Appendix B. Additional demographic parameters used in the nonspatially explicit, age-structured, individual-based model Vortex.

Additional demographic parameters used in the nonspatially explicit, age-structured, individual-based model Vortex

Smartphone-Based Distributed Data Collection Enables Rapid Assessment of Shorebird Habitat Suitability

<div><p>Understanding and managing dynamic coastal landscapes for beach-dependent species requires biological and geological data across the range of relevant environments and habitats. It is difficult to acquire such information; data often have limited focus due to resource constraints, are collected by non-specialists, or lack observational uniformity. We developed an open-source smartphone application called iPlover that addresses these difficulties in collecting biogeomorphic information at piping plover (<i>Charadrius melodus</i>) nest sites on coastal beaches. This paper describes iPlover development and evaluates data quality and utility following two years of collection (<i>n</i> = 1799 data points over 1500 km of coast between Maine and North Carolina, USA). We found strong agreement between field user and expert assessments and high model skill when data were used for habitat suitability prediction. Methods used here to develop and deploy a distributed data collection system have broad applicability to interdisciplinary environmental monitoring and modeling.</p></div

Data collected in the iPlover application and source of input within the smartphone device.

<p>Data collected in the iPlover application and source of input within the smartphone device.</p

Barrier islands are a principal habitat type in the U.S. Atlantic coast piping plover breeding range.

<p>(A) Oblique aerial photograph of southwestern Ocracoke Island, North Carolina, showing open-ocean sandy beach (right), dunes, backbarrier bay, and various types of dune, shrub, forest, and marsh vegetation. (U.S. Geological Survey/photo by Karen L.M. Morgan.) (B) The piping plover (<i>C</i>. <i>melodus</i>), a federally listed beach-nesting shorebird. (U.S. Fish and Wildlife Service/photo by Gene Nieminen.)</p

Plot showing spatial difference between global navigation satellite system receiver (GNSS)- and smartphone-derived data points on coastal beaches.

<p>Black open circles show the distance between each GNSS observation and the corresponding iPlover observation (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0164979#pone.0164979.s001" target="_blank">S1 Table</a>). The black square is the average distance between the GNSS and the smartphone for the 44 data points, and the gray circle is the one-sigma range around that average distance.</p

Examples of data structure and program flow implemented by <i>MetaModel</i><i>Manager</i>.

<p>(A) Nested data representing a species in a metamodel. Global state variables (GSvar), population state variables (PSvar), and individual state variables (ISvar) are descriptors of the overall system, each population, and each individual, respectively. (B) Flow of control among component models. Curved arrows represent access to and modification of data. Block arrows represent control passed among models. (C) A two-species metamodel, with one modifier and one translator model acting on one species and two modifier models acting on the second species. Control alternates between the species, as illustrated by solid block arrows. Each system, modifier, and translator model has access to change any property of its populations and individuals as well as any shared global state variables.</p

Map showing the locations of iPlover mobile application data collection during summer 2014.

<p>Red dots indicate recorded observations (<i>n</i> = 574). (Basemap from GSHHG, <a href="https://www.ngdc.noaa.gov/mgg/shorelines/gshhs.html" target="_blank">https://www.ngdc.noaa.gov/mgg/shorelines/gshhs.html</a>.)</p

Metamodel that integrates demography, landscape change, dispersal, and disease status.

<p>A PVA program acts as the system model (solid outline) to simulate individual survival and reproduction based on individual and population state variables (shown in italics) passed from other models. Modifier models (dashed outlines) simulate habitat dynamics, individual movements, and individual transitions in disease status. A central facilitator program passes state variables between the system and modifier models at appropriate time steps. The ultimate results are measures of population dynamics and extinction risk for a species impacted by habitat change and disease.</p

Classification agreement for the habitat variables considered among four subject-matter experts and between experts and iPlover field users for 181 test points in the iPlover dataset (10% of the full dataset of 1799 data points).

<p>Classification agreement for the habitat variables considered among four subject-matter experts and between experts and iPlover field users for 181 test points in the iPlover dataset (10% of the full dataset of 1799 data points).</p

Skill of Bayesian networks used to predict piping plover habitat suitability when trained with data points (<i>n</i> = 181) classified separately by four subject-matter experts and by an iPlover user in the field.

<p>Models were used to predict nest presence/absence based on the habitat characteristics of 1799 iPlover points collected in 2014 and 2015. Skill was assessed through Cohen’s <i>kappa</i> (0 = poor performance, 1 = perfect performance).</p