Climate and soil nutrients jointly determine the strength of density-dependent interactions in an old-growth temperate forest

A distinct challenge facing ecologists is to understand individual and interactive effects of climate, edaphic characteristics, and biotic relationships on forest ecosystems in order to anticipate and adapt to environmental change. Forest demography is influenced by many ecological processes, including gap-creating to stand-replacing disturbances, pest and pathogen attack, herbivory, shading and crowding, resource competition, heavy metal toxicity, and direct climate impacts. However, a comprehensive assessment of forest demographic responses to these ecological processes can be difficult to accomplish, as seasonal and inter-annual climate variability can interact with biotic and edaphic characteristics to impact individual tree mortality, recruitment, and growth – a complex and, thus, largely understudied phenomenon. We investigate these relationships in the Wind River Forest Dynamics Plot, a 27.2-ha permanent research site located in an approximately 500-yr old forest within the T.T. Munger Research Natural Area of the Gifford Pinchot National Forest in Washington State, USA. We model vital rates, including rates of agent-specific tree mortality, as functions of neighborhood structure and composition along gradients of soil resources and topography, and assess these relationships over a six-year timespan to quantify the direct and indirect effects of climatic water deficit and snowpack variability on tree vital rates. Our data support the interpretation that interactions across multiple scales must be considered prior to interpreting main effects in isolation, as climatic, edaphic, and biotic processes indeed modify each other’s effects - in several cases, these interactions resulted in complete reversals or nullification of main effects. We underscore the point that future research must endeavor to capture the complexity of ecological interactions in order to most accurately understand, and therefore, most appropriately respond, to global change

Hidden Mechanisms of Climate Impacts in Western Forests: Integrating Theory and Observation for Climate Adaptation

Fire, insects, and disease are necessary components of forest ecosystems. Yet, climate change is intensifying these tree stressors and creating new interactions that threaten forest survival. This dissertation combined field observations with statistical predictions of changing disturbances in western forests to identify 1) how conventional models may underestimate future forest loss, and 2) how positive relationships between trees may be exploited by managers to prevent forest loss. In Chapter II, I tested whether increasingly extreme weather with climate change increases Pacific yew extinction risk. I found that conventional modeling methods underestimated local extinction risk because trees were adapted to a range in average conditions, but had limited tolerance of extreme drought. In Chapter III, I predicted whether future climate change will alter the strength of competition between species (heterospecifics) versus within species (conspecifics). I found that heterospecific competition is more sensitive to drought than conspecific competition, leading to higher tree mortality during drought than is currently expected. In Chapter IV, I looked at sugar pine tree rings to measure how pines respond to three centuries of fire exclusion, drought, fire, and a bark beetle outbreak. I found that fire suppression led to higher competitive stress, which decreased pines’ resilience to fire, and consequently, decreased pines’ survival during a subsequent bark beetle outbreak. Woody species diversity, however, was able to increase pine survival following fire and bark beetles by allowing higher pine growth and defenses. In Chapter V, I tested whether beneficial relationships between trees and mutualistic fungi could help trees survive across regional differences in climate, environmental conditions, and disturbances. I found that woody species diversity increased large-diameter tree resistance to insects and disease, but only if those species shared a mycorrhizal network. Large trees comprising 17 common western species across three canonical forest types showed this pattern –– despite residing in different topographic positions and climatological contexts. I identified how biodiversity can increase forest resistance and resilience to disturbances, but also found climate change to be weakening the processes responsible for maintaining biodiversity. Managers must take a more active approach to cultivating and preserving forest tree biodiversity to ensure forests are able to continue provisioning essential services, such as carbon storage, in the future. These four long-term studies of spatially explicit, cause-specific tree mortality provided useful insights into tree survival and forest change that will improve vegetation model accuracy and inform management of mature forests in western North America

An ensemble learning approach for the classification of remote sensing scenes based on covariance pooling of CNN features

International audienceThis paper aims at presenting a novel ensemble learning approach based on the concept of covariance pooling of CNN features issued from a pretrained model. Starting from a supervised classification algorithm, named multilayer stacked covariance pooling (MSCP), which exploits simultaneously second order statistics and deep learning features, we propose an alternative strategy which employs an ensemble learning approach among the stacked convolutional feature maps. The aggregation of multiple learning algorithm decisions, produced by different stacked subsets, permits to obtain a better predictive classification performance. An application for the classification of large scale remote sensing images is next proposed. The experimental results, conducted on two challenging datasets, namely UC Merced and AID datasets, improve the classification accuracy while maintaining a low computation time. This confirms, besides the interest of exploiting second order statistics, the benefit of adopting an ensemble learning approach

Encodage de matrices de covariance par les vecteurs de Fisher log-euclidien : application à la classification supervisée d'images satellitaires

National audienceThis paper introduces a new hybrid architecture based on Fisher vector encoding (VF) of the convolutional layer outputs of a neural network. The originality of this work is based on the exploitation of second-order statistics via the calculation of local covariance matrices. Considering the intrinsic properties of the Riemannian manifold of covariance matrices, we propose to use the log-euclidean metric in order to extend the concept of VF encoding: the log-euclidean Fisher vectors (LE VF). The proposed architecture is then evaluated on different remote sensing databases : the UC Merced Land Use Land Cover database, the AID database, as well as on two Pléiades datasets on maritime pine forests and oyster beds.Cet article présente une nouvelle architecture hybride basée sur l'encodage par vecteurs de Fisher (VF) des sorties des couches convolutives d'un réseau de neurones. L'originalité de ce travail repose sur l'exploitation des statistiques d'ordre deux via le calcul des matrices de covariance locales. Considérant les propriétés intrinsèques à la géométrie Riemannienne propre à l'espace des matrices de covariance, nous proposons d'utiliser la métrique log-euclidienne afin d'étendre le concept des VF pour l'encodage de matrices de covariance : les vecteurs de Fisher log-euclidiens (LE VF). L'architecture proposée est ensuite évaluée sur différentes bases de données de télédétection : la base UC Merced Land Use Land Cover, la base AID, ainsi que sur deux jeux de données Pléiades sur des forêts de pins maritimes et de parcs ostréicoles

Time to improve informed consent for dialysis: an international perspective

The literature reveals that current nephrology practice in obtaining informed consent for dialysis falls short of ethical and legal requirements. Meeting these requirements represents a significant challenge, especially because the benefits and risks of dialysis have shifted significantly with the growing number of older, comorbid patients. The importance of informed consent for dialysis is heightened by several concerns, including: (1) the proportion of predialysis patients and patients on dialysis who lack capacity in decision making and (2) whether older, comorbid, and frail patients understand their poor prognosis and the full implications to their independence and functional status of being on dialysis. This article outlines the ethical and legal requirements for a valid informed consent to dialysis: (1) the patient was competent, (2) the consent was made voluntarily, and (3) the patient was given sufficient information in an understandable manner to make the decision. It then considers the application of these requirements to practice across different countries. In the process of informed consent, the law requires a discussion by the physician of the material risks associated with dialysis and alternative options. We argue that, legally and ethically, this discussion should include both the anticipated trajectory of the illness and the effect on the life of the patient with particular regard to the outcomes most important to the individual. In addition, a discussion should occur about the option of a conservative, nondialysis pathway. These requirements ensure that the ethical principle of respect for patient autonomy is honored in the context of dialysis. Nephrologists need to be open to, comfortable with, and skillful in communicating this information. From these clear, open, ethically, and legally valid consent discussions, a significant dividend will hopefully flow for patients, families, and nephrologists alike

Shrub Communities, Spatial Patterns, and Shrub-Mediated Tree Mortality following Reintroduced Fire in Yosemite National Park, California, USA

Shrubs contribute to the forest fuel load; their distribution is important to tree mortality and regeneration, and vertebrate occupancy. We used a method new to fire ecology—extensive continuous mapping of trees and shrub patches within a single large (25.6 ha) study site—to identify changes in shrub area, biomass, and spatial pattern due to fire reintroduction by a backfire following a century of fire exclusion in lower montane forests of the Sierra Nevada, California, USA. We examined whether trees in close proximity to shrubs prior to fire experienced higher mortality rates than trees in areas without shrubs. We calculated shrub biomass using demography subplots and existing allometric equations, and we developed new equations for beaked hazel (Corylus cornuta ssp. californica [A. de Candolle] E. Murray) from full dissection of 50 stems. Fire decreased shrub patch area from 15.1 % to 0.9 %, reduced live shrub biomass from 3.49 Mg ha−1 to 0.27 Mg ha−1, and consumed 4.41 Mg ha−1 of living and dead shrubs. Distinct (non-overlapping) shrub patches decreased from 47 ha−1 to 6 ha−1. The mean distance between shrub patches increased 135 %. Distances between montane chaparral patches increased 285 %, compared to a 54 % increase in distances between riparian shrub patches and an increase of 267 % between generalist shrub patches. Fire-related tree mortality within shrub patches was marginally lower (67.6 % versus 71.8 %), showing a contrasting effect of shrubs on tree mortality between this forest ecosystem and chaparral-dominated ecosystems in which most trees are killed by fire

Quality of life and pain in premenopausal women with major depressive disorder: The POWER Study

BACKGROUND: Whereas it is established that organic pain may induce depression, it is unclear whether pain is more common in healthy subjects with depression. We assessed the prevalence of pain in premenopausal women with major depression (MDD). Subjects were 21- to 45-year-old premenopausal women with MDD (N = 70; age: 35.4 +/- 6.6; mean +/- SD) and healthy matched controls (N = 36; age 35.4 +/- 6.4) participating in a study of bone turnover, the P.O.W.E.R. (Premenopausal, Osteopenia/Osteoporosis, Women, Alendronate, Depression) Study. METHODS: Patients received a clinical assessment by a pain specialist, which included the administration of two standardized forms for pain, the Brief Pain Inventory – Short Form, and the Initial Pain Assessment Tool, and two scales of everyday stressors, the Hassles and Uplifts Scales. In addition, a quality-of-life instrument, the SF-36, was used. The diagnosis of MDD was established by a semi-structured interview, according to the DSM-IV criteria. Substance P (SP) and calcitonin-gene-related-peptide (CGRP), neuropeptides which are known mediators of pain, were measured every hour for 24 h in a subgroup of patients (N = 17) and controls (N = 14). RESULTS: Approximately one-half of the women with depression reported pain of mild intensity. Pain intensity was significantly correlated with the severity of depression (r(2 )= 0.076; P = 0.04) and tended to be correlated with the severity of anxiety, (r(2 )= 0.065; P = 0.07), and the number of depressive episodes (r(2 )= 0.072; P = 0.09). Women with MDD complained of fatigue, insomnia, and memory problems and experienced everyday negative stressors more frequently than controls. Quality of life was decreased in women with depression, as indicated by lower scores in the emotional and social well-being domains of the SF-36. SP (P < 0.0003) and CGRP (P < 0.0001) were higher in depressed subjects. CONCLUSION: Women with depression experienced pain more frequently than controls, had a lower quality of life, and complained more of daily stressors. Assessment of pain may be important in the clinical evaluation of women with MDD. SP and CGRP may be useful biological markers in women with MDD

Large-Diameter Trees Dominate Snag and Surface Biomass Following Reintroduced Fire

The reintroduction of fire to landscapes where it was once common is considered a priority to restore historical forest dynamics, including reducing tree density and decreasing levels of woody biomass on the forest floor. However, reintroducing fire causes tree mortality that can have unintended ecological outcomes related to woody biomass, with potential impacts to fuel accumulation, carbon sequestration, subsequent fire severity, and forest management. In this study, we examine the interplay between fire and carbon dynamics by asking how reintroduced fire impacts fuel accumulation, carbon sequestration, and subsequent fire severity potential. Beginning pre-fire, and continuing 6 years post-fire, we tracked all live, dead, and fallen trees ≥ 1 cm in diameter and mapped all pieces of deadwood (downed woody debris) originating from tree boles ≥ 10 cm diameter and ≥ 1 m in length in 25.6 ha of an Abies concolor/Pinus lambertiana forest in the central Sierra Nevada, California, USA. We also tracked surface fuels along 2240 m of planar transects pre-fire, immediately post-fire, and 6 years post-fire. Six years after moderate-severity fire, deadwood ≥ 10 cm diameter was 73 Mg ha−1, comprised of 32 Mg ha−1 that persisted through fire and 41 Mg ha−1 of newly fallen wood (compared to 72 Mg ha−1 pre-fire). Woody surface fuel loading was spatially heterogeneous, with mass varying almost four orders of magnitude at the scale of 20 m × 20 m quadrats (minimum, 0.1 Mg ha−1; mean, 73 Mg ha−1; maximum, 497 Mg ha−1). Wood from large-diameter trees (≥ 60 cm diameter) comprised 57% of surface fuel in 2019, but was 75% of snag biomass, indicating high contributions to current and future fuel loading. Reintroduction of fire does not consume all large-diameter fuel and generates high levels of surface fuels ≥ 10 cm diameter within 6 years. Repeated fires are needed to reduce surface fuel loading