A Review of LANDIS and Other Forest Landscape Models for Integration with Wildlife Models

Author(s):  
Hong S. He
2015 ◽  
Vol 300 ◽  
pp. 89-101 ◽  
Author(s):  
Yu Liang ◽  
Hong S. He ◽  
Wen J. Wang ◽  
Jacob S. Fraser ◽  
ZhiWei Wu ◽  
...  

2005 ◽  
Author(s):  
◽  
Jian Yang

Fire disturbance plays an important role in shaping ecosystem dynamics and vegetation patterns in many forested landscapes. This dissertation is dedicated to the modeling of fire disturbance in spatially explicit and stochastic forest landscape models, in particular, LANDIS model. A hierarchical fire frequency model was proposed to model fire occurrence. Four representative fire spread simulation methods were implemented in LANDIS. I compared fire patterns simulated using the four fire spread simulation methods under two fire occurrence process scenarios that are fuel-independent and fuel-dependent. Results showed that the incorporation of fuel into fire occurrence modeling greatly changes simulated fire patterns. Lastly, I used point process modeling approach to study the effects humans and other factors on the probability of fire occurrence in the Missouri Ozark Highlands. The spatial distribution of fire occurrence density, which is one of the results from point pattern modeling, can be further used in LANDIS as an input map for simulating fire occurrence.


2021 ◽  
Vol 9 ◽  
Author(s):  
María Suárez-Muñoz ◽  
Marco Mina ◽  
Pablo C. Salazar ◽  
Rafael M. Navarro-Cerrillo ◽  
José L. Quero ◽  
...  

The use of spatially interactive forest landscape models has increased in recent years. These models are valuable tools to assess our knowledge about the functioning and provisioning of ecosystems as well as essential allies when predicting future changes. However, developing the necessary inputs and preparing them for research studies require substantial initial investments in terms of time. Although model initialization and calibration often take the largest amount of modelers’ efforts, such processes are rarely reported thoroughly in application studies. Our study documents the process of calibrating and setting up an ecophysiologically based forest landscape model (LANDIS-II with PnET-Succession) in a biogeographical region where such a model has never been applied to date (southwestern Mediterranean mountains in Europe). We describe the methodological process necessary to produce the required spatial inputs expressing initial vegetation and site conditions. We test model behaviour on single-cell simulations and calibrate species parameters using local biomass estimations and literature information. Finally, we test how different initialization data—with and without shrub communities—influence the simulation of forest dynamics by applying the calibrated model at landscape level. Combination of plot-level data with vegetation maps allowed us to generate a detailed map of initial tree and shrub communities. Single-cell simulations revealed that the model was able to reproduce realistic biomass estimates and competitive effects for different forest types included in the landscape, as well as plausible monthly growth patterns of species growing in Mediterranean mountains. Our results highlight the importance of considering shrub communities in forest landscape models, as they influence the temporal dynamics of tree species. Besides, our results show that, in the absence of natural disturbances, harvesting or climate change, landscape-level simulations projected a general increase of biomass of several species over the next decades but with distinct spatio-temporal patterns due to competitive effects and landscape heterogeneity. Providing a step-by-step workflow to initialize and calibrate a forest landscape model, our study encourages new users to use such tools in forestry and climate change applications. Thus, we advocate for documenting initialization processes in a transparent and reproducible manner in forest landscape modelling.


Forests ◽  
2019 ◽  
Vol 10 (1) ◽  
pp. 25 ◽  
Author(s):  
Jacob S. Fraser ◽  
Wen J. Wang ◽  
Hong S. He ◽  
Frank R. Thompson

Fire is a multi-scale process that is an important component in determining ecosystem age structures and successional trajectories across forested landscapes. In order to address questions regarding fire effects over large spatial scales and long temporal scales researchers often employ forest landscape models which can model fire as a spatially explicit disturbance. Within forest landscape models site-level fire effects are often simplified to the species, functional type, or cohort level due to time or computational resource limitations. In this study we used a subset of publicly available U.S. Forest Service forest inventory data (FIA) to estimate short-term fire effects on tree densities across multiple stem diameter classes in two ecological sections in the central and southern United States. We found that FIA plots where low-intensity fires occurred within the preceding five years in the Ozark Highlands ecological section had significantly reduced stem densities in the two smallest diameter classes and in the Gulf Coastal Plains and Flatwoods fire reduced stem densities in the three smallest diameter classes. Using an independent subset of FIA plots we then parameterized and calibrated a forest landscape model to simulate site-level fire effects using a logistic regression based method and compare the results to previous methods of modeling fire effects. When representative landscapes from both study areas were simulated under a low-intensity fire regime using a forest landscape model the logistic regression probability method of modeling fire effects produced a similar reduction in stem densities while the previous age-cohort method overestimated density reductions across diameter classes. A more realistic representation of fire effects, particularly in low intensity fire regimes, increases the utility of forest landscape models as tools for planning and management.


2004 ◽  
Vol 180 (1) ◽  
pp. 7-19 ◽  
Author(s):  
David J. Mladenoff

Ecosphere ◽  
2016 ◽  
Vol 7 (4) ◽  
Author(s):  
Eric J. Gustafson ◽  
Arjan M. G. De Bruijn ◽  
Brian R. Miranda ◽  
Brian R. Sturtevant

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