Data from: Physical calculations of resistance to water loss improve predictions of species range models

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Empirical vs theoretical skin resistancesThis file contains the raw data of skin resistances calculated from theoretical and empirical methods.fig1_empirical_vs_theoretical.xlsxEmpirical vs theoretical estimates of the boundary layer vs flow ratefig2_rb_flow_rate.xlsxEmpirical vs theoretical estimates of the boundary layer vs VPDfig3_vpd_rb.xlsxEstimates of activity and energy balance with and without the boundary layerfig5_energy_and_activity_with_rb.xlsxPositive or negative energy balance against VPDvpd_energy_balance.xlsxSkin resistance values across taxafig7_rs_comparative.xlsxPython code for biophysical modelThis code requires hourly temperature and VPD ascii files. It will compile each file individually and calculate estimates of activity and energy for a single night. This program should not be used for monthly or yearly estimates of activity and net energy balance unless a user has provided substantial modifications.biophysical_model.pyActivity without realistic resistances to water lossEstimates of activity (in seconds) without a boundary layer or realistic skin resistance to water loss over a single night in August.activity_norb_rs1.ascActivity with realistic resistancesEstimates of activity (seconds) with a boundary layer and average skin resistance (5 sec/cm) over a single night in August.activity_rb_rs5.ascEnergy without realistic resistancesNet energy balance (J) without a boundary layer or realistic skin resistance to water loss over a single night in August.energy_norb_rs1.ascEnergy with realistic resistances to water lossNet energy balance (J) with realistic resistance to water loss and skin resistance to water loss over a single night in August.energy_rb_rs5.asc,Species ranges are constrained by the physiological tolerances of organisms to climatic conditions. By incorporating physiological constraints, species distribution models can identify how biotic and abiotic factors constrain a species’ geographic range. Rates of water loss influence species distributions, but characterizing water loss for an individual requires complex calculations. Skin resistance to water loss (ri) is considered to be the most informative metric of water loss rates because it controls for experimental biases. However, calculating ri requires biophysical equations to solve for the resistance of the air that surrounds an organism, termed the boundary layer resistance (rb). Here, we compared theoretical and empirical methods for measuring skin resistance to water loss of a Plethodon salamander collected from nature. For the empirical methods, we measured rb of agar replicas at five body sizes, two temperatures, three vapor pressure deficits, and six flow rates using a flow through system. We also calculated rb using biophysical equations under the same experimental conditions. We then determined the ecological implications of incorporating skin and boundary layer resistance into a species range model that estimated potential activity time and energy balance throughout the geographic range of the study species. We found that empirical methods for calculating rb resulted in negative values of ri, whereas biophysical calculations produced meaningful values of ri. The species range model determined that ignoring realistic boundary layer and skin resistances reduced average estimates of energy balance by as much as 64% and potential activity time by 88% throughout the spatial extent of the model. We conclude that the use of agar replicas is an inadequate technique to characterize skin resistance to water loss, and incorporating boundary layer and skin resistances to water loss improve estimates of activity and energetics for mechanistic species distribution models. More importantly, our study suggests incorporating the physical processes underlying rates of water loss could improve estimates of habitat suitability for many animals.

Publication Date

1-1-2016

Publisher

DRYAD

DOI

10.5061/dryad.481g3

Language

en

Document Type

Data Set

Identifier

10.5061/dryad.481g3

Embargo Date

1-1-2016

Version

1

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