Spatial tools for assessing the sustainability of subsistence hunting in tropical forests

Subsistence hunting provides a crucial food source for rural populations in tropical forests but is often practiced unsustainably. We use the empirical observation that subsistence hunters are central-place foragers to develop three 'biodemographic' hunting models of increasing complexity and realism for assessing the sustainability of hunting of an indicator species. In all our models, we calculate the spatial pattern of depletion of an indicator species (here, a large-bodied primate) across a landscape. Specifically, we show how to identify the area surrounding a human settlement that is expected to suffer local extinction. Our approach is an improvement over well-known sustainability indices of hunting, which are error-prone and do not provide clear links to policy prescriptions. Our first approach models the long-term effect of a single settlement and (1) can be parameterized with easily obtainable field data (such as settlement maps and knowledge of the major weapon used), (2) is simple enough to be used without requiring technical skill, and (3) reveals the asymptotic relationship between local human density and the level of game depletion. Our second model allows multiple settlements with overlapping hunting zones over large spatial scales. Our third model additionally allows temporal changes in human population size and distribution and source-sink dynamics in game populations. Using transect and hunting data from two Amazonian sites, we show that the models accurately predict the spatial distribution of primate depletion. To make these methods accessible, we provide software-based tools, including a toolbox for ArcGIS, to assist in managing and mapping the spatial extent of hunting. The proposed application of our models is to allow the quantitative assessment of settlement-stabilization approaches to managing hunting in Amazonia

Appendix A. Calculating the game encounter rate e from line-transect data.

Calculating the game encounter rate e from line-transect data

Supplement 1. Model implementation with Excel spreadsheet, Matlab, and Python script for ArcGIS.

<h2>File List</h2><blockquote> <a href="analytical_spreadsheet_solver.xls">analytical_spreadsheet_solver.xls</a>*<br> <a href="multisettlement_model_solver.m">multisettlement_model_solver.m</a><br> <a href="Hunting_Mapper.zip">Hunting_Mapper.zip</a> - contains: <blockquote> - <a href="Hunting Mapper.tbx">Hunting Mapper.tbx</a><br> - <a href="monkeyscriptSteadyState.py">monkeyscriptSteadyState.py</a><br> - <a href="monkeyscript.py">monkeyscript.py</a> </blockquote> </blockquote><h2>Description</h2><blockquote> <p>analytical_spreadsheet_solver.xls* implements the simplest single settlement model in Microsoft Excel. It is designed to be easy to use on a platform familiar to non-technical users.</p> <p>multisettlement_model_solver.m is commented Matlab Code to produce multisettlement maps, cumulative distribution functions, and calculate the global and local catch per unit effort.</p> <p>Hunting_Mapper.zip is a zip file containing ArcGIS toolbox extension and the required python scriptsWindows executable. The toolbox is called, Hunting Mapper.tbx, and it can call two scripts. One script, monkeyscriptSteadyState.py, implements the multisettlement steady state model, which makes it suitable for large spatial and temporal scales. The other script, monkeyscript.py, implements the numerical multisettlement model with diffusive source-sink dynamics. This model is computationally expensive, and it requires the user to input the annual population data for evey settlement. See <a href="appendix-E.htm">Appendix E</a> for further instructions.</p> <p>* <i>Please note</i>: ESA cannot guarantee the accessibility of Excel files into the future due to the proprietary nature of the Excel format.</p> </blockquote

Appendix F. How sustainability indices work and why we need better methods.

How sustainability indices work and why we need better methods

Appendix B. Equations for rmax(θ), rmin(θ), θmax, and θmin.

Equations for rmax(θ), rmin(θ), θmax, and θmin

Appendix E. Using the Python scripts in ArcGIS.

Using the Python scripts in ArcGIS