Ecological Niche Modeling

1. Ecological Niche Modeling 1 March 2011

2. Climate is often one of the most important factors that affect the distribution of a species. Climate encompasses the temperatures, humidity, rainfall, atmospheric particle count and numerous other meteorological factors in a given region over long periods of time Weather, in contrast, is the current temperature, humidity, rainfall, atmospheric particle count, etc. in a given region. Climate and weather

3. Hot air rises at the equator, sinks at the poles So, low pressure (= clouds & rain) at equator, high pressure (= clear skies) at poles Climate varies depending upon global circulation

4. Prevailing wind varies by latitude

5. Water heats more slowly than land, but retains heat longer than land So prevailing winds that pass over water during winter will be warmer (and wetter) than prevailing winds that pass over land London (UK) is located at 51� N latitude Ave Jan high is 6�C (43 �F) Ave Jan low is 1�C (33 �F) Winnipeg (Manitoba, Canada) is located at ~50� N latitude Ave Jan high is -13 �C (9 �F) Ave Jan low is -23 �C (-9 �F) Effects of water

6. Altitudinal zonation or the creation of zones to explain the different characteristic climates at different elevations. Altitudinal zonation

7. Altitudinal Zonation in Southwest

8. A life zone is an area with similar plant and animal communities Developed by Merrian in 1889 using the southwest as an example Lower Sonoran (e.g. creosote bush and Joshua Tree) Upper Sonoran (e.g. sagebrush, scrub oak) Transition (e.g. Ponderosa Pine) Canadian (e.g. Douglas-Fir, Quaking Aspen) Hudsonian (e.g. Engelman spruce) Arctic-Alpine (e.g. lichen, grass) Life zones

9. Holdridge Life Zones (1947)

10. Grinnell, J. 1917. The niche-relationship of the California Thrasher. Auk 34: 427-433. These various circumstances, which emphasize dependence upon cover, and adaptation in physical structure and temperament thereto, go to demonstrate the nature of the ultimate associational niche occupied by the California Thrasher. [I]ts range is determined by a narrow phase of conditions obtaining in the Chaparral association, within the California fauna, and within the Upper Sonoran life-zone. Niche history

11. Consider two independent environmental variables x1 and x2 which can be measured along ordinary rectangular coordinates. Let the limiting values permitting a species S1 to survive and reproduce be respectively x�1, x��1 for x1 and x�2, x��2 for x2 . An area is thus defined, each point of which corresponds to a possible environmental state permitting the species to exist indefinitely. We may now introduce another variable x3 and obtain a volume� In this way, an n-dimensional hypervolume is defined, every point of which corresponds to a state of the environment that would permit species S1 to exist indefinitely. Hutchinson (1957) niche definition

12. Niche A niche includes the animal�s limits of temperature,moisture, food and other factors. Fundamental vs. realized niches

13. An ecological niche model is a type of predictive model that describes a species range based on environmental variables at sites the species is known to inhabit. Generally, ecological niche models are used to generate predictive maps (called habitat suitability maps) of a species range Common variables include Elevation Proximity of water Temperature Distance to towns/cities Rainfall Etc. Soils Slope Aspect Ecological Niche Model

14. Organisms will have a preferred range of variables The preferred range can be described by a 2nd degree polynomial Polynomials

15. Started in 1989 The goal of the GAP Analysis Program is to keep common species common by identifying those species and plant communities that are not adequately represented in existing conservation lands. Common species are those not currently threatened with extinction. By identifying their habitats, GAP Analysis gives land managers and policy makers the information they need to make better-informed decisions when identifying priority areas for conservation. GAP analysis

17. 1. Map the landcover of the US 2. Map predicted distributions of vertebrate species in the US 3. document the representation of vertebrate species and land cover types in areas managed for the long-term maintenance of biodiversity 4. provide this information to the public and those entities charged with land use research, policy, planning, and management 5. build institutional cooperation in the application of this information to state and regional management activities. Five objectives of GAP Analysis

20. 1.) GARP Relies upon presence / absence data 2.) Maximum entropy Can use only presence data 3.) ENFA Uses only presence data Ecological Niche Models

21. GIGO � Garbage In, Garbage Out Presence data may not be randomly distributed throughout range Often biased towards areas with roads, rivers, or some other method of access Presence data may be spatially autocorrelated Researchers may gather multiple specimens of a species from a small area Sampling intensity may vary across regions & researchers Issues with data

22. Occurrence localities may contain errors Transcriptions error May not be precise enough to be useful Species may have been misidentified The number of occurrence records may be too low to generate a useful model Usually want at least 40-50 records Set of environmental variables used to describe niche may not be sufficient Errors may exist in environmental variables, particularly if they have been extrapolated from a small dataset Additional issues

23. GARP (Genetic Algorithm for Rule-set Prediction) uses a heuristic approach to identifying factors correlated with species presence / absence I.e. formulates a collection of rules that produce a binary prediction (i.e. presence / absence) Relies upon collecting presence / absence data Presence data can be gathered from museum specimens Absence data is more problematic GARP

24. Typically, you do �training�, where you run the GARP on a subset of your sites Each time you run a GARP, you get a slightly different map Consequently, GARP maps are typically overlaid on each other to get a composite map More GARP details

25. DesktopGARP (http://www.nhm.ku.edu/desktopgarp/) Runs on Win98, ME, NT4, 2000, and XP Doesn�t run on Mac, Linus, Unix openModeller (http://openmodeller.sourceforge.net/) Can be run on most versions of Windows, as well as Mac & Linux systems Sample data can be downloaded from (http://openmodeller.sourceforge.net/index.php?option=com_content&task=blogcategory&id=13&Itemid=16) Software for running GARP

26. Brown-throated Sloth

27. One-tailed ?2-statistics (or binomial probabilities if small samples sizes are used) determine whether test points fall into regions of predicted presence more often than expected by chance This is called an extrinsic measure of model significance The relative proportions of false negatives (omission errors) and false positives (commission errors) are shown in a matrix called a confusion matrix Evaluating GARP models

28. http://www.lifemapper.org/

29. Relies upon data provided to the GBIF GBIF (Global Biodiversity Information Facility) is essentially a central warehouse of georeferenced biodiversity data US has sent a letter of intent to join, but has not signed a memorandum of understanding As of February 2011, Lifemapper has modeled the distribution of 92,199 species Has enough data to model an additional 30,000 + species Need at least 50 georeferenced points before you can model Additional Lifemapper details

30. Sabal minor

31. In order to use GARP, you need to know not only where specimens were collected, but also where they were not found Absence data can be tricky because 1.) Species may not occur there; or 2.) Species may be present in small numbers but not be detected; or 3.) Habitat is suitable but species no longer is present due to historical reasons; or 4.) Species has been introduced and has not yet spread into the area �False absences� can bias models Absence errors

32. Information theory is a branch of applied mathematics and electrical engineering involving the quantification of information. Historically, information theory was developed to find fundamental limits on compressing and reliably communicating data. Since its inception it has broadened to find applications in many other areas, including statistical inference, natural language processing, cryptography generally, networks other than communication networks -- as in neurobiology, the evolution and function of molecular codes, model selection in ecology, thermal physics, quantum computing, plagiarism detection and other forms of data analysis. Information Theory

33. A key measure of information in the theory is known as entropy, which is usually expressed by the average number of bits needed for storage or communication. Intuitively, entropy quantifies the uncertainty involved when encountering a random variable. For example, a fair coin flip (2 equally likely outcomes) will have less entropy than a roll of a die (6 equally likely outcomes). Entropy

34. Jaynes (1957) wrote �Information theory provides a constructive criterion for setting up probability distributions on the basis of partial knowledge, and leads to a type of statistical inference which is called the maximum-entropy estimate. It is the least biased estimate possible on the given information; i.e. it is maximally noncommittal with regards to missing information.� This approach (Maxent) can be used to model species distributions Maximum Entropy

35. Use known localities of species on landscape Define geographic area and environmental variables of interest Engage in iterative models How it works - overview

37. Performed using Receiver Operating Characteristic (ROC) analysis This characterizes the performance of a model at all possible thresholds by summing up the area under the curve (AUC). The higher the AUC, the better the model. Evaluation of Model

38. Comparison of Maxent to GARP

39. Ecological Niche Factor Analysis (ENFA) is an alternative approach to using presence-only data. Similar to a principal components analysis Principal component analysis (PCA) involves a mathematical procedure that transforms a number of possibly correlated variables into a smaller number of uncorrelated variables called principal components. The first principal component accounts for as much of the variability in the data as possible, and each succeeding component accounts for as much of the remaining variability as possible. ENFA

40. However, the �components� an ENFA calculates have specific biological significance. The first factor is the �marginality� factor, which maximizes the difference in environmental conditions between the species niche and the general study area. Subsequent factors are �specialization� factors and are created by computing the direction that maximizes the remaining variance between the study area and the locations where the species were found. These factors can then be applied to the EGV maps to generate a habitat suitability map ENFA details continued

41. Alpine ibex (Capra ibex) were extirminated in Switzerland (due to overhunting) during the 19th century. They were reintroduced in 1911and are now widespread 34 ecogeographical variableswere examined using ENFA byHirzel et al. (2002) Ibex example

42. Ibex preferred steep, rocky, areas at high altitudes with good pastures Important variables

43. Hirzel et al. (2002)

44. Done using a continuous Boyce curve This is a threshold-independent technique The higher the value, the better the model The maximum value of the curve is called the F-value and can be used to compare how much the model differs from a random model Evaluation of ENFA