To Model or not to Model, That is no Longer the Question for Ecologists

In deutscher Sprache nicht verfügbarHere, I argue that we should abandon the division between “field ecologists” and “modelers,” and embrace modeling and empirical research as two powerful and often complementary approaches in the toolbox of 21st century ecologists, to be deployed alone or in combination depending on the task at hand. As empirical research has the longer tradition in ecology, and modeling is the more recent addition to the methodological arsenal, I provide both practical and theoretical reasons for integrating modeling more deeply into ecosystem research. Empirical research has epistemological priority over modeling; however, that is, for models to realize their full potential, and for modelers to wield this power wisely, empirical research is of fundamental importance. Combining both methodological approaches or forming “super ties” with colleagues using different methods are promising pathways to creatively exploit the methodological possibilities resulting from increasing computing power. To improve the proficiency of the growing group of model users and ensure future innovation in model development, we need to increase the modeling literacy among ecology students. However, an improved training in modeling must not curtail education in basic ecological principles and field methods, as these skills form the foundation for building and applying models in ecology.(VLID)176142

Climate change amplifies the interactions between wind and bark beetle disturbances in forest landscapes

Abstract in dt. Sprache nicht verfügbarContext Growing evidence suggests that climate change could substantially alter forest disturbances. Interactions between individual disturbance agents are a major component of disturbance regimes, yet how interactions contribute to their climate sensitivity remains largely unknown. Objectives Here, our aim was to assess the climate sensitivity of disturbance interactions, focusing on wind and bark beetle disturbances. Methods We developed a process-based model of bark beetle disturbance, integrated into the dynamic forest landscape model iLand (already including a detailed model of wind disturbance). We evaluated the integrated model against observations from three wind events and a subsequent bark beetle outbreak, affecting 530.2 ha (3.8 %) of a mountain forest landscape in Austria between 2007 and 2014. Subsequently, we conducted a factorial experiment determining the effect of changes in climate variables on the area disturbed by wind and bark beetles separately and in combination. Results iLand was well able to reproduce observations with regard to area, temporal sequence, and spatial pattern of disturbance. The observed disturbance dynamics was strongly driven by interactions, with 64.3 % of the area disturbed attributed to interaction effects. A +4 C warming increased the disturbed area by +264.7 % and the area-weighted mean patch size by +1794.3 %. Interactions were found to have a ten times higher sensitivity to temperature changes than main effects, considerably amplifying the climate sensitivity of the disturbance regime. Conclusions Disturbance interactions are a key component of the forest disturbance regime. Neglecting interaction effects can lead to a substantial underestimation of the climate change sensitivity of disturbance regimes.(VLID)164896

Tree defence and bark beetles in a drying world: carbon partitioning, functioning and modelling.

Drought has promoted large-scale, insect-induced tree mortality in recent years, with severe consequences for ecosystem function, atmospheric processes, sustainable resources and global biogeochemical cycles. However, the physiological linkages among drought, tree defences, and insect outbreaks are still uncertain, hindering our ability to accurately predict tree mortality under on-going climate change. Here we propose an interdisciplinary research agenda for addressing these crucial knowledge gaps. Our framework includes field manipulations, laboratory experiments, and modelling of insect and vegetation dynamics, and focuses on how drought affects interactions between conifer trees and bark beetles. We build upon existing theory and examine several key assumptions: (1) there is a trade-off in tree carbon investment between primary and secondary metabolites (e.g. growth vs defence); (2) secondary metabolites are one of the main component of tree defence against bark beetles and associated microbes; and (3) implementing conifer-bark beetle interactions in current models improves predictions of forest disturbance in a changing climate. Our framework provides guidance for addressing a major shortcoming in current implementations of large-scale vegetation models, the under-representation of insect-induced tree mortality

Empirical analysis shows reduced cost data collection may be an efficient method in economic clinical trials

BACKGROUND: Data collection for economic evaluation alongside clinical trials is burdensome and cost-intensive. Limiting both the frequency of data collection and recall periods can solve the problem. As a consequence, gaps in survey periods arise and must be filled appropriately. The aims of our study are to assess the validity of incomplete cost data collection and define suitable resource categories. METHODS: In the randomised KORINNA study, cost data from 234 elderly patients were collected quarterly over a 1-year period. Different strategies for incomplete data collection were compared with complete data collection. The sample size calculation was modified in response to elasticity of variance. RESULTS: Resource categories suitable for incomplete data collection were physiotherapy, ambulatory clinic in hospital, medication, consultations, outpatient nursing service and paid household help. Cost estimation from complete and incomplete data collection showed no difference when omitting information from one quarter. When omitting information from two quarters, costs were underestimated by 3.9% to 4.6%. With respect to the observed increased standard deviation, a larger sample size would be required, increased by 3%. Nevertheless, more time was saved than extra time would be required for additional patients. CONCLUSION: Cost data can be collected efficiently by reducing the frequency of data collection. This can be achieved by incomplete data collection for shortened periods or complete data collection by extending recall windows. In our analysis, cost estimates per year for ambulatory healthcare and non-healthcare services in terms of three data collections was as valid and accurate as a four complete data collections. In contrast, data on hospitalisation, rehabilitation stays and care insurance benefits should be collected for the entire target period, using extended recall windows. When applying the method of incomplete data collection, sample size calculation has to be modified because of the increased standard deviation. This approach is suitable to enable economic evaluation with lower costs to both study participants and investigators. TRIAL REGISTRATION: The trial registration number is ISRCTN0289374

A simulation platform to assess forest landscape resilience

Forest ecosystems suffer increasingly from pressures such as anthropogenic climatic change, while at the same time being subject to an increasing societal demand for ecosystem services. In addition to direct climate effects on tree growth and regeneration recent research suggests that climate-induced changes in disturbance regimes will likely increase the volatility of future forest trajectories. Addressing these challenges requires a coupled human and natural systems perspective, as most of Europe’s forests are heavily influenced by management, and forests are a crucial component in the emerging bioeconomy. Methodologically, assessments of the long-term socio-ecological resilience of forest ecosystems require approaches that combine social and ecological processes. Here we present iLand, the individual based landscape and disturbance model, offering a versatile and powerful platform for such analyses. iLand is a process-based landscape model that simulates growth, regeneration and mortality of individual trees. It has a modular structure and contains sub-modules for abiotic (forest fire, wind) and biotic (bark beetles) disturbance agents. Furthermore, it is coupled with an agent-based model of forest management, simulating realistic management activities of single or multiple managing agents, which dynamically adapt their management strategies to changing conditions. Applying the model to a 6,500 ha landscape in Austria we here investigate the potential of different management strategies and responses – deduced from a stakeholder process with local managers – to improve forest resilience to changing climate and disturbance regimes. We demonstrate how scenario analysis by means of dynamic simulation modeling can help to identify pathways of future resilience in forest ecosystem management

Scaling vegetation dynamics: a metamodeling approach based on deep learning

Terrestrial vegetation is of crucial importance for human well-being and provides a wide variety of ecosystem services to society. To tackle global issues such as climate change or biodiversity loss, managers increasingly demand tools that allow the prediction of vegetation dynamics at large spatial scales. While dynamic vegetation models with a faithful representation of demographic processes exist for local to landscape scale, addressing larger scales with the fine spatial grain required to answer management questions remains a challenge. We here introduce a new framework for Scaling Vegetation Dynamics (SVD) that at its core utilizes deep neural networks (DNNs). Deep Learning is an emerging branch of machine learning, currently revolutionizing computer vision, natural language processing and many other fields. In the context of SVD, a DNN learns vegetation dynamics from a high resolution process based vegetation model (PBM). Specifically, the DNN is trained to predict the probability of transitions between discrete vegetation states contingent on the current state, the residence time, environmental drivers (climate and soil conditions), and the spatial context (i.e., the state of neighboring cells). In addition, the density distributions of relevant ecosystem attributes (e.g., total ecosystem carbon or biodiversity) are derived from PBM output for each vegetation state, which allows assessing the impact of vegetation transitions on those attributes. In this contribution we introduce the conceptual approach of SVD and show results for an example application in the Austrian Alps. More generally, we discuss aspects of applying deep learning in the context of ecological modeling

Resilience and vulnerability : distinct concepts to address global change in forests

Resilience and vulnerability are important concepts to understand, anticipate, and manage global change impacts on forest ecosystems. However, they are often used confusingly and inconsistently, hampering a synthetic understanding of global change, and impeding communication with managers and policy-makers. Both concepts are powerful and have complementary strengths, reflecting their different history, methodological approach, components, and spatiotemporal focus. Resilience assessments address the temporal response to disturbance and the mechanisms driving it. Vulnerability assessments focus on spatial patterns of exposure and susceptibility, and explicitly address adaptive capacity and stakeholder preferences. We suggest applying the distinct concepts of resilience and vulnerability where they provide particular leverage, and deduce a number of lessons learned to facilitate the next generation of global change assessments

The effects of forest cover and disturbance on torrential hazards

Global human population growth, limited space for settlements and a booming tourism industry have led to a strong increase of human infrastructure in mountain regions. As this infrastructure is highly exposed to natural hazards, a main role of mountain forests is to regulate the environment and reduce hazard probability. However, canopy disturbances are increasing in many parts of the world, potentially threatening the protection function of forests. Yet, large-scale quantitative evidence on the influence of forest cover and disturbance on natural hazards remains scarce to date. Here we quantified the effects of forest cover and disturbance on the probability and frequency of torrential hazards for 10 885 watersheds in the Eastern Alps. Torrential hazard occurrences were derived from a comprehensive database documenting 3768 individual debris flow and flood events between 1986 and 2018. Forest disturbances were mapped from Landsat satellite time series analysis. We found evidence that forests reduce the probability of natural hazards, with a 25 percentage point increase in forest cover decreasing the probability of torrential hazards by 8.7%± 1.2%. Canopy disturbances generally increased the probability of torrential hazard events, with the regular occurrence of large disturbance events being the most detrimental disturbance regime for natural hazards. Disturbances had a bigger effect on debris flows than on flood events, and press disturbances were more detrimental than pulse disturbances. We here present the first large scale quantification of forest cover and disturbance effects on torrential hazards. Our findings highlight that forests constitute important green infrastructure in mountain landscapes, efficiently reducing the probability of natural hazards, but that increasing forest disturbances can weaken the protective function of forests.Austrian Climate Research ProgramAustrian Science Fund https://doi.org/10.13039/501100002428Peer Reviewe

Nurse-based case management for aged patients with myocardial infarction: study protocol of a randomized controlled trial

BACKGROUND: Aged patients with coronary heart disease (CHD) have a high prevalence of co-morbidity associated with poor quality of life, high health care costs, and increased risk for adverse outcomes. These patients are often lacking an optimal home care which may result in subsequent readmissions. However, a specific case management programme for elderly patients with myocardial infarction (MI) is not yet available. The objective of this trial is to examine the effectiveness of a nurse-based case management in patients aged 65 years and older discharged after treatment of an acute MI in hospital. The programme is expected to influence patient readmission, mortality and quality of life, and thus to reduce health care costs compared with usual care. In this paper the study protocol is described. METHODS/DESIGN: The KORINNA (Koronarinfarkt Nachbehandlung im Alter) study is designed as a single-center randomized two-armed parallel group trial. KORINNA is conducted in the framework of KORA (Cooperative Health Research in the Region of Augsburg). Patients assigned to the intervention group receive a nurse-based follow-up for one year including home visits and telephone calls. Key elements of the intervention are to detect problems or risks, to give advice regarding a broad range of aspects of disease management and to refer to the general practitioner, if necessary. The control group receives usual care. Twelve months after the index hospitalization all patients are re-assessed. The study has started in September 2008. According to sample size estimation a total number of 338 patients will be recruited. The primary endpoint of the study is time to first readmission to hospital or out of hospital death. Secondary endpoints are functional status, participation, quality of life, compliance, and cost-effectiveness of the intervention. For the economic evaluation cost data is retrospectively assessed by the patients. The incremental cost-effectiveness ratio (ICER) will be calculated. DISCUSSION: The KORINNA study will contribute to the evidence regarding the effectiveness of case management programmes in aged people with MI. The results can be an important basis for clinicians, administrators and health policy makers to decide on the provision of high-quality care to older patients with CHD. TRIAL REGISTRATION: ISRCTN0289374