DOI: 10.5061/dryad.ksn02v7cz
title: Data from: Hyperbolic discounting underpins response curves of mammalian avoidance behaviour

description: As humans clear natural habitat they are brought into increased conflict
 with wild animals. Some conflict is direct (e.g., elevated exposure of
 people to predators), some indirect (e.g., abandoning suitable habitat
 because of human activity). The magnitude of avoidance is expected to
 track frequency of human activity, but the type of response is an open
 question. We postulated that animals do not respond passively to increased
 disturbance, nor does response follow a power law; instead, their ability
 to estimate magnitude leads to ‘discounting’ behaviour, as in classic
 time-to-reward economic models in which individuals discount larger value
 (or risk) in more distant time. We used a ten-year camera dataset from
 southern California to characterise response curves of seven mammal
 species. Bayesian regressions of two non-discounting models (exponential
 and inverse polynomial) and two discounting models (hyperbolic and
 harmonic) revealed that the latter better fit response curves. The Arps
 equation, from petroleum extraction modelling, was used to estimate a
 discount exponent, a taxon-specific ‘sensitivity’ to humans, yielding a
 general model across species. Although discounting can mean mammal
 activity recovers rapidly after disturbance, increased recreational
 pressure on reserves limits recovery potential, highlighting a need to
 strike a balance between animal conservation and human use.
description: Over fifty camera traps (Cuddeback Expert 3300 or HCO Scoutguard
 SG-565F) were placed in a complex of urban-adjacent parks with restricted
 public access in central and coastal Orange County, California [Patten
 & Burger 2018,Patten et al. 2019]. Cameras were placed
 inconspicuously (to minimize either spooking animals or drawing unwanted
 human attention) along trails (62%), at watering troughs (12%), or in
 vegetation away from either putative ‘concentration point’ (26%). Herein
 we analyse same-day data collected by 50 cameras June 2007–June 2016. We
 defined a ‘day’ as human activity &lt; 24 h prior to mammal detection
 [Patten et al. 2019]. 
description: # Hyperbolic discounting underpins response curves of mammalian avoidance
 behaviour DATA FILES - hyperbolic discounting 1. response curves Data used
 to fit exponential, inverse polynomial, hyberbolic, and harmonic curves.
 The response variable was the proportion of camera trap records for a
 given number of humans. As such, the 'disturbance' column is
 days with 0, 1, 2, 3 . . . hikers, vehicles, cyclists, or equestrians.
 Data columns are aggregate counts (2007-2016) for mammal species for given
 disturbance level, both across all mammals ('mammals') and for
 individual species with good sample size. The 'mammals' column
 includes a few other species whose incidence was too few to analyse
 separately. 2. wilderness access days Camera trap records before (-),
 during (0), and after (+) a 'wilderness access day', sporadic
 events during which otherwise strictly protected (i.e., 'off
 limits') areas of the park. 'Camera'is an abbreviation for
 the specific camera, but this field was not used in analyses; instead,
 data were aggregated across the several cameras. 3. time series Data
 (2007-2016) used for a time-series analysis. Time steps were quarters,
 which are coded as numeric month---3 = March, 6 = June, 9 = September, and
 12 = December, which accords with the 1st, 2nd, 3rd, and 4th quarter
 'steps' through the data from year to year. 'Camera
 days'is a measure of effort: it is the aggregate number of days
 cameras (up to 50) were active for a given quarter.
 'Disturbance', 'mammals', and individual species are
 defined as above.