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1. Fifty years of runoff response to conversion of old‐growth forest to planted forest in the H. J. Andrews Forest, Oregon, USA

   https://doi.org/10.1002/hyp.14168  

   Crampe, Emily A. ; Segura, Catalina ; Jones, Julia A. ( May 2021 , Hydrological Processes)

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2. Forest Inventory of a Northern Hardwood Forest: Bird Area, 1991 - present, Hubbard Brook Experimental Forest

   https://doi.org/10.6073/pasta/58dfdebfd1b6440510def2394ab92c53  

   Battles, John J ; Cleavitt, Natalie ( January 2022 )

   Abstract

   The forest inventory surveys in the bird area were initiated in 1981 and transects were made permanent in 1991 by Tom Siccama who created and designed this tree survey. The inventory is representative of approximately 2.5 km2 of mid elevation northern hardwood forest. The data set is particularly geared toward producing accurate mortality and recruitment estimates. It consists of a total inventory of all trees greater than or equal to 10 cm dbh within each of four 10 m wide belt transects. The parallel transects are placed approximately 200 m apart and 290° bearing in an east-west direction for 2200 to 2900 m. In 1991, each live stem greater than or equal to 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 (greater than or equal to 2 to less than 10 cm dbh) was first taken in 1991 and is resurveyed every ten years. This dataset includes 1991 and subsequent samplings. Data from an earlier sampling in 1981 can be found in: Sherry, T., D. Holmes, and T. Siccama. 2019. Forest Inventory of a Northern Hardwood Forest: Bird Area at the Hubbard Brook Experimental Forest, 1981 ver 7. Environmental Data Initiative. https://doi.org/10.6073/pasta/206b98f6553f1ff95cf584dd2185554e (Accessed 2021-09-16). 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. These data have been used in the following publication: Siccama, T.G., Fahey, T.J., Johnson, C.E., Sherry, T.W., Denny, E.G., Girdler, E.B., Likens, G.E., and Schwarz, P.A. 2007. Population and biomass dynamics of trees in a northern hardwood forest at Hubbard Brook. Can. J. For. Res. 37(4): 737–749. doi:10.1139/X06-261.
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3. Water yield following forest–grass–forest transitions

   https://doi.org/10.5194/hess-21-981-2017  

   Elliott, Katherine J. ; Caldwell, Peter V. ; Brantley, Steven T. ; Miniat, Chelcy F. ; Vose, James M. ; Swank, Wayne T. ( January 2017 , Hydrology and Earth System Sciences)

   Many currently forested areas in the southern Appalachians were harvested in the early 1900s and cleared for agriculture or pasture, but have since been abandoned and reverted to forest (old-field succession). Land-use and land-cover changes such as these may have altered the timing and quantity of water yield (Q). We examined 80 years of streamflow and vegetation data in an experimental watershed that underwent forest–grass–forest conversion (i.e., old-field succession treatment). We hypothesized that changes in forest species composition and water use would largely explain long-term changes in Q. Aboveground biomass was comparable among watersheds before the treatment (208.3 Mg ha⁻¹), and again after 45 years of forest regeneration (217.9 Mg ha⁻¹). However, management practices in the treatment watershed altered resulting species composition compared to the reference watershed. Evapotranspiration (ET) and Q in the treatment watershed recovered to pretreatment levels after 9 years of abandonment, then Q became less (averaging 5.4 % less) and ET more (averaging 4.5 % more) than expected after the 10th year up to the present day. We demonstrate that the decline in Q and corresponding increase in ET could be explained by the shift in major forest species from predominantly Quercus and Carya before treatment to predominantly Liriodendron and Acer through old-field succession. The annual change in Q can be attributed to changesmore »in seasonal Q. The greatest management effect on monthly Q occurred during the wettest (i.e., above median Q) growing-season months, when Q was significantly lower than expected. In the dormant season, monthly Q was higher than expected during the wettest months.« less
4. Heightened nest loss in tropical forest fragments despite higher predator load in core forest

   https://doi.org/10.1007/s42965-019-00032-1  

   Fernandez, Christopher M. ; Vera Alvarez, Maria D. ; Cove, Michael V. ( June 2019 , Tropical Ecology)
5. 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)

   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.