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Predicting the distribution of a rare chipmunk (Neotamias quadrivittatus oscuraensis): comparing MaxEnt and occupancy models

Perkins-Taylor, Ian E, Frey, Jennifer K·2020·Journal of Mammalogy
DOI 10.1093/jmammal/gyaa057PMC PMC7528646
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Abstract

Species distribution models (SDMs) use presence records to determine the relationship between species occurrence and various environmental variables to create predictive maps describing the species’ distribution. The Oscura Mountains Colorado chipmunk (Neotamias quadrivittatus oscuraensis) occurs in central New Mexico and is of conservation concern due to its relict distribution and threats to habitat. We previously created an occupancy model for this taxon, but were concerned that the model may not have adequately captured the ecological factors influencing the chipmunk’s distribution because of the data hungry nature of occupancy modeling. MaxEnt is another SDM method that is particularly effective at testing large numbers of variables and handling small sample sizes. Our goal was to create a MaxEnt model for the Oscura Mountains Colorado chipmunk and to compare it with our previous occupancy model for this taxon, either to strengthen our original assessment of the relevant ecological factors or identify additional factors that were not captured by our occupancy model. We created MaxEnt models using occurrence records from baited camera traps and opportunistic surveys. We adjusted model complexity using a novel method for tuning both the regularization multiplier and feature class parameters while also performing variable selection. We compared the distribution maps and variables selected by MaxEnt to the results of our occupancy model for this taxon. The MaxEnt and occupancy models selected similar environmental variables and the overall spatial pattern of occurrence was similar for each model. Likelihood of occurrence was positively related to elevation, piñon woodland vegetation type, and topographic variables associated with escarpments. The overall similarities between the MaxEnt and occupancy models increased our confidence of the ecological factors influencing the distribution of the chipmunk. We conclude that MaxEnt offers advantages for predicting the distribution of rare species, which can help inform conservation actions.

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Fig. 1. (A) Map of occurrence records used for MaxEnt Model 1 for Neotamias quadrivittatus oscuraensis in the Oscura Mountains, New Mexico. All points were taken from camera surveys conducted in an occupancy modeling framework from 2015 to 2016. (B) Map of occurrence records used for MaxEnt Model 2. Points in gray were taken from camera surveys conducted in an occupancy modeling framework from 2015 to 2016. Points in black were taken from opportunistic sightings while traveling to survey sites, or from previous, nonsystematic surveys by White Sands Missile Range from 2004 to 2009. All points were rarefied at a 200-m scale. Black lines represent roads.
Fig. 1. (A) Map of occurrence records used for MaxEnt Model 1 for Neotamias quadrivittatus oscuraensis in the Oscura Mountains, New Mexico. All points were taken from camera surveys conducted in an occupancy modeling framework from 2015 to 2016. (B) Map of occurrence records used for MaxEnt Model 2. Points in gray were taken from camera surveys conducted in an occupancy modeling framework from 2015 to 2016. Points in black were taken from opportunistic sightings while traveling to survey sites, or from previous, nonsystematic surveys by White Sands Missile Range from 2004 to 2009. All points were rarefied at a 200-m scale. Black lines represent roads.CC BY-NC · PubMed Central
Fig. 2. Diagram depicting our four-step model selection process for the MaxEnt models. This process adjusted model complexity by tuning the regularization parameter (β) and feature classes. Variable selection was done iteratively based on variable contribution to the model instead of a priori. AICc = Akaike’s Information Criterion with small sample size correction.
Fig. 2. Diagram depicting our four-step model selection process for the MaxEnt models. This process adjusted model complexity by tuning the regularization parameter (β) and feature classes. Variable selection was done iteratively based on variable contribution to the model instead of a priori. AICc = Akaike’s Information Criterion with small sample size correction.CC BY-NC · PubMed Central
Fig. 3. Response curves for variables included in (A) MaxEnt Model 1 and (B) MaxEnt Model 2 for Neotamias quadrivittatus oscuraensis in the Oscura Mountains, New Mexico. Relative likelihood of occurrence is predicted for the full range of values of each variable at the background points while holding all other variables at their median values. Vegetation types are: 0—other, unsuitable vegetation types such as roads and nonmontane categories, 1—montane scrub, 2—interior chaparral, 3—piñon woodland, 4—foothills-montane temperate grasslands, 5—juniper woodland. TPI-10 = topographic position index calculated using a 10-cell moving window. VRM-3 = vector ruggedness measure calculated using a 3-cell moving window.
Fig. 3. Response curves for variables included in (A) MaxEnt Model 1 and (B) MaxEnt Model 2 for Neotamias quadrivittatus oscuraensis in the Oscura Mountains, New Mexico. Relative likelihood of occurrence is predicted for the full range of values of each variable at the background points while holding all other variables at their median values. Vegetation types are: 0—other, unsuitable vegetation types such as roads and nonmontane categories, 1—montane scrub, 2—interior chaparral, 3—piñon woodland, 4—foothills-montane temperate grasslands, 5—juniper woodland. TPI-10 = topographic position index calculated using a 10-cell moving window. VRM-3 = vector ruggedness measure calculated using a 3-cell moving window.CC BY-NC · PubMed Central
Fig. 4. Relative likelihood of occurrence on the logistic scale for Neotamias quadrivittatus oscuraensis in the Oscura Mountains, New Mexico predicted by (A) MaxEnt Model 1 and (B) MaxEnt Model 2, and (C) occupancy probability predicted by the final occupancy model (Perkins-Taylor and Frey 2018). Areas of high likelihood are represented by warmer colors, and areas of low likelihood are represented by cooler colors. The background extent for each MaxEnt model is outlined in black.
Fig. 4. Relative likelihood of occurrence on the logistic scale for Neotamias quadrivittatus oscuraensis in the Oscura Mountains, New Mexico predicted by (A) MaxEnt Model 1 and (B) MaxEnt Model 2, and (C) occupancy probability predicted by the final occupancy model (Perkins-Taylor and Frey 2018). Areas of high likelihood are represented by warmer colors, and areas of low likelihood are represented by cooler colors. The background extent for each MaxEnt model is outlined in black.CC BY-NC · PubMed Central

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Metadata

Authors
Perkins-Taylor, I. E., & Frey, J. K.
Affiliations
Department of Fish, Wildlife and Conservation Ecology, New Mexico State University, Las Cruces, NM, USA
ORCID
Venue
Journal of Mammalogy
Publisher
Oxford University Press (OUP)
Published
2020-06-16
Volume
101
Issue
4
Pages
1035-1048
ISSN
  • 0022-2372
  • 1545-1542
DOI
10.1093/jmammal/gyaa057
PMC
PMC7528646
Cited by
37
References
1
Language
en
Licence
creativecommons.org/licenses/by-nc/4.0/ (vor)
Funders
Conservation Branch, Garrison Environmental Division, White Sands Missile Range

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