DOI: 10.5061/dryad.cz8w9gj0f
title: Hierarchical multi-grain models improve descriptions of species’ environmental associations, distribution, and abundance

description: The characterization of species’ environmental niches and spatial
 distribution predictions based on them are now central to much of ecology
 and conservation, but implicitly requires decisions about the appropriate
 spatial scale (i.e. grain) of analysis. Ecological theory and empirical
 evidence suggest that range-resident species respond to their environment
 at two characteristic, hierarchical spatial grains: (i) response grain,
 the (relatively fine) grain at which an individual uses environmental
 resources, and (ii) occupancy grain, the (relatively coarse) grain
 equivalent to a typical home range. We use a multi-grain (MG) occupancy
 model, aided by fine-grain remotely sensed imagery, to simultaneously
 estimate species-environment associations at both grains, conduct grain
 optimization to measure response grain, and apply this analysis framework
 to an example species: a medium-sized bird (Tockus deckeni) in a
 heterogeneous East African landscape. Based on home range analysis of
 movement data, we calculate an occupancy grain of 1km for T. deckeni.
 Using a grain optimization procedure across 32 grains from 10m to 500m, we
 identify 60m as the most strongly supported response grain for a suite of
 environmental variables, slightly coarser than opportunistic behavioral
 observations would have suggested. Validation confirms that the accuracy
 of the optimized MG occupancy model substantially exceeds that of
 equivalent single-grain (SG) occupancy models. We further use a simulation
 approach to assess the potential impacts of accounting for the multi-scale
 structure of species’ environmental requirements on estimates of
 population size. We find that the more strongly supported MG approach
 consistently predicts a minimum population sizes in the study landscape
 that is much lower than that provided by the SG model. This suggests that
 SG approaches commonly used in conservation applications could lead to
 overly optimistic abundance and population estimates and that the MG
 approach may be more appropriate for supporting species conservation
 goals. More generally, we conclude that multi-grain approaches of the sort
 presented, and increasingly enabled by growing high-resolution remotely
 sensed data, hold great promise for offering a more mechanistic framework
 for assessing the appropriate grain(s) for population monitoring and
 management and enable more reliable estimates of abundances and species’
 distributions.
description: During April 2014 – December 2014, we conducted surveys for
 <i>Tockus deckeni</i> (von Der Decken's hornbills) within
 75, 1km x 1km sampling sites ("1km grid cells") at Mpala
 Research Centre ("MRC"; Laikipia, Kenya). Each 1km grid cell was
 surveyed approximately bimonthly during morning (06:30-10:30) and
 afternoon (15:30-18:30) periods. Each indvidual survey searched at least
 10% of the 1km grid cell's area and lasted at least 30
 minutes. Because we searched an entire 1km grid cell across multiple
 surveys, individual survey routes were not identical. Any adult
 <i>T. deckeni</i> encountered during a survey was followed for
 up to 15 consecutive locations. We recorded each bird location using
 distance-and-bearing methods: distance measured by rangefinder
 (uncertainty ± 1m), and bearing measured by compass (uncertainty ±
 1°).  We then converted these survey data into series
 of detections and non-detections in a grid of 10m x 10m cells laid over
 MRC. To determine the area searched effectively during an individual
 survey, we calculated the median distance at which <i>T.
 deckeni</i> were first detected during the study period
 (44m) and buffered each survey route by this distance. All observations
 outside this searched area (i.e., more than 44m from the survey route)
 were discarded. Since accounting for detection probability requires
 repeated visits to the same location, we also discarded any 10m grid cells
 surveyed less than three times during the study period. Finally, to avoid
 false negatives in non-detection data, we established a conservative
 minimum distance (500m) between detections and non-detections in searched
 areas where <i>T. deckeni</i> were detected. For the minimum
 survey duration (30 minutes), we estimated the median distance traveled by
 <i>T. deckeni </i>(287m). For a survey in which <i>T.
 deckeni</i> was detected, only 10m grid cells that were both within
 the searched area and at least 500m from any detection were considered
 non-detections. For a survey in which no <i>T.
 deckeni </i>were detected, all 10m grid cells within the searched
 area were considered non-detections.
description: The data are presented as a matrix in which every row represents
 an indiviudal 10m x 10m grid cell in the study area.
 Columns 2:21 contain detection event for an individual survey:
 either 1 (detection of adult <em>T. deckeni</em>), 0
 (non-detection of adult <em>T. deckeni</em>), or NA (no survey
 performed). Columns 22:41 contain dates for each
 detection event. Each 10m cell has a varying number (3-20) survey
 occasions and an equivalent number of detection events. (The value NA was
 used to fill empty survey events and dates.) The
 accompanying RData file "grids_1km_10m" provides the spatial
 coordinates for 1km x 1km and 10x 10m grids over the study area.