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1. Deep forest: Neural network reconstruction of the Lyman-α forest

   https://doi.org/10.1093/mnras/stab2041  

   Huang, Lawrence; Croft, Rupert A; Arora, Hitesh (August 2021, Monthly Notices of the Royal Astronomical Society)

   null (Ed.)

   ABSTRACT We explore the use of Deep Learning to infer physical quantities from the observable transmitted flux in the Ly α forest. We train a Neural Network using redshift z = 3 outputs from cosmological hydrodynamic simulations and mock data sets constructed from them. We evaluate how well the trained network is able to reconstruct the optical depth for Ly α forest absorption from noisy and often saturated transmitted flux data. The Neural Network outperforms an alternative reconstruction method involving log inversion and spline interpolation by approximately a factor of 2 in the optical depth root mean square error. We find no significant dependence in the improvement on input data signal to noise, although the gain is greatest in high optical depth regions. The Ly α forest optical depth studied here serves as a simple, one dimensional, example but the use of Deep Learning and simulations to approach the inverse problem in cosmology could be extended to other physical quantities and higher dimensional data. 

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2. Toward a forest transition across the Brazilian Atlantic Forest biome

   https://doi.org/10.3389/ffgc.2023.1071495  

   Bicudo da Silva, Ramon Felipe; Moran, Emilio; Viña, Andrés; Millington, James D.; Dou, Yue; Vieira, Simone A.; Lopez, Maria Claudia; Liu, Jianguo (June 2023, Frontiers in Forests and Global Change)

   The world has entered the United Nations Decade on Ecosystem Restoration (2021–2030), yet many regions of the world still face environmental degradation. In this context a question arises: under what conditions may a given region shift from a trajectory of environmental degradation to environmental recovery? Answering this question constitutes an important endeavor for the scientific community, policymakers, and organizations leading the planning and implementation of restoration projects. This study examines some of the human-environment conditions underpinning the net gains in natural forest cover in a region that has experienced environmental degradation: the Atlantic Forest biome, Brazil. Using land-use/cover (LULC) data, we assessed the loss and successive gain in forest cover during the 1987–2001 and 2001–2015 periods. Municipality-level statistics on agriculture and economy, together with LULC and biophysical data, were used to develop models for assessing forest cover trajectories. Of the 1,972 municipalities experiencing net forest loss during the 1987–2001 period, 59% shifted their trajectory to a net gain during the 2001–2015 period. This shift, known as forest transition, followed a complex socio-economic pathway characterized by (i) the stagnation of traditional agricultural activities favoring the replacement of pasturelands by mechanized agriculture, and (ii) the emergence of non-agricultural rural activities together with multifunctional landscapes. Furthermore, a trend of decrease in precipitation seems to be associated with land abandonment, thus, favoring the return of natural vegetation. Our findings suggest that forest transition at the biome level is possible if environmental and land regulations take advantage of specific context-dependent situations that foster net forest gains. 

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3. Forest Inventory of a Northern Hardwood Forest: Bird Area at the Hubbard Brook Experimental Forest, 1981

   https://doi.org/10.6073/pasta/18a5a1790d985b38a47a4c79c2f673ef  

   Sherry, Thomas W; Holmes, Richard T; Siccama, Thomas G (January 2024, Environmental Data Initiative)

   The forest inventory surveys in the bird area were initiated in 1981 and transects were made permanent in 1991. The inventory is representative of approximately 2.5 km-squared of mid elevation northern hardwood forest. It consists of a total inventory of all trees >=10 cm dbh, within each of four 10 m wide belt transects. The parallel transects are placed approximately 200 m apart and run roughly in an east-west direction for 2200 to 2900 m. In 1991, each live stem >=10 cm dbh was tagged with a unique number. Tree vigor is assessed every two years and diameter is remeasured every ten years. Every two years, new tags are placed on stems that have grown into the 10 cm diameter class. A survey of smaller trees (>=2 to <10 cm dbh) was first taken in 1991 and is resurveyed every ten years. This dataset includes the initial inventory values measured in 1981. The full timeline of tree inventory data for this site is available at https://doi.org/10.6073/pasta/58dfdebfd1b6440510def2394ab92c53 These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station. 

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4. Seeing the forest for the trees: Assessing genetic offset predictions from gradient forest

   https://doi.org/10.1111/eva.13354  

   Láruson, Áki Jarl; Fitzpatrick, Matthew C.; Keller, Stephen R.; Haller, Benjamin C.; Lotterhos, Katie E. (February 2022, Evolutionary Applications)

   Abstract Gradient Forest (GF) is a machine learning algorithm designed to analyze spatial patterns of biodiversity as a function of environmental gradients. An offset measure between the GF‐predicted environmental association of adapted alleles and a new environment (GF Offset) is increasingly being used to predict the loss of environmentally adapted alleles under rapid environmental change, but remains mostly untested for this purpose. Here, we explore the robustness of GF Offset to assumption violations, and its relationship to measures of fitness, using SLiM simulations with explicit genome architecture and a spatial metapopulation. We evaluate measures of GF Offset in: (1) a neutral model with no environmental adaptation; (2) a monogenic “population genetic” model with a single environmentally adapted locus; and (3) a polygenic “quantitative genetic” model with two adaptive traits, each adapting to a different environment. We found GF Offset to be broadly correlated with fitness offsets under both single locus and polygenic architectures. However, neutral demography, genomic architecture, and the nature of the adaptive environment can all confound relationships between GF Offset and fitness. GF Offset is a promising tool, but it is important to understand its limitations and underlying assumptions, especially when used in the context of predicting maladaptation. 

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5. Compounding the Disturbance: Family Forest Owner Reactions to Invasive Forest Insects

   https://doi.org/10.1016/j.ecolecon.2019.106461  

   Markowski-Lindsay, Marla; Borsuk, Mark E.; Butler, Brett J.; Duveneck, Matthew J.; Holt, Jonathan; Kittredge, David B.; Laflower, Danelle; MacLean, Meghan Graham; Orwig, David; Thompson, Jonathan R. (January 2020, Ecological Economics)