DOI: 10.5061/dryad.hv8kk
title: Data from: The value of learning about natural history in biodiversity markets

description: Markets for biodiversity have generated much controversy because of the
 often unstated and untested assumptions included in transactions rules.
 Simple trading rules are favored to reduce transaction costs, but others
 have argued that this leads to markets that favor development and erode
 biodiversity. Here, I describe how embracing complexity and uncertainty
 within a tradable credit system for the Red-cockaded Woodpecker (Picoides
 borealis) creates opportunities to achieve financial and conservation
 goals simultaneously. Reversing the effects of habitat fragmentation is
 one of the main reasons for developing markets. I include uncertainty in
 habitat fragmentation effects by evaluating market transactions using five
 alternative dispersal models that were able to approximate observed
 patterns of occupancy and movement. Further, because dispersal habitat is
 often not included in market transactions, I contrast how changes in
 breeding versus dispersal habitat affect credit values. I use an
 individually-based, spatially-explicit population model for the
 Red-cockaded Woodpecker (Picoides borealis) to predict spatial- and
 temporal- influences of landscape change on species occurrence and genetic
 diversity. Results indicated that the probability of no net loss of
 abundance and genetic diversity responded differently to the transient
 dynamics in breeding and dispersal habitat. Trades that do not violate the
 abundance cap may simultaneously violate the cap for the erosion of
 genetic diversity. To highlight how economic incentives may help reduce
 uncertainty, I demonstrate tradeoffs between the value of tradable credits
 and the value of information needed to predict the influence of habitat
 trades on population viability. For the trade with the greatest
 uncertainty regarding the change in habitat fragmentation, I estimate that
 the value of using 13-years of data to reduce uncertainty in dispersal
 behaviors is $6.2 million. Future guidance for biodiversity markets should
 at least encourage the use of spatially- and temporally-explicit
 techniques that include population genetic estimates and the influence of
 uncertainty.
description: Input Data for banking
 scenariosFile folders are named
 in agreement with Table 1 abbreviations for landscape treatments.
 "lcsimgrid.csv" is the underlying landscape map.
 "ter(year).csv" reports the y, x coordinates for RCW territory
 centers assuming the origin in the upper left corner.
 "(year)mitiXY.csv" reports the cells that are restored to high
 habitat quality to create a bank. "(year)takeXY.csv" reports the
 cells to be changed to non-forested to simulate a take.
 "takeTers.csv" reports the territory centers for those
 territories lost due to a take.LEA_Input.tarResults from trading scenariosFile nomenclature: (service)_(model)_(landscape treatment).csv. Service = PBG, Hs, or Dst. Model = 0-5, for the 6 dispersal models. Landscape treatment = corresponds to table 1, plus Pre-Settlement for genetic services and Recovery for PBGs. Within the file, column 1 = year (starting at 0), column 2 = landscape state (status quo [baseline], bank, take). Remaining columns are all individual results from each MC iteration.LEA_Results.tar