Northern expansion is not compensating for southern declines in North American boreal forests

Abstract Climate change is expected to shift the boreal biome northward through expansion at the northern and contraction at the southern boundary respectively. However, biome-scale evidence of such a shift is rare. Here, we used remotely-sensed tree cover data to quantify temporal changes across the North American boreal biome from 2000 to 2019. We reveal a strong north-south asymmetry in tree cover change, coupled with a range shrinkage of tree cover distributions. We found no evidence for tree cover expansion in the northern biome, while tree cover increased markedly in the core of the biome range. By contrast, tree cover declined along the southern biome boundary, where losses were related largely to wildfires and timber logging. We show that these contrasting trends are structural indicators for a possible onset of a biome contraction which may lead to long-term carbon declines

The potential of resilience indicators to anticipate infectious disease outbreaks, a systematic review and guide.

To reduce the consequences of infectious disease outbreaks, the timely implementation of public health measures is crucial. Currently used early-warning systems are highly context-dependent and require a long phase of model building. A proposed solution to anticipate the onset or termination of an outbreak is the use of so-called resilience indicators. These indicators are based on the generic theory of critical slowing down and require only incidence time series. Here we assess the potential for this approach to contribute to outbreak anticipation. We systematically reviewed studies that used resilience indicators to predict outbreaks or terminations of epidemics. We identified 37 studies meeting the inclusion criteria: 21 using simulated data and 16 real-world data. 36 out of 37 studies detected significant signs of critical slowing down before a critical transition (i.e., the onset or end of an outbreak), with a highly variable sensitivity (i.e., the proportion of true positive outbreak warnings) ranging from 0.03 to 1 and a lead time ranging from 10 days to 68 months. Challenges include low resolution and limited length of time series, a too rapid increase in cases, and strong seasonal patterns which may hamper the sensitivity of resilience indicators. Alternative types of data, such as Google searches or social media data, have the potential to improve predictions in some cases. Resilience indicators may be useful when the risk of disease outbreaks is changing gradually. This may happen, for instance, when pathogens become increasingly adapted to an environment or evolve gradually to escape immunity. High-resolution monitoring is needed to reach sufficient sensitivity. If those conditions are met, resilience indicators could help improve the current practice of prediction, facilitating timely outbreak response. We provide a step-by-step guide on the use of resilience indicators in infectious disease epidemiology, and guidance on the relevant situations to use this approach

Rapid Vegetation Succession and Coupled Permafrost Dynamics in Arctic Thaw Ponds in the Siberian Lowland Tundra

Thermokarst features, such as thaw ponds, are hotspots for methane emissions in warming lowland tundra. Presently we lack quantitative knowledge on the formation rates of thaw ponds and subsequent vegetation succession, necessary to determine their net contribution to greenhouse gas emissions. This study sets out to identify development trajectories and formation rates of small‐scale (<100 m2), shallow arctic thaw ponds in north‐eastern Siberia. We selected 40 ponds of different age classes based on a time‐series of satellite images and measured vegetation composition, microtopography, water table, and thaw depth in the field and measured age of colonizing shrubs in thaw ponds using dendrochronology. We found that young ponds are characterized by dead shrubs, while older ponds show rapid terrestrialization through colonization by sedges and Sphagnum moss. While dead shrubs and open water are associated with permafrost degradation (lower surface elevation, larger thaw depth), sites with sedge and in particular Sphagnum display indications of permafrost recovery. Recruitment of Betula nana on Sphagnum carpets in ponds indicates a potential recovery toward shrub‐dominated vegetation, although it remains unclear if and on what timescale this occurs. Our results suggest that thaw ponds display potentially cyclic vegetation succession associated with permafrost degradation and recovery. Pond formation and initial colonization by sedges can occur on subdecadal timescales, suggesting rapid degradation and initial recovery of permafrost. The rates of formation and recovery of small‐scale, shallow thaw ponds have implications for the greening/browning dynamics and carbon balance of this ecosystem

Overview of the 37 papers included in this review.

A Number of included papers per year. The number of studies on resilience indicators to anticipate epidemics has shown an increasing trend in the last few years. Since 2020, more studies have been published as data on the COVID-19 pandemic became publicly available. B Included papers classified according to the type of study into three categories: case studies, simulation studies, and simulation studies supported by case studies. C Included papers classified according to the type of transition, into three categories: the onset of an outbreak, disease elimination, and both.</p

Summary of the included studies.

Summary of the included studies and their classification. (XLSX)</p

Decision tree.

Step-by-step approach to use resilience indicators in epidemiology.</p

Illustration of resilience indicators based on simulated data using an SIR model.

In the model, the transmission rate increases linearly over time, resulting in a critical transition when R crosses one. A-C are potential landscapes, showing the energy of the system for different states. The ball represents the state of the system. A R is relatively far from the threshold: the system will recover easily from an external perturbation. B R is close to the critical threshold: the potential to recover from external perturbation is low, and the system undergoes critical slowing down. C The threshold is crossed: the system will stabilize at a state for which the disease is endemic. D incidence time series generated using a SIR model. The system is undergoing a critical transition: R increases linearly over time until it crosses one (shaded area). E and F are associated resilience indicators calculated in the simulated time series (daily resolution) using a rolling window. We observe a significant increase of autocorrelation and variance prior to the outbreak.</p

PRISMA flowchart.

PRISMA flowchart of the literature search process.</p

Inclusion and exclusion criteria.

To reduce the consequences of infectious disease outbreaks, the timely implementation of public health measures is crucial. Currently used early-warning systems are highly context-dependent and require a long phase of model building. A proposed solution to anticipate the onset or termination of an outbreak is the use of so-called resilience indicators. These indicators are based on the generic theory of critical slowing down and require only incidence time series. Here we assess the potential for this approach to contribute to outbreak anticipation. We systematically reviewed studies that used resilience indicators to predict outbreaks or terminations of epidemics. We identified 37 studies meeting the inclusion criteria: 21 using simulated data and 16 real-world data. 36 out of 37 studies detected significant signs of critical slowing down before a critical transition (i.e., the onset or end of an outbreak), with a highly variable sensitivity (i.e., the proportion of true positive outbreak warnings) ranging from 0.03 to 1 and a lead time ranging from 10 days to 68 months. Challenges include low resolution and limited length of time series, a too rapid increase in cases, and strong seasonal patterns which may hamper the sensitivity of resilience indicators. Alternative types of data, such as Google searches or social media data, have the potential to improve predictions in some cases. Resilience indicators may be useful when the risk of disease outbreaks is changing gradually. This may happen, for instance, when pathogens become increasingly adapted to an environment or evolve gradually to escape immunity. High-resolution monitoring is needed to reach sufficient sensitivity. If those conditions are met, resilience indicators could help improve the current practice of prediction, facilitating timely outbreak response. We provide a step-by-step guide on the use of resilience indicators in infectious disease epidemiology, and guidance on the relevant situations to use this approach.</div

Summary of the indicators and their usage.

Original references refer to primary studies not included in that review that studied the use of the indicators. Mathematical derivation of the indicators is given in [53]. (XLSX)</p