Pine Forest Management and Disturbance in Northern Poland: Combining High-Resolution 100-Year-Old Paleoecological and Remote Sensing Data

Global warming has compelled to strengthen the resilience of European forests. Due to repeated droughts and heatwaves, weakened trees become vulnerable to insect outbreaks, pathogen invasions, and strong winds. This study combines high-resolution analysis of a 100-year-old high-resolution peat archive synthesized from the Martwe peatland in Poland with remote sensing data. We present the first REVEALS based vegetation reconstruction in a tornado-hit area from Poland on the background of previous forest management in monocultural even-aged stands – Tuchola Pinewoods. During the 20th century, the pine monocultures surrounding the peatland were affected by clear-cutting and insect outbreaks. In 2012, a tornado, destroyed ca. 550 ha of pine forest around the peatland. The palynological record reflects these major events of the past 100 years as well as changes in forest practices. Our study showed the strong relationships between the decrease of Pinus sylvestris (Scots pine) in palynological record as well as planting patterns after the tornado. Moreover, past forestry practices [such as domination of Pinus sylvestris, the collapse of Picea abies (Norway spruce), low share of Betula spec. (birch) due to Pinus sylvestris promotion and probable also to a lesser by removal of Betula as a “forest weed,” and low plant coverage of tree species due to clear-cutting and cutting after insect outbreaks] were well identified in the proxy record. In monocultures managed over decades, the reconstruction of vegetation may be challenging due to changes in the age composition of the Pinus sylvestris stands. We found that through historical, remote sensing, and paleoecological data, the dynamics of disturbances such as insect outbreaks and tornadoes, as well as the changing perceptions of local society about forests, can be determined.

Forest defoliator outbreaks alter nutrient cycling in northern waters

Insect defoliators alter biogeochemical cycles from land into receiving waters by consuming terrestrial biomass and releasing biolabile frass. Here, we related insect outbreaks to water chemistry across 12 boreal lake catchments over 32-years. We report, on average, 27% lower dissolved organic carbon (DOC) and 112% higher dissolved inorganic nitrogen (DIN) concentrations in lake waters when defoliators covered entire catchments and reduced leaf area. DOC reductions reached 32% when deciduous stands dominated. Within-year changes in DOC from insect outbreaks exceeded 86% of between-year trends across a larger dataset of 266 boreal and north temperate lakes from 1990 to 2016. Similarly, within-year increases in DIN from insect outbreaks exceeded local, between-year changes in DIN by 12-times, on average. As insect defoliator outbreaks occur at least every 5 years across a wider 439,661 km2 boreal ecozone of Ontario, we suggest they are an underappreciated driver of biogeochemical cycles in forest catchments of this region.

Canopy Disturbances Catalyse Tree Species Shifts in Swiss Forests

Widely observed inertia of forest communities contrasts with climate change projections that suggest dramatic alterations of forest composition for the coming decades. Disturbances might be a key process to catalyse changes in tree species composition under environmental change by creating opportunities for ‘new’ species to establish. To test this assumption, we compared two assessments (1993–1995, 2009–2017) from the Swiss National Forest Inventory to evaluate which forests were opened by natural canopy disturbance (that is, wind, insect outbreaks, fire and drought) and if these disturbances altered tree species composition both in terms of species-specific basal area and recruitment densities. Natural disturbances affected 14% of the Swiss forests within 25 years, with wind and insect outbreaks being the most frequent (75%) and fire and drought being rare (< 1.5%). Disturbances led to a shift from conifer to broadleaf tree species at low elevation, in particular in dense Picea abies stands, but no change was observed at higher elevations. The composition of undisturbed sites persisted during the same period. Our results demonstrate that undisturbed forests widely resist changes in tree species composition as an effect of direct ingrowth by stand-forming species. Disturbance events seem necessary to create opportunities for climatically ‘better suited and site-adapted’ species to (re-)establish and therefore potentially catalyse tree species turnover under environmental changes. We detected a reduction of tree species that were—centuries ago—cultivated outside their primary natural range, in particular P. abies, or depended on traditional management practices (Pinus sylvestris, Castanea sativa), which may inform us on how the projected increase in disturbance frequency and severity might filter tree species composition and hereby alter forest structure.

Herbivore Population Dynamics in Response to Plant Allocation Strategies

When herbivores feed, plants may respond by altering the quantity of edible biomass available to future feeders through mechanisms such as compensatory regrowth of edible structures or allocation of biomass to inedible reserves. Previous work showed that some forms of compensatory regrowth can drive insect outbreaks, but this work assumed regrowth occurred without any energetic cost to the plant. While this is a useful simplifying assumption for gaining preliminary insights, plants face an inherent trade-off between allocating energy to regrowth versus storage. Therefore, we cannot truly understand the role of compensatory regrowth in driving insect outbreaks without continuing on to more realistic scenarios. In this paper, we model the interaction between insect herbivores and plants that have a trade-off between compensatory regrowth and allocation to inedible reserves in response to herbivory. We found that the plant's allocation strategy, described in our model by parameters representing the strength of the overcompensatory response and the rates at which energy is stored and mobilized for growth, strongly affect whether herbivore outbreaks occur. Additional factors, such as the strength of food limitation and herbivore interference while feeding, influence the frequency of the outbreaks. Overall, we found a possible new role of overcompensation to promote herbivore fluctuations when it co-occurs with allocation to inedible reserves. We highlight the importance of considering trade-offs between tolerance mechanisms that plants use in response to herbivory by showing that new dynamics arise when different plant allocation strategies occur simultaneously.

Individual Sick Fir Tree (Abies mariesii) Identification in Insect Infested Forests by Means of UAV Images and Deep Learning

&lt;p&gt;Insect outbreaks are a recurrent natural phenomenon in forest ecosystems expected to increase due to climate change. Recent advances in Unmanned Aerial Vehicles (UAV) and Deep Learning (DL) Networks provide us with tools to monitor them. In this study we used nine orthomosaics and normalized Digital Surface Models (nDSM) to detect and classify healthy and sick Maries fir trees as well as deciduous trees. This study aims at automatically classifying treetops by means of a novel computer vision treetops detection algorithm and the adaptation of existing DL architectures. Considering detection alone, the accuracy results showed 85.70% success. In terms of detection and classification, we were able to detect/classify correctly 78.59% of all tree classes (39.64% for sick fir). However, with data augmentation, detection/classification percentage of the sick fir class rose to 73.01% at the cost of the result accuracy of all tree classes that dropped 63.57%. The implementation of UAV, computer vision and DL techniques contribute to the development of a new approach to evaluate the impact of insect outbreaks in forest.&lt;/p&gt;

Emergent vulnerability to climate-driven disturbances in European forests

Forest disturbance regimes are expected to intensify as Earth’s climate changes. Quantifying forest vulnerability to disturbances and understanding the underlying mechanisms is crucial to develop mitigation and adaptation strategies. However, observational evidence is largely missing at regional to continental scales. Here, we quantify the vulnerability of European forests to fires, windthrows and insect outbreaks during the period 1979–2018 by integrating machine learning with disturbance data and satellite products. We show that about 33.4 billion tonnes of forest biomass could be seriously affected by these disturbances, with higher relative losses when exposed to windthrows (40%) and fires (34%) compared to insect outbreaks (26%). The spatial pattern in vulnerability is strongly controlled by the interplay between forest characteristics and background climate. Hotspot regions for vulnerability are located at the borders of the climate envelope, in both southern and northern Europe. There is a clear trend in overall forest vulnerability that is driven by a warming-induced reduction in plant defence mechanisms to insect outbreaks, especially at high latitudes.

Individual Sick Fir Tree (Abies mariesii) Identification in Insect Infested Forests by Means of UAV Images and Deep Learning

Insect outbreaks are a recurrent natural phenomenon in forest ecosystems expected to increase due to climate change. Recent advances in Unmanned Aerial Vehicles (UAV) and Deep Learning (DL) Networks provide us with tools to monitor them. In this study we used nine orthomosaics and normalized Digital Surface Models (nDSM) to detect and classify healthy and sick Maries fir trees as well as deciduous trees. This study aims at automatically classifying treetops by means of a novel computer vision treetops detection algorithm and the adaptation of existing DL architectures. Considering detection alone, the accuracy results showed 85.70% success. In terms of detection and classification, we were able to detect/classify correctly 78.59% of all tree classes (39.64% for sick fir). However, with data augmentation, detection/classification percentage of the sick fir class rose to 73.01% at the cost of the result accuracy of all tree classes that dropped 63.57%. The implementation of UAV, computer vision and DL techniques contribute to the development of a new approach to evaluate the impact of insect outbreaks in forest.