Tree adaptive growth (TAG) model: a life-history theory-based analytical model for post-thinning forest stand dynamics

Background

Understanding stand dynamics is essential for predicting future wood supply and associated ecosystem services for sustainable forest management. The dynamics of natural stands can be characterized by age-dependent growth and yield models. However, dynamics in managed stands appear somewhat different from that of natural stands, especially with difficulties in explaining the phenomenon of post-thinning overcompensation, based upon some long-term observations. Though overcompensation is an ideal outcome for the forest sector, it had been largely treated as an outlier and thus ignored or dismissed as "out-of-the-ordinary".

Methodology

We developed a life history theory-based, state-dependent model of Tree Adaptive Growth (TAG) to investigate this phenomenon and verified that overcompensation should be a common outcome in post-thinning forest stands when the stand growth over time is sigmoid shaped. TAG posits that individual trees will invest proportionately more into growth following thinning because it is evolutionarily adaptive to do so.

Results

Our investigation of the model's behavior unearthed diverse stand growth patterns similar to that which is observed in the empirical datasets and predicted by a statistics-based Tree's Compensatory Growth (TreeCG) model.

Conclusion

A simple, theory-driven, analytical model, TAG, can reproduce the diverse growth patterns in post-thinning stands and thus assist addressing silviculture-related issues. The model can be applied to various jurisdictions even without detailed regional growth and yield relationships and is capable of incorporating the effects of other time sensitive factors like fertilization, pruning, and climate change.

Short- and long-term wildfire threat when adapting infrastructure for wildlife conservation in the boreal forest

Managers designing infrastructure in fire-prone wildland areas require assessments of wildfire threat to quantify uncertainty due to future vegetation and climatic conditions. In this study, we combine wildfire simulation and forest landscape composition modeling to identify areas that would be highly susceptible to wildfire around a proposed conservation corridor in Québec, Canada. In this measure, managers have proposed raising the conductors of a new 735-kV hydroelectric powerline above the forest canopy within a wildlife connectivity corridor to mitigate the impacts to threatened boreal woodland caribou (Rangifer tarandus). Retention of coniferous vegetation, however, can increase the likelihood of an intense wildfire damaging powerline infrastructure. To assess the likelihood of high-intensity wildfires for the next 100 years, we evaluated three time periods (2020, 2070, 2120), three climate scenarios (observed, RCP 4.5, RCP 8.5), and four vegetation projections (static, no harvest, extensive harvesting, harvesting excluded in protected areas). Under present-day conditions, we found a lower probability of high-intensity wildfire within the corridor than in other parts of the study area, due to the protective influence of a nearby, poorly regenerated burned area. Wildfire probability will increase into the future, with strong, weather-induced inflation in the number of annual ignitions and wildfire spread potential. However, a conversion to less-flammable vegetation triggered by interactions between climate change and disturbance may attenuate this trend. By addressing the range of uncertainty of future conditions, we present a robust strategy to assist in decision-making about long-term risk management for both the proposed conservation measure and the powerline.

Multi-Objective Drug Design Based on Graph-Fragment Molecular Representation and Deep Evolutionary Learning

Drug discovery is a challenging process with a huge molecular space to be explored and numerous pharmacological properties to be appropriately considered. Among various drug design protocols, fragment-based drug design is an effective way of constraining the search space and better utilizing biologically active compounds. Motivated by fragment-based drug search for a given protein target and the emergence of artificial intelligence (AI) approaches in this field, this work advances the field of in silico drug design by (1) integrating a graph fragmentation-based deep generative model with a deep evolutionary learning process for large-scale multi-objective molecular optimization, and (2) applying protein-ligand binding affinity scores together with other desired physicochemical properties as objectives. Our experiments show that the proposed method can generate novel molecules with improved property values and binding affinities.

Genome-scale phylogeography resolves the native population structure of the Asian longhorned beetle, Anoplophora glabripennis (Motschulsky)

Human-assisted movement has allowed the Asian longhorned beetle (ALB, Anoplophora glabripennis (Motschulsky)) to spread beyond its native range and become a globally regulated invasive pest. Within its native range of China and the Korean peninsula, human-mediated dispersal has also caused cryptic translocation of insects, resulting in population structure complexity. Previous studies used genetic methods to detangle this complexity but were unable to clearly delimit native populations which is needed to develop downstream biosurveillance tools. We used genome-wide markers to define historical population structure in native ALB populations and contemporary movement between regions. We used genotyping-by-sequencing to generate 6102 single-nucleotide polymorphisms (SNPs) and amplicon sequencing to genotype 53 microsatellites. In total, we genotyped 712 individuals from ALB's native distribution. We observed six distinct population clusters among native ALB populations, with a clear delineation between northern and southern groups. Most of the individuals from South Korea were distinct from populations in China. Our results also indicate historical divergence among populations and suggest limited large-scale admixture, but we did identify a restricted number of cases of contemporary movement between regions. We identified SNPs under selection and describe a clinal allele frequency pattern in a missense variant associated with glycerol kinase, an important enzyme in the utilization of an insect cryoprotectant. We further demonstrate that small numbers of SNPs can assign individuals to geographic regions with high probability, paving the way for novel ALB biosurveillance tools.

Sympatrically breeding congeneric seabirds (Stercorarius spp.) from Arctic Canada migrate to four oceans

Polar systems of avian migration remain unpredictable. For seabirds nesting in the Nearctic, it is often difficult to predict which of the world's oceans birds will migrate to after breeding. Here, we report on three related seabird species that migrated across four oceans following sympatric breeding at a central Canadian high Arctic nesting location. Using telemetry, we tracked pomarine jaeger (Stercorarius pomarinus, n = 1) across the Arctic Ocean to the western Pacific Ocean; parasitic jaeger (S. parasiticus, n = 4) to the western Atlantic Ocean, and long-tailed jaeger (S. longicaudus, n = 2) to the eastern Atlantic Ocean and western Indian Ocean. We also report on extensive nomadic movements over ocean during the postbreeding period (19,002 km) and over land and ocean during the prebreeding period (5578 km) by pomarine jaeger, an irruptive species whose full migrations and nomadic behavior have been a mystery. While the small sample sizes in our study limit the ability to make generalizable inferences, our results provide a key input to the knowledge of jaeger migrations. Understanding the routes and migratory divides of birds nesting in the Arctic region has implications for understanding both the glacial refugia of the past and the Anthropocene-driven changes in the future.