For some time now, and for several reasons, i have engaged my own thinking and the thinking of others about how the study of "real-time" social [here meaning inter-individual interactions among conspecifics] behavior, social interactions, and group "behavior" might be facilitated by using remote sensing. Figure and Table below summarize my information and thinking up to February 2014.
I am interested in advances that are not summarized in Table and that may have been made since Feb. '14 in remote sensing or other mechanistic approaches that would facilitate "mapping" of individual & interindividual responses, within & between groups, onto critical resources. Ideally, identification of individuals & groups could be "mapped" onto resources &, beyond ideal!, remote sensing could tell us something about the dispersion [distribution & abundance], quality, and type of resources, particularly, plants. Other questions related to, say, access to mates, might also be envisioned. I understand that such remote sensing as i describe is a future project; however, i'm simply wondering whether advances have been made in that direction [beyond the approaches described in Table below]?
I acknowledge the input and expertise of Dr. Daniel Mennill [Univ of Windsor], who informed me of Encounternet, has answered numerous questions from me, and whose work, combined with that of others, has stimulated much of my thinking on these topics.
I. MAP: A map, including this “nested-vision” 3D schema,
constitutes a systematic effort and tool to model (conceptualize, understand)
an actual or potentially real problem, event, condition, situation, response,
or, other, phenomenon, in the physical or perceived universe. The depicted model represents a generic
“nested-vision” 3D map, amenable to rotation and a variety of other
alterations. This structure might, for
example, characterize a group (black square) from which individuals leave
(lines) to forage in two habitats or patches (grey squares) of a home range (A,
B), subsequently, returning to their group (lines). Circles in each “patch” might represent
different diameters at breast height (DBH) of trees, colored differentially for
species recognition. The grey squares
might, alternatively, represent 2 sub-groups of a group (black square), with
circles identifying individuals by age or dominance rank, or other features
(e.g., degree of relatedness to a matriline or, in a polygynous group, a
resident male). Or, the black square
might represent a source of water in an arid zone, with lines representing
proportional (circle size) frequency of movement of different bands of different
classes of taxa (within- or between-taxa), A and B.
Continuing to visualize the black box as a source of water, lines might
represent individuals of different groups, A and B, with circles
representing, for instance, age-size-class membership, frequency of transit,
sub-group membership, or related variables.
Following Ware et al.
(1997; Parker et al. 1998, Ware and Mitchell 2008), 3D graphs and maps make
three assumptions: (1) that 3D is preferable to 2D visualization for large information
structures; (2) that “nested” graphs are required to represent “complex”
databases; and, (3) that maximum utility of these approaches includes “manual
and automatic layout of structures”.
Other types of visualization utilities can be accessed at Colin Ware’s
(University of New Hampshire) website: http://ccom.unh.edu/vislab/projects/networks,
including, “node-link” diagrams with capacities for thousands of nodes and
links, as well as, “interactive motion” structures whereby relevant information
can be highlighted with a cursor. In
addition, conventional methods of graphing or mapping information can be
expanded, such as the modified physical map presented by Jones (1995, Fig. 1, p
4). Visualized applications are models
constructed systematically to convert a researcher’s conceptualizations of
hypothesized and “real-world” systems into graphic and mapped displays of
imagined or actual information. However,
graphs, maps, and, related, utilities, do not substitute for mathematical
modeling. ©Clara B. Jones
II. TABLE: This
table identifies “mechanistic approaches” that may be employed to study groups
of social mammals based on a review of the literature, and communication with
researchers.
These technologies capture events of a species at one or more scale of
analysis, from individuals to groups, to populations as well as abiotic (e.g.,
soil gradients) and other biotic features (plants, conspecific groups, animal species
composition). Appariti are
systematically employed to convert a partial or complete array of real-world
problems (migration, group foraging, contest competition, mate choice,
cooperation, and the like) into analyzable data. Challenges are encountered since, in social
groups, one animal’s behaviors are a function of interactions with
conspecifics, usually, other group members.
In most cases, these apparati will be used in association with
traditional data-collection techniques (“focal” observations of animals,
hand-held instrumentation, fruit-fall traps, DBH measurements: see, for
example, Reich et al. 2004). As
Moorcroft (2012) pointed out, in addition to post-study (and real-time)
advances in analyses, including model-fitting, the major contributions of newer
technologies at present are increasing capacities to capture concurrent,
fine-scale data within- and between-populations. These utilities, also, permit assessments of
environmental “grains” at different levels of analysis (Moorcroft 2012), an
important capability for social biologists because events at one scale generally cannot
be employed to predict events at lower or higher scales, and, because current
technologies and near-generation mechanistic approaches permit a researcher to
estimate static and dynamic population parameters (e.g., generation time,
population growth). I thank D.J. Mennill, W.J.
Foley, S. Kawano, B. Nicolai, and N. Pettorelli for providing information via "personal communication" and remain grateful to Ted Fleming for assistance with the literature search. ©Clara B. Jones
TECHNOLOGY
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CURRENT UTILITIES
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REFERENCES
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FUTURE UTILITIES/NOTES
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Animal Patterns
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Radio-tracking
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Land-based
telemetry system for tracking spatial ecology of individuals and groups
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bats:
Almenar et al. (2013); primates: Joly & Zimmerman (2011); birds and bats
concurrently: Taylor et al. (2011); ungulates: Mueller et al. (2011)
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2 or
more animals can be studied concurrently (e.g., members of sub-groups);
operates on relatively small spatial scales; short-term temporal data; difficulties
associated with tracking in closed forest habitats when tracking on foot;
recent advances enhance power and applications (Moorcroft 2012); relatively
inexpensive compared with other tracking methods
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Radio-tracking
via airplane
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Allows
descriptions of landscapes relative to animal use when
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Bats:
Eby (1991)
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Problems
associated with length of battery life
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Resource-selection
analysis (RSA)
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Used
in combination with radio-telemetry, allowing descriptions of landscapes
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Moorcroft
(2012)
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Capable
of identifying spatial scales permitting “multi-layer” analyses; applicable
to tests of socio-ecological hypotheses recording within-population
dispersion of individuals and groups relative to resource dispersion;
adaptable to studies of leadership and rank relations via differential use of
space; “mechanistic home-range analysis” (Moorcroft 2012) applicable to
social biology
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Global-positioning
system (GPS)
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Satellite-based
tracking system using solar- or battery-powered transmitters
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Moorcroft
(2012); Holland and Wikelski (2009),
Richter and Cumming (2008), Epstein et al. (2009); Tsoar et al. (2011),
Markham and Altmann 2008; Tomkiewicz et al. (2010), Cagnacci et al. (2010)
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Widespread
scientific use relatively recent; permits deployment on animals smaller than
large terrestrial and marine mammals; can be used to monitor physiological
states; can track animal movements and use of space over large geographical
ranges; databases can be created and managed for behavioral, ecological, and
comparative studies (Tomkiewicz et al. 2010)
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©Encounternet
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Light-weight
tags “enable automated mapping of social networks” (including, position and
duration of interactions and signals
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Rutz
et al. (2012), Mennill et al. (2012a, b), Taylor et al. (2011)
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Data
received by a “grid of fixed receiver stations” yielding large, high-quality,
high-resolution datasets; so far tested using birds; can be used for
terrestrial and arboreal taxa (D.J. Mennill, personal communication)
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“Proximity
data-loggers”
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Similar
to and may be used in association with ©Encounternet technology for studying
interactions of group-living animals
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Ryder
et al. (2012), Mennill et al. (2012a, b), Maynard et al. (2012)
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Data
transferred to receiver “grids” capturing frequency of contacts permitting
construction of “weighted networks” characterizing “complex social dynamics
and calculation of statistics; captures changes in individual and
inter-individual responses, group structure, population processes, and
resource dispersion, including, phonologies; useful for tests of
sociobiological hypotheses (e.g., cooperation, sexual selection: Mennill et
al. 2012)
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Camera
traps
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Remote
instruments that take photos or video when a sensor is triggered
(mongabay.com)
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Diaz
et al. 2005, Harmsen et al. 2009, Norris et al. 2020
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Use of
robo-mammals lags behind studies of robo-mollusks or robo-amphibians; can
adapt technology for estimates of animal interactions, such as, local
predator-prey abundance and temporal distributions; can utilize for
preliminary estimates of species distributions, including, relative
occurrences of social and non-social taxa
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Robotics
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“Automated
machines” capable of simulating biological events
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fish:
Ioannou et al. (2012: coordinated group movement); Handegard et al.(2012:
group hunting and schooling prey); Kopman et al. (2013)
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Use of
robo-mammals lags behind studies of robo-mollusks, robo-amphibians, or
robo-fish (“etho-robotics”); however, a
wide range of sociobiological questions is amenable to tests with
robotic techniques, including, patterns of group dispersion relative to
robots manipulated in various positions or configurations or simulated
predators or prey (see references for fish) or manipulations of pelage or
skin color and pattern relative to, for example, reproductive condition; in
certain ways, these techniques can be employed in association with
quantitative modeling (e.g., “agent-based” models)
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Resource
Patterns
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Normalized
Difference Vegetation Index (NDVI); Enhanced Vegetation Index (EVI); Moderate
Resolution Imaging Spectroradiometer (MODIS); Light Detection and Ranging
(LiDAR); Satellite-based remote sensing; Near Infra-red Spectroscopy (NIS);
Imaging spectroscopy
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Methods
employed to assess plant food dispersion, type, and quality
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Asner
and Levick (2012), Bradbury et al. (2005), Youngentob et al. (2011)////,
Saranwong et al. (2004), Saranwong et al. (2003), Nicolai et al. (2007),
Pettorelli et al. (2005), Duffy and Pettorelli (2012)////, Xiao et al. (2006)
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Research
and development needed to for applications to covariation of events between
plants (e.g., phenology, fruit type and ripeness) and mammal groups
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Molecular
genetics
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Collection
of tissue samples from animals at different locations, using data from
mitochondrial and/or nuclear genes (e.g., microsatellites) to determine
degree of genetic similarity between populations
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Fleming
(2010)
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Measures
of genetic similarities between seasonally-occupied habitats indicates
connectivity of migratory movements; utility for studying migrants relative
to particular resources in early stages of development; social biologists can
use these techniques alone or in combination with other mechanistic
approaches to assess genetic patterns within and between sub-groups (e.g.,
“fission-fusion” units) of the same species
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Visualization
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See
Map, above
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Various
approaches employed to visualize data/information, including, software
structures; these utilities may incorporate motion, 3D, “fish-bowl”,
“node-link”, and other information architectures
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See
Map, above
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These
mechanistic approaches require research and development for specific
applications to questions, models, results, configurations (e.g., networks),
and conceptualizations pertinent to Social Biology
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