Multi-model comparison on the effects of climate change on tree species in the eastern U.S.: results from an enhanced niche model and process-based ecosystem and landscape models

Context

Species distribution models (SDM) establish statistical relationships between the current distribution of species and key attributes whereas process-based models simulate ecosystem and tree species dynamics based on representations of physical and biological processes. TreeAtlas, which uses DISTRIB SDM, and Linkages and LANDIS PRO, process-based ecosystem and landscape models, respectively, were used concurrently on four regional climate change assessments in the eastern Unites States.

Objectives

We compared predictions for 30 species from TreeAtlas, Linkages, and LANDIS PRO, using two climate change scenarios on four regions, to derive a more robust assessment of species change in response to climate change.

Methods

We calculated the ratio of future importance or biomass to current for each species, then compared agreement among models by species, region, and climate scenario using change classes, an ordinal agreement score, spearman rank correlations, and model averaged change ratios.

Results

Comparisons indicated high agreement for many species, especially northern species modeled to lose habitat. TreeAtlas and Linkages agreed the most but each also agreed with many species outputs from LANDIS PRO, particularly when succession within LANDIS PRO was simulated to 2300. A geographic analysis showed that a simple difference (in latitude degrees) of the weighted mean center of a species distribution versus the geographic center of the region of interest provides an initial estimate for the species’ potential to gain, lose, or remain stable under climate change.

Conclusions

This analysis of multiple models provides a useful approach to compare among disparate models and a more consistent interpretation of the future for use in vulnerability assessments and adaptation planning.

     

Modeling the invasive emerald ash borer risk of spread using a spatially explicit cellular model

The emerald ash borer (EAB, Agrilus planipennis) is decimating native ashes (Fraxinus sp.) throughout midwestern North America, killing millions of trees over the years. With plenty of ash available throughout the continent, the spread of this destructive insect is likely to continue. We estimate that the insect has been moving along a “front” at about 20 km/year since about 1998, but more alarming is its long-range dispersal into new locations facilitated by human activities. We describe a spatially explicit cell-based model used to calculate risk of spread in Ohio, by combining the insect’s flight and short-range dispersal (“insect flight”) with human-facilitated, long-range dispersal (“insect ride”). This hybrid model requires estimates of EAB abundance, ash abundance, major roads and traffic density, campground size and usage, distance from the core infested zone, wood products industry size and type of wood usage, and human population density. With the “insect flight” model, probability of movement is dependent on EAB abundance in the source cells, the quantity of ash in the target cells, and the distances between them. With the “insect-ride” model, we modify the value related to ash abundance based on factors related to potential human-assisted movements of EAB-infested ash wood or just hitchhiking insects. We attempt to show the advantage of our model compared to statistical approaches and to justify its practical value to field managers working with imperfect knowledge. We stress the importance of the road network in distributing insects to new geographically dispersed sites in Ohio, where 84% were within 1 km of a major highway.     

Modeling tsunami damage in Aceh: a reply

In reply to the critique of Baird and Kerr, we emphasize that our model is a generalized vulnerability model, built from easily acquired data from anywhere in the world, to identify areas with probable susceptibility to large tsunamis—and discuss their other criticisms in detail. We also show that a rejection of the role of trees in helping protect vulnerable areas is not justified in light of existing evidence.     

A multistage decision support framework to guide tree species management under climate change via habitat suitability and colonization models,and a knowledge-based scoring system

Context

No single model can capture the complex species range dynamics under changing climates—hence the need for a combination approach that addresses management concerns.

Objective

A multistage approach is illustrated to manage forested landscapes under climate change. We combine a tree species habitat model—DISTRIB II, a species colonization model—SHIFT, and knowledge-based scoring system—MODFACs, to illustrate a decision support framework.

Methods

Using shortleaf pine (Pinus echinata) and sugar maple (Acer saccharum) as examples, we project suitable habitats under two future climate change scenarios (harsh, Hadley RCP8.5 and mild CCSM RCP4.5 at ~2100) at a resolution of 10 km and assess the colonization likelihood of the projected suitable habitats at a 1 km resolution; and score biological and disturbance factors for interpreting modeled outcomes.

Results

Shortleaf pine shows increased habitat northward by 2100, especially under the harsh scenario of climate change, and with higher possibility of natural migration confined to a narrow region close to the current species range boundary. Sugar maple shows decreased habitat and has negligible possibility of migration within the US due to a large portion of its range being north of the US border. Combination of suitable habitats with colonization likelihoods also allows for identification of potential locations appropriate for assisted migration, should that be deemed feasible.

Conclusion

The combination of these multiple components using diverse approaches leads to tools and products that may help managers make management decisions in the face of a changing climate.

     

A Gis-derived integrated moisture index to predict forest composition and productivity of Ohio forests (U.S.A.)

A geographic information system (GIS) approach was used in conjunction with forest-plot data to develop an integrated moisture index (IMI), which was then used to predict forest productivity (site index) and species composition for forests in Ohio. In this region, typical of eastern hardwoods across the Midwest and southern Appalachians, topographic aspect and position (rather than elevation) change drastically at the fine scale and strongly influence many ecological functions. Elevational contours, soil series mapping units, and plot locations were digitized for the Vinton Furnace Experimental Forest in southeastern Ohio and gridded to 7.5-m cells for GIS modeling. Several landscape features (a slope-aspect shading index, cumulative flow of water downslope, curvature of the landscape, and water-holding capacity of the soil) were used to create the IMI, which was then statistically analyzed with site-index values and composition data for plots. On the basis of IMI values for forest land harvested in the past 30 years, we estimated oak site index and the percentage composition of two major species groups in the region: oak (Quercus spp.), and yellow poplar (Liriodendron tulipifera) plus black cherry (Prunus serotina). The derived statistical relationships were then applied in the GIS to create maps of site index and composition, and verified with independent data. The maps show the oaks will dominate on dry, ridge top positions (i.e., low site index), while the yellow poplar and black cherry will predominate on mesic sites. Digital elevation models with coarser resolution (1:24K, 1:100K, 1:250K) also were tested in the same manner. We had generally good success for 1:24K, moderate success for 1:100K, but no success for 1:250K data. This simple and portable approach has the advantage of using readily available GIS information which is time-invariant and requires no fieldwork. The IMI can be used to better manage forest resources where moisture is limiting and to predict how the resource will change under various forms of ecosystem management.     

Defining landscape-level forest types: application of latent Dirichlet allocation to species distribution models

Landscape Ecology - Forest type (FT) classification provides useful information to ecologists and forest managers by representing similar sites based on species dominance. Various methods have been...     

Neural networks in fisheries research

Piscimetrics deals with software implementation of experimental design, second-generation artificial intelligence tools, viz. Neural Nets (NNs), genetic algorithms, Fuzzy Logic, Expert Systems, Wavelets and Image analysis in the field of fisheries. A brief sketch of NNs is followed by a review of their applications in forecasting, classification, distribution and fisheries management since 1978. Forecasting in fisheries covers distribution of eggs, recruitment, fish growth/age, biomass and fish catch. Other major areas are identification, abundance and food products, environmental factors and collapse of fishery industry. The data structures are given in tensorial notation. The need for the paradigm shift from classical to multi-level hybrid NNs is emphasized.