AI News, Artificial Intelligence for Computational Sustainability: A Lab Companion/Biodiversity

Artificial Intelligence for Computational Sustainability: A Lab Companion/Biodiversity

The scale of biodiversity ranges from the variation of genes within a given species population, to the variation of life forms across an ecosystem, biome, or an entire planet.

Species distribution modeling is the process of using computational methods to predict the geographic distribution of one or more species based on a combination of environmental factors (including climate, land cover, water depth, etc.), resource availability, and the distribution of other species.

Species distribution models can be used to assess climate change impacts, evaluate conservation policies, measure the effects of deforestation, and prioritize areas for policy intervention.

Combining climate with other influential factors for modelling the impact of climate change on species distribution

We tested two approaches to forecast species distributions while balancing the impact of climate change against the inertia promoted by other influential factors that have been forecast as not changing.

This enabled the construction of models that combined climate with the other explanatory factors, to be later extrapolated to the future by replacing current climatic and human values with those expected from each emission and socio-economic scenario, while preserving spatial and topographic variables in the model.

Tree Habitat Shifts - Species Distribution Models

William Hargrove, Southern Research Station Species distribution models (SDMs) project future species habitats based on statistical associations between species occurrence or abundance and environmental (predictor) variables thought to influence habitat suitability (1).

SDMs can approximate potential species habitat for scores of species using multiple future scenarios of climate and human-based decisions, and thus can be useful for conservation and management, especially since ignoring the inevitability of future changes in a rapidly changing climate is not a realistic option (8).

SDMs have limitations, however, including assumptions that the selected variables reflect the niche requirements of a species, that species are in equilibrium with their suitable habitat, that predictions can be made into novel climates and land covers, that the effects of adaptation and evolution are minimal over the modeled timeframe, and that the effects of biotic interactions (including human interactions) are minimal (9, 10, 4, 11 ).

It is also important to realize that not all SDMs are equal – within a series of species run with a particular methodology, some models will perform much better than others, as a function of the quantity and quality of input data.

The “ForeCASTS” project of Hargrove and Potter (13) uses 17 climatic, soil-related (edaphic), and topographic variables to model environmental niches and geographic ranges for more than 200 tree species under current and future climates, nationally and globally.

ForeCASTS partitions climatic change risk into three separate metrics: percent change in suitable area under future climates, percent overlap of present and future suitable ranges, and average nonzero Minimum Required Migration (MRM) distance.

McKenney and colleagues (14) (15) used heat and moisture climatic variables to model tree species, and projected an average northward movement of the climate habitat for 130 North American tree species of roughly 700 km, across three scenarios, by end of century.

Crookston and colleagues (16) modified the Forest Vegetation Simulator to assess presence/absence for 74 tree species of the western United States and to modify site index to account for expected climate effects under various GCM/emission scenarios.

The outputs in the TreeAtlas are somewhat different than the rest in that they are based on abundance, not a binary presence/absence, and the Modification Factors add a level of interpretability by including information on disturbances and biological characteristics not readily included in SDMs (20).

In an evaluation of several SDMs, (23) asserted that for conservation purposes, there is, at least sometimes, an inherent bias for models to over-estimate climate-driven vulnerability to extirpation, and are therefore generally more useful in estimating future habitat (poleward or upslope) than in estimating where current habitat will no longer exist under future states.

a species having little overlap between present and future ranges, and with a large average minimum required migration distance may be a candidate for human-assisted migration (discussed elsewhere on this site), for example.

Considerations for Building Climate-based Species Distribution Models1

Climate plays an important role in the distribution of species, and past periods of climate change have corresponded with species’

In this example, the species occurrences points (black dots) fall within a certain range of temperatures (represented by different colors, ranging from blue [cooler] to red [warmer]) in the present time period (upper left).

In order to measure the effect of these choices, scientists can build two models in exactly the same way except for one parameter (e.g., including a land-use variable or excluding it), and then compare the two model’s outputs.

Table 1 summarizes the SDM choices that were covered in these projects, along with the section(s) in this document that address each particular choice, the strength of each choice’s effect on SDM outputs, and recommendations related to each choice for scientists building species distribution models.

Our results for these 12 species showed that neither model performance nor the prediction maps (for the current time period only) were significantly different depending on which contemporary climate dataset was used (Watling et al.

Given this result, we found no reason to prefer either of the contemporary climate datasets, concluding that modelers can base their choice of dataset on practical aspects such as availability, spatial resolution, or geographic coverage.

To predict climate in future decades and centuries, climate scientists employ global climate models (GCMs), which incorporate atmospheric, oceanic, land, sea ice, and other relevant components to simulate global climate patterns.

Global climate models are useful for projecting climate changes over large areas (e.g., continents), but due to their coarse scale, less useful for representing local or regional climates—the scales at which conservation planning generally takes place.

(create higher-resolution) projections from GCMs to much finer scales (e.g., one prediction every 1–50 km), but are limited to one region, using information on factors that influence the climate for that particular region.

downscaling, which uses statistical relationships between local and global factors influencing climate to downscale GCM projections (for either one region or the entire world), rather than developing a new climate model (as in RCMs).

For example, for the Everglade snail kite (Rostrhamus sociabilis plumbeus), the SDM prediction map using non-RCM projections predicts loss of suitability throughout much of southern Florida, but one using RCM projections does not (Figure 5).

time period (2050) SDM prediction maps using non-RCM (left) and RCM (right) climate datasets for the Everglade snail kite, illustrating the absence of suitable conditions in southern Florida predicted by the non-RCM model.

Contemporary climate datasets like CRU and WorldClim are often prepared as monthly averages (e.g., mean temperature in January, mean precipitation in May) or as bioclimate variables, which describe seasonal conditions and/or climate extremes (e.g., maximum temperature of the warmest month, precipitation of the driest season).

Bioclimate variables are generally assumed to be more informative for SDMs because certain climatic extremes may be directly limiting to species due to tolerance limits for certain hot, cold, dry, or wet extremes.

In addition, for SDMs for species with large ranges, bioclimate variables may be preferable to monthly because of the differences in seasons between the northern and southern hemispheres (for example, the temperature in January represents mid-winter in the North, but mid-summer in the South, and a species occurring in both hemispheres would experience a wide range of conditions in the same calendar month).

time period SDM prediction maps for models built using monthly climate variables (left) and bioclimate variables (right) for the American crocodile, with greatest discrepancies in suitability found in extreme southern Florida and the Florida Keys.Credit: David Bucklin [Click thumbnail to enlarge.]

To test this, we compared models built with climate variables only to those built with climate variables plus variables from several different sets (including land use, human influence, and extreme weather).

Using metrics that calculate how important individual variables are within a model, we found that climate variables were generally much more important than non-climate variables, regardless of which non-climate variables were combined with them (Bucklin et al.

Performance metrics were not highly variable among any of the models, though we did find that the climate + human-influence models performed significantly better than climate-only models, and that prediction maps from these two models were also the most different from one another.

focusing on a number of choices of input data and modeling methods (some also addressed in previous sections), including: This analysis highlighted each factor’s relative contribution to SDM variation (uncertainty).

Models were run for 15 species for every combination of the 7 factors, resulting in 48 different models and prediction maps for the contemporary period, and 288 prediction maps (48 ×

(It is important to note, however, that in many SDM studies, modelers do not use more than one algorithm.) In prediction maps, though, a small amount of variation was also attributable to GCM (for future predictions) and the variable selection process (Figure 8).

For example, SDM users employing ensemble methods could combine prediction maps from multiple algorithms, GCMs, or even species (if they are considering how a group of species may respond to climate change).

showing the partitioning of variance (a measure of how strongly a factor contributes to variation in model outputs) associated with seven sources of uncertainty in species distribution models, indicating that algorithm is a major source of variation in species distribution models.Credit: Adapted by authors from Watling et al.

Results of this work suggest that scientists building SDMs for estimations of wildlife responses to future climate change should focus on using a multiple-algorithm ensemble to project the models for several different representations of future climate.

DOI:10.1016/j.ecolmodel.2015.03.017 The Institute of Food and Agricultural Sciences (IFAS) is an Equal Opportunity Institution authorized to provide research, educational information and other services only to individuals and institutions that function with non-discrimination with respect to race, creed, color, religion, age, disability, sex, sexual orientation, marital status, national origin, political opinions or affiliations.

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