Spatial Dynamics and Ecology of Large Ungulate Populations in Tropical Forests of India

217 Pages·2021·5.415 MB·English

by  N. Samba Kumar, K. Ullas Karanth, James D. Nichols, Srinivas Vaidyanathan, Beth Gardner, Jagdish Krishnaswamy

N. Samba Kumar 
K. Ullas Karanth · James D. Nichols 
Srinivas Vaidyanathan · Beth Gardner 
Jagdish Krishnaswamy
Spatial Dynamics
and Ecology
of Large Ungulate
Populations in
Tropical Forests
of India
Spatial Dynamics and Ecology of Large
Ungulate Populations in Tropical Forests
of India
N. Samba Kumar • K. Ullas Karanth •
James D. Nichols • Srinivas Vaidyanathan •
Beth Gardner • Jagdish Krishnaswamy
Spatial Dynamics and
Ecology of Large Ungulate
Populations in Tropical
Forests of India
N. Samba Kumar K. Ullas Karanth
Centre for Wildlife Studies Centre for Wildlife Studies
Bengaluru, Karnataka, India Bengaluru, Karnataka, India
James D. Nichols Srinivas Vaidyanathan
University of Florida Foundation for Ecological Research,
Gainesville, FL, USA Advocacy and Learning
Morattandi, Tamil Nadu, India
Beth Gardner
School of Environmental and Forest Sciences Jagdish Krishnaswamy
University of Washington Centre for Biodiversity and Conservation
Seattle, WA, USA Ashoka Trust for Research in Ecology and
the Environment
Bengaluru, Karnataka, India
ISBN 978-981-15-6933-3 ISBN 978-981-15-6934-0 (eBook)
https://doi.org/10.1007/978-981-15-6934-0
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It is impossible for us to convey in the dry
pages of text here, the sheer scale of all the
hard field work, high motivation, intense
focus and passion for wildlife, brought to
bear on the task of massive data collection
by our army of volunteers, as well as
dedicated staff and field assistants at the
Centre for Wildlife Studies during
1989–2017. They mastered the art of bravely
sneaking past wild elephants, dodging angry
sloth bears, and even worse, literally being
eaten alive by tiny jungle ticks that burrowed
into their skins. These “transect surveyors”
stayed in dingy field camps, ate monotonous
meals and withstood the harsh work regimen
and disciplinary code imposed on them. The
high quality and massive quantity of the data
we present here are testimonies to their
commitment.
We humbly dedicate this monograph to all
these citizen scientists. It has been our
privilege to work with them for over three
decades.
Foreword
I met Samba Kumar in the summer of 2007 when he spent a sabbatical at USGS
Wildlife Research Center at Patuxent to work on analyses related to his PhD effort,
the effort that ultimately resulted in this monograph on ungulate ecology and con-
servation. At the time, one of the coauthors, Beth Gardner, was a post-doc in our
group. To my mind, this time was Patuxent’s heyday not only in terms of staffing
and scientific activity but also its vibrant and dynamic atmosphere. In those days,
some of the hierarchical modeling ideas applied in this monograph were just being
developed by members of our group, our collaborators, and colleagues. At least
some of the ideas that came out of Patuxent during this period were inspired by col-
laboration with Ullas Karanth who approached me through Jim Nichols who has
been a long- term mentor to both of us. This collaboration between USGS Patuxent
Lab and Centre for Wildlife Studies in India contributed to innovations such as the
application of spatial capture–recapture models to camera trap data on tigers, occu-
pancy models to sign survey data on mammals as well as the spatial distance sam-
pling models to line transect data on ungulates, presented in this monograph. My
engagement with spatial distance sampling work done by Samba Kumar was a part
of this broader collaboration, which continues to this day (Karanth and Nichols 2017).
This monograph by Samba and his coauthors is really two monographs rolled-up
into one. On one hand, it is a monograph on spatial ecology and conservation of
large ungulates in an important ecosystem, the Nagarahole–Bandipur landscape of
southern India. This critical ecosystem includes a suite of ungulate species and also
Indian tigers which several of the authors have been studying for most or all of their
careers. Second, it is a monograph on applied hierarchical modeling.
The important ecological and conservation context of this work is established in
Chap. 1. As the authors emphasize, there is a sparsity of reliable information on
ungulate abundance and distribution in south Asia, and this study addresses that
information gap. The authors emphasize a number of important ecological and
methodological challenges which structure the development and implementation of
their study, statistical models, and analysis of the data. This sets the stage for the
second viewpoint that this monograph is also a unique and very practical mono-
graph on applied hierarchical modeling, and specifically what I would call “hierar-
chical distance sampling” (HDS). Distance sampling is one of the most important
methodologies in wildlife monitoring, and the type of data analyzed by Samba and
coauthors are routinely collected in many different systems throughout the world.
vii
viii Foreword
Hierarchical distance sampling concerns the situation where transects or points are
surveyed using a distance sampling protocol, but the underlying abundance or den-
sity of each sample unit varies spatially according to some ecological process. In
HDS models, understanding the structure and dynamics of how density varies
among units is a fundamental objective. In Chap. 2, the core methodological chapter
of the monograph, the authors provide a comprehensive specification of the HDS
model, allowing for group size to affect detection probability, covariates that affect
density and detection, spatial variation in the form of a flexible model of spatial
dependence, and Bayesian variable selection. In Chap. 3, the HDS model is applied
to the suite of five ungulate species using a number of covariates which represent
habitat, physical environment, and management effectiveness. Together, these
approaches allow geographic predictions of density for these important ungulates
that occupy various niches in this community.
One novel methodological aspect of the monograph is that the model is a novel
integration of density surface models (DSM; Miller et al. 2013) with a flexible
model of spatial dependence (a conditional autoregression or CAR model). The
authors provide a “fully Bayesian” implementation of the DSM framework in which
both observation and process models are analyzed jointly, in contrast to the original
two-stage analysis of Hedley and Buckland (1999). This monograph is the most
comprehensive application of fully Bayesian DSMs in the literature, and it is one
element which will be widely appreciated. In Chap. 3, the authors give a compre-
hensive analysis of an extraordinary monitoring data set representing more than
1400 km of survey transects, and in the appendix they provide a complete R script
to show how the models are analyzed in NIMBLE which greatly increases the prac-
tical utility of this work.
The management context of this study is detailed excellently in Chaps. 4 and 5.
Chapter 4 is about quantifying the spatial distribution of proximate threats to ungu-
lates, the development of a “composite human disturbance index” or CDI, and eval-
uating management strategies to increase ungulate abundance. The authors
formulate various management strategies involving elements of habitat manage-
ment and law enforcement and then project the impact of those actions on ungulate
density under new habitat scenarios induced by six alternative management regimes.
The authors look at existing strategies and budgets to compute a “return on manage-
ment efforts.” Anti-poaching measures provide the most “bang for buck,” which
suggest changes to the existing management regime. The final chapter summarizes
the big picture of ungulate conservation in this system and, importantly, outlines a
vision for the future derived from key conservation, monitoring, models, and man-
agement concepts developed as part of this work. The authors envision a formal
adaptive management framework for the management of this landscape integrating
decision-making with the biological (density/habitat models) and conservation con-
text (increasing ungulate populations). This is perhaps an aspirational vision but
very much consistent with the direction of the conservation field as a whole includ-
ing the work of some of the monograph’s authors.
In summary, “Spatial dynamics and ecology of large ungulate populations in
tropical forests of India” represents a complete treatise on applied hierarchical
Foreword ix
modeling motivated by an important conservation problem, the development of a
rigorous methodological framework along with the provision of an accessible
implementation, and then the use of the model to evaluate explicit management
actions. As an important ecological case study, or as a methodological exposition,
this monograph is sure to be widely useful and informative.
I hope wildlife managers, researchers, and conservationists who are investing
much energy and resources on recovering all threatened ungulate species will avidly
consider recommendations provided here in their efforts.
USGS Patuxent Wildlife Research Center J. Andrew Royle
Laurel, MD, USA
March 27, 2020
References
Hedley SL, Buckland ST, Borchers DL (1999) Spatial modelling from line transect data. J Cetacean
Res Manage 1(3):255–264
Karanth KU, Nichols JD (eds) (2017) Methods for monitoring tiger and prey populations. Springer
Nature Singapore Pte. Ltd.
Miller DL, Burt ML, Rexstad EA, Thomas L (2013) Spatial models for distance sampling data:
recent developments and future directions. Methods Ecol Evol 4(11):1001–1010
Preface
Wild ungulates inhabiting tropical forests of the world are bearing the brunt of
increasing human impacts of the current Anthropocene epoch. Once ubiquitous and
abundant throughout their range, distributions and abundances of most species are
declining at unprecedented rates. In spite of the recognition of the critical role of
wild ungulates in maintaining the structure, stability and functioning of ecosystems
they live in, science-based efforts focused at ungulate conservation are scarce, par-
ticularly in the tropical regions where ungulates attain greatest diversity.
Before the second half of the twentieth century, conservation efforts directed at
populations of wild ungulates were constrained by the absence of rigorous methods
to evaluate ungulate-habitat relationships to prioritize effective conservation inter-
ventions. Subsequent development of better field survey methods and robust statisti-
cal inferential approaches have laid the foundations for improved monitoring.
However, the actual implementation of these improved methods by wildlife manag-
ers as well as researchers has consistently lagged in tropical forested regions, which
are inherently more difficult environments for conducting monitoring compared to
Savannas and other open habitats. The lack of adoption of modern monitoring, we
believe, is also—at least partially—due to the apathy of wildlife professionals in
Asia. Numerous reports of methods aimed at “censusing” ungulates (in other words
getting a total count), which is an impossibility in reality, are examples of this weak-
ness. This managerial apathy to rigorous science arises also because wildlife
researchers have generally not published large-scale case studies illustrating useful
application of such science.
Several factors that compound challenges faced in monitoring ungulate popula-
tions need urgent attention. Most tropical forest ungulate species occur at relatively
lower densities and consequently require field efforts to attain sufficient sample
sizes demanded by design-based inference methods to estimate population density
and abundance. The problems get further compounded because field counts of
ungulates simply do not address measurement errors, most important being imper-
fect detection, leading to the “noise” in the data often drowning the “signal” sought
by the surveyors.
This situation characterizes virtually all approaches to abundance estimation,
including the case of distance-sampling-based line transect survey, which traces its
history to the middle of the twentieth century, and is widely used for ungulate
xi
xii Preface
species that can be visually detected. Early exposure of some of the authors to these
issues set the stage for the evolution of this monograph.
In 1989, the first author (Kumar), then an amateur naturalist, participated as a
volunteer in the line transect survey of ungulate species conducted by the second
author (Karanth). The survey objective was to rigorously assess the prey availability
for tigers and their sympatric predators in a macro-ecological study forming the lat-
ter’s graduate work at University of Florida. Karanth became deeply interested in
line transect methodology, through his early interactions with field biologists Mel
Sunquist, John Seidensticker, R. Rudran, and John Eisenberg. However, most cru-
cially, he was also guided toward the right statistical approaches to the line transects
by David Anderson at Colorado State University (in 1987) and James Nichols (third
author) in 1989.
In 1994, Kumar decided on a career in wildlife science, and joined as lead
researcher in Karanth’s team at the Centre for Wildlife Studies, after quitting his
position as a project manager in India’s space agency. Soon their focus progressed
from implementing rigid field survey protocols (to ensure quality of data collected)
to survey design, analysis, and estimation methods rooted in modern sampling the-
ory applied under a likelihood-based inferential framework. The progress toward
modern line transect methodology was enriched and facilitated greatly in 2000 by
interactions with Stephen Buckland, David Borchers, Len Thomas, and Samantha
Strindberg at the University of St. Andrews, which had by then become a global
leader in this field.
The continuing mentorship by the versatile James Nichols who straddled both
the universes of likelihood theory and Bayesian inference, led Kumar and Karanth
to the exciting field of hierarchical modeling of wildlife populations under a
Bayesian paradigm pioneered by researchers at the US Geological Survey—William
Link, Andy Royle, and Robert Dorazio. What impressed us most was the ability of
their hierarchical models to elegantly deal with many thorny realities in the data
generated from field sampling, without distracting us from ecological parameters
that really mattered most. Along the way, other coauthors joined these efforts
strengthening the team of authors. The most exciting aspect of working in such a
team, consisting of passionate field biologists, quantitative ecologists, landscape
ecologists, and mathematical statisticians, has been the ability we gained collec-
tively to bridge the yawning gap between the rapidly unfolding methodological
advancements and their full-fledged implementation in macro-ecological studies
conducted in real world.
In this monograph, we address the application of statistically robust hierarchical
modeling approaches for understanding and conservation of tropical forest ungulate
populations. Our aim is to demonstrate model development that can address thorny
field sampling issues typically glossed over in many monitoring schemes, and, the
application of these models to mechanistically explain spatial abundance patterns of
five threatened ungulate species in a conservation priority landscape. Here, we
expose readers to different important components of a science-based animal popu-
lation monitoring program.