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What’s Next? Frontiers in Distributional Ecology Town Peterson University of Kansas

Frontiers in Distributional Ecology

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Page 1: Frontiers in Distributional Ecology

What’s Next? Frontiers in Distributional Ecology

Town PetersonUniversity of Kansas

Page 2: Frontiers in Distributional Ecology

Current Status of ENM

• An established conceptual framework for distributional ecology

• A growing empirical literature demonstrating its utility in diverse challenges, both basic and applied

• Hundreds of papers published yearly using ENM techniques

• And yet not without limitations…

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LIMITS TO CORRELATIONAL ENM: E.G., LOOK AT PHYSIOLOGY

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Study Design

• Spanish Moss, an epiphytic bromeliad• Detailed physiological studies by Craig Martin in

the 1970s and 1980s• Related to a climate data set that provides basic

climate information for the entire Earth every 6 hours for the past several decades!!!

• Detailed work scaling from the micro (physiology) to the macro (climate and geography)

• Goal: To test whether Spanish Moss distribution can be anticipated by optimal conditions for its physiology

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Maxent Models

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Outcome …

• Correlational ENMs are a simplication, actually in several dimensions …• Physiological response shapes -

SIMPLIFY• Physiology and tolerance of non-

optimal conditions – TIME-SPECIFIC• The BAM is an oversimplification – ADD

DISPERSAL

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SIMPLIFY

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Maxent Model Responses

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Current Methods• Popular methods fit highly complex objects to

estimate niches … but which niche?• Complex objects are more likely to correspond

to the existing niche, rather than the fundamental

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Dove Project Results …

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Simplify:

• Complex response forms created by many current algorithms do not fit well with our understanding of the fundamental niche

• Simple, convex response forms may be much more appropriate as approximations to the fundamental niche

• This thinking will most likely require algorithms that can incorporate incomplete data, as well as uncertainty in inference, but fit simple response forms

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TIME-SPECIFIC

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Environmental Data in ENM

• Climate data are the canonical environmental data type in ENM applications

• Climate data are horribly averaged and smoothed, both in space and in time

• Much of the interesting detail is lost … as well as much of the predictive power

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Time-specific

• Except for where data or goal considerations prohibit …

• Remote-sensing data should be used• But in real time …• No more broad climate averages

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ADD DISPERSAL

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BAM and Dispersal

A

M

B A

M

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SUMMARY

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ENM Innovations• Seek at all times a detailed and appropriate

conceptual framework within which to develop modeling approaches

• If it smells like a set-in-stone, static “recipe,” it probably is a bad idea and is giving sub-standard results

• Default parameters are a bad idea• One does not have to stay within accepted,

“canonical” methods, if one is guided by appropriate thinking

• But … one key ingredient is missing … DATA

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India

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India

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The dragon is a great lover of art, especially gold, and silver work. He loves to hoard

jewels and treasure, amassing vast amounts of valuable antique metal work. Although they

haven't any real use for money and jewels, they collect heaps of gold and gems. He is

very jealous of his belongings and guards the treasure he has built up over the years in large storerooms. He keeps detailed inventories of all his possessions so that he can be alerted immediately if a single object goes missing.

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Resources

• Biodiversity Informatics journal - https://journals.ku.edu/index.php/jbi

• Biodiversity Informatics training resources - http://biodiversity-informatics-training.org/

• Global online biodiversity informatics seminar series - https://plus.google.com/communities/111802729072058850441

• News and communications - https://www.facebook.com/groups/BiodiversityInformatics/

• Partial ROC tool - http://shiny.conabio.gob.mx:3838/nichetoolb2/