutton, T., Iriondo, A., Jadaud, A., Maravelias, C. D., Punzón, A., Sacchi, J., Tidd, A., Tsitsika, E., Marchal, P., Goascoz, N., Mortreux, S., and Roos, D. 2011. An investigation of human vs. technology-induced variation in catchability for a selection of European fishing fleets. – ICES Journal of Marine Science, 68: 2252–2263. The impact of the fishing effort exerted by a vessel on a population depends on catchability, which depends on population accessibility and fishing power. The work investigated whether the variation in fishing power could be the result of the technical characteristics of a vessel and/or its gear or whether it is a reflection of inter-vessel differences not accounted for by the technical attributes. These inter-vessel differences could be indicative of a skipper/crew experience effect. To improve understanding of the relationships, landings per unit effort (lpue) from logbooks and technical information on vessels and gears (collected during interviews) were used to identify variables that explained variations in fishing power. The analysis was undertaken by applying a combination of generalized additive models and generalized linear models to data from several European fleets. The study highlights the fact that taking into account information that is not routinely collected, e.g. length of headline, weight of otter boards, or type of groundrope, will significantly improve the modelled relationships between lpue and the variables that measure relative fishing power. The magnitude of the skipper/crew experience effect was weaker than the technical effect of the vessel and/or its gear.

Climate Influences on Meningitis Incidence in Northwest Nigeria

2014 ◽  
Vol 6 (1) ◽  
pp. 62-76 ◽  
Author(s):  
Auwal F. Abdussalam ◽  
Andrew J. Monaghan ◽  
Vanja M. Dukić ◽  
Mary H. Hayden ◽  
Thomas M. Hopson ◽  
...  
Keyword(s):  
Generalized Linear Models ◽  
Linear Models ◽  
Generalized Additive Models ◽  
Skill Score ◽  
Additive Models ◽  
Maximum Temperature ◽  
Daily Maximum Temperature ◽  
Daily Maximum ◽  
Explanatory Variables ◽  
Score Statistics

Abstract Northwest Nigeria is a region with a high risk of meningitis. In this study, the influence of climate on monthly meningitis incidence was examined. Monthly counts of clinically diagnosed hospital-reported cases of meningitis were collected from three hospitals in northwest Nigeria for the 22-yr period spanning 1990–2011. Generalized additive models and generalized linear models were fitted to aggregated monthly meningitis counts. Explanatory variables included monthly time series of maximum and minimum temperature, humidity, rainfall, wind speed, sunshine, and dustiness from weather stations nearest to the hospitals, and the number of cases in the previous month. The effects of other unobserved seasonally varying climatic and nonclimatic risk factors that may be related to the disease were collectively accounted for as a flexible monthly varying smooth function of time in the generalized additive models, s(t). Results reveal that the most important explanatory climatic variables are the monthly means of daily maximum temperature, relative humidity, and sunshine with no lag; and dustiness with a 1-month lag. Accounting for s(t) in the generalized additive models explains more of the monthly variability of meningitis compared to those generalized linear models that do not account for the unobserved factors that s(t) represents. The skill score statistics of a model version with all explanatory variables lagged by 1 month suggest the potential to predict meningitis cases in northwest Nigeria up to a month in advance to aid decision makers.

scholarly journals Percolation models of pathogen spillover

2019 ◽  
Vol 374 (1782) ◽  
pp. 20180331 ◽  
Author(s):  
Alex D. Washburne ◽  
Daniel E. Crowley ◽  
Daniel J. Becker ◽  
Kezia R. Manlove ◽  
Marissa L. Childs ◽  
...  
Keyword(s):  
Statistical Analysis ◽  
Linear Models ◽  
Mechanistic Model ◽  
Piecewise Linear ◽  
Generalized Additive Models ◽  
Additive Models ◽  
Management Intervention ◽  
Management Concepts ◽  
The Impact ◽  
Reservoir Hosts

Predicting pathogen spillover requires counting spillover events and aligning such counts with process-related covariates for each spillover event. How can we connect our analysis of spillover counts to simple, mechanistic models of pathogens jumping from reservoir hosts to recipient hosts? We illustrate how the pathways to pathogen spillover can be represented as a directed graph connecting reservoir hosts and recipient hosts and the number of spillover events modelled as a percolation of infectious units along that graph. Percolation models of pathogen spillover formalize popular intuition and management concepts for pathogen spillover, such as the inextricably multilevel nature of cross-species transmission, the impact of covariance between processes such as pathogen shedding and human susceptibility on spillover risk, and the assumptions under which the effect of a management intervention targeting one process, such as persistence of vectors, will translate to an equal effect on the overall spillover risk. Percolation models also link statistical analysis of spillover event datasets with a mechanistic model of spillover. Linear models, one might construct for process-specific parameters, such as the log-rate of shedding from one of several alternative reservoirs, yield a nonlinear model of the log-rate of spillover. The resulting nonlinearity is approximately piecewise linear with major impacts on statistical inferences of the importance of process-specific covariates such as vector density. We recommend that statistical analysis of spillover datasets use piecewise linear models, such as generalized additive models, regression clustering or ensembles of linear models, to capture the piecewise linearity expected from percolation models. We discuss the implications of our findings for predictions of spillover risk beyond the range of observed covariates, a major challenge of forecasting spillover risk in the Anthropocene. This article is part of the theme issue ‘Dynamic and integrative approaches to understanding pathogen spillover’.

Generalized additive models reveal among-stand variation in live tree biomass equations

2020 ◽  
Author(s):  
Jacob I. Levine ◽  
Perry de Valpine ◽  
John J. Battles
Keyword(s):  
Linear Models ◽  
Generalized Additive Models ◽  
Additive Models ◽  
Carbon Accounting ◽  
Stand Age ◽  
Accurate Estimation ◽  
Biomass Estimation ◽  
Allometric Relationships ◽  
Tree Biomass ◽  
Live Tree

Accurate estimation of forest biomass is important for scientists and policymakers interested in carbon accounting, nutrient cycling, and forest resilience. Estimates often rely on the allometry of trees; however, limited datasets, uncertainty in model form, and unaccounted for sources of variation warrant a re-examination of allometric relationships using modern statistical techniques. We asked the following questions: (1) Is there among-stand variation in allometric relationships? (2) Is there nonlinearity in allometric relationships? (3) Can among-stand variation or nonlinearities in allometric equations be attributed to differences in stand age? (4) What are the implications for biomass estimation? To answer these questions, we synthesized a dataset of small trees from six different studies in the White Mountains of New Hampshire. We compared the performance of generalized additive models (GAMs) and linear models and found that GAMs consistently outperform linear models. The best-fitting model indicates that allometries vary among both stands and species and contain subtle nonlinearities which are themselves variable by species. Using a planned contrasts analysis, we were able to attribute some of the observed among-stand heterogeneity to differences in stand age. However, variability in these results point to additional sources of stand-level heterogeneity, which if identified could improve the accuracy of live-tree biomass estimation.

Habitat partitioning and the influence of benthic topography and oceanography on the distribution of fin and minke whales in the Bay of Fundy, Canada

2007 ◽  
Vol 87 (1) ◽  
pp. 149-156 ◽  
Author(s):  
Simon N. Ingram ◽  
Laura Walshe ◽  
Dave Johnston ◽  
Emer Rogan
Keyword(s):  
Linear Relationship ◽  
Linear Models ◽  
Generalized Additive Models ◽  
Bottom Topography ◽  
Distribution Patterns ◽  
Additive Models ◽  
Bay Of Fundy ◽  
Fin Whale ◽  
Minke Whales ◽  
Fin Whales

We collected data on the distribution of fin whales (Balaenoptera physalus) and minke whales (Balaenoptera acutorostrata) in the Bay of Fundy, Canada from a whale-watching vessel during commercial tours between July and September 2002. A single observer recorded the positions, species, numbers and surface activity of whales encountered during boat tours. We controlled for biased search effort by calculating sightings rates for both species in cells measuring 2′ latitude by 2′ longitude throughout the study area. Sightings rates were calculated by dividing the number of sightings of fin and minke whales in each cell by the number of visits by the tour boat to that cell. We used generalized additive models and generalized linear models to examine the influence of benthic topography on whale distribution patterns. Models showed a non-linear relationship for minke whale sighting rates with increasing benthic slopes and a linear relationship for minke and fin whale sightings rates with increasing water depth. Sightings of minkes were concentrated in areas subject to tidal wakes near the northern tips of Grand Manan and Campobello Island. Fin whales were also found off the northern tip of Grand Manan but sighting rates for this species were highest in areas with less benthic sloping topography adjacent to the relatively deep Owen Basin. Foraging was recorded during 87% of all whale encounters and our results indicate that whale distribution in this area is likely to be influenced by depth, bottom topography and fine scale oceanographic features that facilitate foraging.

scholarly journals On the Below- and Aboveground Phenology in Deciduous Trees: Observing the Fine-Root Lifespan, Turnover Rate, and Phenology of Fagus sylvatica L., Quercus robur L., and Betula pendula Roth for Two Growing Seasons

Forests ◽  
2021 ◽  
Vol 12 (12) ◽  
pp. 1680
Author(s):  
Bertold Mariën ◽  
Ivika Ostonen ◽  
Alice Penanhoat ◽  
Chao Fang ◽  
Hòa Xuan Nguyen ◽  
...  
Keyword(s):  
Fine Roots ◽  
Turnover Rate ◽  
Fine Root ◽  
Generalized Additive Models ◽  
Root Surface ◽  
Additive Models ◽  
Bud Burst ◽  
Root Lifespan ◽  
Root Phenology ◽  
Species Specific

We tested the relation between the below- and aboveground tree phenology, determining if beech and oak have a greater fine-root lifespan and a smaller turnover rate than birch and if thinner fine-roots or fine-roots born in spring have a shorter lifespan and greater turnover rate than thicker fine-roots or fine-roots born in another season. The fine-root phenology, bud burst, and leaf senescence in Belgian stands were monitored using minirhizotrons, visual observations, and chlorophyll measurements, respectively. The fine-root phenology and the lifespan and turnover rate were estimated using generalized additive models and Kaplan–Meier analyses, respectively. Unlike the aboveground phenology, the belowground phenology did not show a clear and repeating yearly pattern. The cumulative root surface remained stable for birch but peaked for beech and oak around summer to autumn in 2019 and spring in 2020. The new root count was larger in 2019 than in 2020. The mean lifespan of fine-roots with a diameter below 0.5 mm (308 to 399 days) was shorter than those with a diameter between 0.5 to 1 mm (438 to 502 days), 1 to 2 mm (409 to 446 days), or above 2 mm (418 to 471 days). Fine-roots born in different seasons showed a species-specific lifespan and turnover rate.

scholarly journals Standardization of CPUE for walleye pollock in the Okhotsk Sea with inclusion of some environmental factors

2020 ◽  
Vol 200 (4) ◽  
pp. 819-836
Author(s):  
V. V. Kulik ◽  
A. I. Varkentin ◽  
O. I. Ilyin
Keyword(s):  
Linear Models ◽  
Stock Assessment ◽  
Generalized Additive Models ◽  
Total Yield ◽  
Walleye Pollock ◽  
Additive Models ◽  
Okhotsk Sea ◽  
Catch Per Unit Effort ◽  
Bayesian Criterion ◽  
In The Beginning

Catch of walleye pollock by Russia is the highest in the northern Okhotsk Sea where on average 0.94 million metric tons were caught annually in the period between 1962 and 2017, or around 24 % of the total yield of Russian fishery. The total stock and spawning stock of pollock grow there since 2002, though the catch per unit effort (CPUE) has significantly decreased in the beginning of 2018 despite expected high levels of both total and spawning stocks. The sea surface temperature, ice cover and storms frequency were examined as possible reasons of low fishing efficiency in 2018. For this purpose, the generalized linear models (GLM) and generalized additive models (GAM) of catch dynamics are compared. GAM with addition of temperature and storms factors has the lowest Schwarz’s Bayesian criterion and the highest explained deviance (61.6 %). Efficiency of fishing gears has nonlinear relationship with the towing time. CPUE has hypersensitivity to the stock biomass presented as the power dependence (γ = 0.94, r = 0.923). Standardized CPUE is recommended for using in the final GAM for the pollock stock assessment in the northern Okhotsk Sea, hypersensitivity of CPUE should be estimated and corrected if necessary.

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