DOI: 10.5061/dryad.z612jm6n0
title: Data from: Combining past and contemporary species occurrences with ordinal species distribution modeling to investigate responses to climate change

description: Many organisms leave evidence of their former occurrence, such as scat,
 abandoned burrows, middens, ancient eDNA, or fossils, which indicate areas
 from which a species has since disappeared. However, combining this
 evidence with present-day occurrences within a single modeling framework
 remains challenging. Traditional binary species distribution modeling
 reduces occurrence to two temporally vague states (present/absent), so
 thus cannot leverage the information inherent in temporal sequences of
 evidence of past occurrence. In contrast, ordinal modeling can use the
 natural time-varying order of states (e.g., never occupied vs. occupied in
 the past vs. currently occupied) to provide greater insights into range
 shifts. We demonstrate the power of ordinal modeling for identifying the
 major influences of biogeographic and climatic, variables on current and
 past occupancy of the American pika (Ochotona princeps), a
 climate-sensitive mammal. Sampling over 5 years across the species’
 southernmost, warm-edge range limit, we tested the effects of these
 variables at 570 habitat patches where occurrence was classified either as
 binary or ordinal. The two analyses produced different top models and
 predictors – ordinal modeling highlighted chronic cold as the
 most-important predictor of occurrence, whereas binary modeling indicated
 primacy of average summer-long temperatures. Colder wintertime
 temperatures were associated in ordinal models with higher likelihood of
 occurrence, which we hypothesize reflect longer retention of insulative
 and meltwater-provisioning snowpacks. Our binary results mirrored those of
 several other past pika investigations employing binary analysis. Because
 both ordinal- and binary-analysis top models included climatic and
 biogeographic factors, results constitute important considerations for
 climate-adaptation planning. Cross-time evidences of species occurrences
 are common, yet remain underutilized for assessing responses to climate
 change. Compared to multi-state occupancy modeling, which presumes all
 states occur in the same time period, ordinal models enable use of
 historical evidence of species’ occurrence to identify factors driving
 species’ distributions more finely across time.
description: Our dataset includes <em>n</em> = 570 habitat
 patches surveyed by &gt;1 of the authors and/or
 field technicians and determined to be currently occupied, previously
 occupied, or having no evidence of occupancy by the study organism, the
 American pika (<em>Ochotona princeps</em>). The dataset spans
 5 years, from 2016 to 2020 inclusive. Latitude and longitude were recorded
 using hand-held GPS devices accurate to 2-7 m (WAAS-enabled), and “survey
 Year” is the year in which the site was most recently surveyed. “Ordinal
 status” is parameterized as 0 (no evidence of pika occurrence), 1 (old
 evidence of pika occurrence), or 2 (evidence of current pika occurrence).
 Binary status is given as either 0 (not currently pika-occupied) or 1
 (current pika occurrence detected). Our study domain is divided into
 northeast, southeast, northwest, and southwest “sub-regions” of mountain
 ranges that are separated from each other by ~45-100 km of lower-elevation
 topography. We recorded “elevation” (in meters) using handheld GPS units.
 The number of home ranges (“numHomeRanges”) is given for each site and was
 estimated <em>in situ</em> by observers, aided by laser
 rangefinders. If more than one value was given for the number of home
 ranges by different observers, then we used the mean value. The mean
 distance to the closest four patches is given in meters (m) and was
 calculated as the mean geographic distance to each of four nearest
 patches, which we used to determine patch-level isolation.
description: # Data from: Combining past and contemporary species occurrences with
 ordinal species distribution modeling to investigate responses to climate
 change
 [https://doi.org/10.5061/dryad.z612jm6n0](https://doi.org/10.5061/dryad.z612jm6n0) ## Description of the data and file structure Our dataset includes *n* = 570 habitat patches surveyed by &gt;1 of the authors and/or field technicians. We identified talus or broken-rock habitat patches to survey using high-resolution imagery in CalTopo.com. In the field patches were determined to be currently occupied, previously occupied, or having no evidence of occupancy by the study organism, the American pika (*Ochotona princeps*). The dataset spans 5 years, from 2016 to 2020 inclusive. We utilized this data to assess how analyzing binary (currently occupied vs unoccupied) and ordinal (occupied vs historically occupied vs no evidence) may indicate different factors as being important to describe species distributions, over different timescales. We analyzed the importance of climate variables downloaded from PRISM AN81d data, 2006-2021 (Daly et al. 2002).  ### Files and variables #### File: Pika\_Patch-level\_Data\_Ordinal\_Modeling\_.csv **Description:** Ordinal occupancy status of surveyed habitat patches.  ##### Variables * siteName: The name of the patch, designated by the authors and technicians  - longitude: The geographic longitude recorded by handheld GPS units (rounded to two decimals) - latitude: The geographic latitude recorded by handheld GPS units (rounded to two decimals) - surveyYear: The year of the most recent survey for each patch - lastSurveyDate: The date of the most recent survey for each patch - ordinalStatus: Parameterized as 0 (no evidence of pika occurrence), 1 (old evidence of pika occurrence) or 2 (evidence of current pika occurrence) - binaryStatus: Given as either 0 (not currently pika-occupied) or 1 (current pika occurrence detected) - sub-region: Indicates northeast, southeast, northwest, and southwest “sub-regions” of mountain ranges that are separated from each other by ~45-100 km of lower-elevation topography in our study region.  - elevation_m: Elevation, given in meters, was recorded by handheld GPS units - numHomeRanges: The number of 20 meter diameter potential pika home ranges estimated *in situ* by observers, aided by laser rangefinders.  - meanDistToClosest4Patches_m: the mean geographic distance to each of four nearest patches ## Code/software Analyzed with R software. To analyze occupancy, we used ordinal models along continuous climate variables and a logistic error distribution (Package OrdinalNet version 2.12 in R, Wurm et al. 2021). We also used binary models that assumed a binomial error distribution (function 'glm' within 'lme4', Bates et al. 2015).  ## Access information Other publicly accessible locations of the data: * NA Data was derived from the following sources: * NA