Forest Insect Population Dynamics, Outbreaks, And Global Warming Effects E-Book

Alexander S. Isaev

• Herausgeber: John Wiley & Sons
• Kategorie: Wissenschaft und neue Technologien
• Sprache: Englisch

Seitenzahl: 455

Veröffentlichungsjahr: 2017

Beschreibung

This new approach to insect modeling discusses population dynamics' regularities, control theory, theory of transitions, and describes methods of population dynamics and outbreaks modeling for forest phyllophagous insects and their effects on global climate change.

Research in insect population dynamics is important for more reasons than just protecting forest communities. Insect populations are among the main ecological units included in the analysis of stability of ecological systems. Moreover, it is convenient to test new methods of analyzing population and community stability on the insect-related data, as by now ecologists and entomologists have accumulated large amounts of such data. In this book, the authors analyze population dynamics of quite a narrow group of insects – forest defoliators. It is hoped that the methods proposed herein for the analysis of population dynamics of these species may be useful and effective for analyzing population dynamics of other animal species and their effects and role in global warming.

What can insects tell us about our environment and our ever-changing climate? It is through studies like this one that these important answers can be obtained, along with data on the insects and their behaviors themselves. The authors present new theories on modeling and data accumulation, using cutting-edge processes never before published for such a wide audience. This volume presents the state-of-the-art in the science, and it is an essential piece of any entomologist's and forest engineer's library.

Leseprobe

Contents

Cover

Title page

Copyright page

Authors

Introduction

Chapter 1: Population Dynamics of Forest Insects: Outbreaks in Forest Ecosystems

1.1 Approaches to modeling population dynamics of forest insects

1.2 The role of insects in the forest ecosystem

1.3 The phenomenological theory of forest insect population dynamics: the principle of stability of flexible ecological systems

1.4 Classification of the factors of forest insect population dynamics

1.5 Delayed and direct regulation mechanisms

Chapter 2: Ways of Presenting Data on Forest Insect Population Dynamics

2.1 Representation of population dynamics data

2.2 Presenting the data on forest insect population dynamics through changes in density over time

2.3 Presenting the data on population dynamics as a phase portrait

2.4 The probability of the population leaving the stability zone and reaching an outbreak density: A model of a one-dimensional potential well

2.5 Presenting the data on forest insect population dynamics as a potential function

Chapter 3: The Effects of Weather Factors on Population Dynamics of Forest Defoliating Insects

3.1 The necessary and sufficient weather conditions for the development of outbreaks of defoliating insects in Siberia

3.2 Weather influence on the development of the pine looper

Bupalus piniarius

L. outbreaks

3.3 Siberian silk

moth Dendrolimus sibiricus

Tschetv. population dynamics as related to weather conditions

3.4 Synchronization of weather conditions on vast areas as a factor of the occurrence of pan-regional outbreaks

Chapter 4: Spatial and Temporal Coherence of Forest Insect Population Dynamics

4.1 Coherence and synchronicity of population dynamics

4.2 Spatiotemporal coherence of the population dynamics of defoliating insects in pine forests of Middle Siberia

4.3 Spatiotemporal coherence of population dynamics of defoliating insects in the Alps

4.4 Global coherence of pine looper population dynamics in Eurasia

4.5 Synchronization of the time series of gypsy moth population dynamics in the South Urals

Chapter 5: Interactions Between Phytophagous Insects and Their Natural Enemies and Population Dynamics of Phytophagous Insects During Outbreaks

5.1 Entomophagous organisms as a regulating factor in forest insect population dynamics

5.2 A “phytophagous – entomophagous insect” model

Chapter 6: Food Consumption by Forest Insects

6.1 Energy balance of food consumption by insects: an optimization model

6.2 A population-energy model of insect outbreaks

Chapter 7: AR- and ADL-models of Forest Insect Population Dynamics

7.1 An ADL-model (autoregressive distributed lag) of insect population dynamics

7.2 A model of population dynamics of the gypsy moth in the South Urals

7.3 Modeling population dynamics of the larch bud moth in the Alps

7.4 Simulation models of population dynamics of defoliating insects in the Krasnoturansk pine forest

7.5 Modeling and predicting population dynamics of the European oak leaf-roller

7.6 Gain margin of the AR-models of forest insect population dynamics

Chapter 8: Modeling of Population Dynamics and Outbreaks of Forest Insects as Phase Transitions

8.1 Models of phase transitions for describing critical events in complex systems

8.2 Population buildup and development of an outbreak of forest insects as a first-order phase transition

8.3 Possible mechanisms of the development of forest insect outbreaks

8.4 Colonization of the tree stands by forest insects as a second-order phase transition

8.5 Risks of elimination of the population from the community

Chapter 9: Forecasting Population Dynamics and Assessing the Risk of Damage to Tree Stands Caused by Outbreaks of Forest Defoliating Insects

9.1 Methods of forecasting forest insect population dynamics

9.2 Long-term forecast of population dynamics of defoliating insects

9.3 Assessment of the maximum risk of damage to tree stands caused by insects

9.4 Modeling and forecasting of eastern spruce budworm population dynamics

Chapter 10: Global Warming and Risks of Forest Insect Outbreaks

10.1 Climate change and forest insect outbreaks in the Siberian taiga

10.2 Stress testing of insect impact on forest ecosystems under different scenarios of climate changes in the Siberian taiga

10.3 Risks of outbreaks of forest insect species with the stable type of population dynamics

Conclusion

References

Index

End User License Agreement

Guide

Cover

Copyright

Contents

Begin Reading

List of Tables

Chapter 2

Table 2.1.

Classification of the types of population dynamics based on the type of the potential well

Chapter 3

Table 3.1.

Conditional probabilities of the occurrence of the pine looper outbreak, including and excluding the low-density years

Table 3.2.

Determination of the combinations of pre-outbreak years in which the May weather had the strongest effect on the development of the pine looper outbreak in the Minusinsk district (based on HTC)

Table 3.3.

Determination of the combinations of pre-outbreak years in which the

July weather

had the strongest effect on the development of the pine looper outbreak in the Minusinsk district (based on HTC)

Table 3.4.

The significance of the difference between the conditional and unconditional probabilities of the necessity of weather effects (based on HTC) in different combinations of pre-outbreak years in Altai

Table 3.5.

Necessary conditions of weather effects on the development of pine looper outbreaks in the ribbon pine forests of the Minusinsk Depression

Table 3.6.

Necessary conditions of weather effects on the development of pine looper outbreaks in the ribbon pine forests of the Altai Territory

Table 3.7.

Determination of the combinations of pre-outbreak years in which weather had the strongest effect on the development of the Siberian silk moth outbreak in the Boguchanskii district (based on HTC)

Table 3.8.

Determination of the combinations of pre-outbreak years in which weather had the strongest effect on the development of the Siberian silk moth outbreak in the Yeniseiskii district (based on HTC)

Table 3.9.

Necessary conditions of weather effects on the development of Siberian silk moth outbreaks in the fir forests of the Boguchanskii district

Table 3.10.

Necessary conditions of weather effects on the development of Siberian silk moth outbreaks in the fir forests of the Yeniseiskii district in the Krasnoyarsk Territory

Table 3.11.

Correlation coefficients of temperature variations in the Angara region in the pre-outbreak years

Table 3.12.

The structure of the contingency table for two weather parameters

Table 3.13.

Coherence of precipitation in the Angara region (June-August 1993), based on the data of the Boguchany and Yeniseisk weather stations

Table 3.14.

The coherence of precipitation events in the Angara region in pre-outbreak years

Table 3.15.

Correlation coefficients of HTC in the Angara region in pre-outbreak years

Chapter 4

Table 4.1.

Densities of population (individuals per tree) in Krasnoturansk pine forest

Table 4.2.

Long-term annual average densities (insects per tree) of the study species populations and standard deviations of the mean in different habitats

Table 4.3.

Characterization of cross-correlation functions for evaluation of the temporal coherence of different species in the same habitat*

Table 4.4.

Characterization of cross-correlation functions of a particular species in different habitats (the data for the

B.piniarius

are above the main diagonal, and the data for the

D.pini

are below the main diagonal)

Table 4.5.

Distances (km) between habitats in the Alps where surveys of populations of insects defoliating the European larch were carried out

Table 4.7.

Lists parameters of the lag

k

and the maximum value of the cross-correlation functions in the same habitat (Oberengadin)

Table 4.8.

Cross-correlation functions for larch bud moth populations in different habitats in the Alps

Chapter 5

Table 5.1.

The states of the engrailed moth population in the phases of outbreak peak, decline and crisis

Table 5.2.

The rate of parasitism of engrailed moth pupae in different population outbreak phases

Chapter 6

Table 6.1.

Table 6.2.

Relationship between the fecundity of black-veined white females and food consumption efficiency

Table 6.3.

Energy balance of Siberian silk moth larvae feeding on different tree species

Table 6.4.

The effectiveness of food consumption by the sixth-instar larvae of the larch and fir races

Chapter 7

Table 7.1.

Changes in statistical parameters of the time series of gypsy moth population dynamics during sequential data transformations

Table 7.2.

Characterization of gypsy moth outbreak cycles in the South Urals (1957–2012)

Table 7.3.

Calculating the coefficients of the regressive model (7.17) of gypsy moth population dynamics in the FSZ

Table 7.4.

Calculating the coefficients of the ADL(2,1) model of gypsy moth population dynamics in the SZ

Table 7.5.

Parameters of the AR(3) model

Table 7.6.

Geographical positions of larch bud moth outbreaks and weather stations closest to outbreak sites

Table 7.7.

Coefficients calculated for the model equation (7.17) of larch bud moth population dynamics, based on survey data collected between 1952 and 1979 (Oberengadin)

Table 7.8.

Coefficients calculated for the model equation (7.17) of larch bud moth population dynamics, based on the survey data collected in Goms, Valle Aurina, and Lungau

Table 7.9.

Forest inventory of the tree stands in different habitats of the Krasnoturansk pine forest

Table 7.10.

Coefficients of the AR(2)-model (7.19) for the data of the transformed series of pine looper population density in habitat “Lake”

Table 7.11.

Coefficients of the ADL(2, 1) model for the LTI series of pine looper population density in habitat “Lake”

Table 7.12.

Coefficients of the AR(2)-model for the LTI series of pine looper population density in habitat “Dune”

Table 7.13.

Coefficients of the AR(2)-model for the transformed time series of

D.pini

population density in habitat “Dune”

Table 7.14.

Parameters of the ADL(2,1) model of the European oak leaf-roller

Table 7.15.

Listing of the program in the MATLAB package for calculating gain margin of the autoregressive model (Gaiduk et al., 2011)

Table 7.16.

Coefficients of the AR(2) model and their ratios for defoliating insects in habitat Oberengadin in the Alps

Chapter 8

Table 8.1.

Relative colonization of trees estimated using survey data and models (8.16) and (8.17)

Table 8.2.

Evaluations of density

x

1

and coefficients of the model equation (8.23) of pine-tree lappet population dynamics

Table 8.3.

Parameters of the equations of the relationship between the order parameter and the tawny-barred angle population density (evaluation of

x

1

was based on the values of the medians

Me

[

x

] of population dynamics time series in the habitats)

Chapter 9

Table 9.1.

Coefficients of the AR(2) model for different lengths of the training set

Table 9.2.

Classification of the risks of occurrence of defoliating insect outbreaks as dependent on parameters of potential functions

Table 9.3.

Parameters of the potential functions of the defoliating insect populations

Table 9.4.

Characteristics of AR(2)-model of eastern spruce budworm population dynamics

Chapter 10

Table 10.1.

Climatic parameters in different habitats in the Krasnoyarsk Territory and the frequency of occurrence of pine looper outbreaks

Table 10.2.

Evaluations of model parameters of pine looper population dynamics in different habitats in the Krasnoturansk pine forest for the period between 1979 and 2014

Table 10.3.

PaR

values for pine looper populations in different simulation experiments

Table 10.4.

Risks of pine looper outbreaks under different scenarios of climate changes in Middle Siberia

Table 10.5.

Characterization of pine looper population dynamics

Table 10.6.

Characterization of the potential functions of pine looper populations in different habitats of pine forests in Europe

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Forest Insect Population Dynamics, Outbreaks, and Global Warming Effects

A. S. Isaev

V. G. Soukhovolsky

O. V. Tarasova

E. N. Palnikova

A. V. Kovalev

This edition first published 2017 by John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USA and Scrivener Publishing LLC, 100 Cummings Center, Suite 541J, Beverly, MA 01915, USA© 2017 Scrivener Publishing LLCFor more information about Scrivener publications please visit www.scrivenerpublishing.com.

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Library of Congress Cataloging-in-Publication DataISBN 978-1-119-40646-4

Authors

Alexander S. Isaev, D.Sc. (Biology), Full Member of the Russian Academy of Sciences (RAS), Head of Research at the Centre for Problems of Ecology and Productivity of Forests RAS (CEPF RAS). Graduated from the Leningrad Forestry Engineering Academy. An expert in forest entomology and ecology. Director of the V. N. Sukachev Institute of Forest and Wood SB USSR AS (1976–1985), Head of the USSR Forest State Committee (1985–1991), Director of CEPF RAS. The author of more than 300 published studies, including over 20 monographs on forest ecology and forest entomology. Awards: Gold Medal of the International Union of Forest Research Organizations (IUFRO), V. N. Sukachev Medal of RAS, and IUFRO George Varley Award for Excellence in Forest Insect Research.

Vladislav G. Soukhovolsky, D.Sc. (Biology), Professor, Leading Researcher at the V. N. Sukachev Institute of Forest SB RAS. Graduated from the Faculty of Physics at the Krasnoyarsk State University. An expert in mathematical modeling of complex biological, ecological, social, and political systems. The author of over 500 published studies, including 16 monographs.

Olga V. Tarasova, D.Sc. (Agriculture), Professor of the Department of Ecology at the Siberian Federal University. Graduated from the Faculty of Biology at the Krasnoyarsk State University. Between 1978 and 1981, a graduate student at the Department of Ecology at the Krasnoyarsk State University (Academic Adviser – A. S. Isaev). An expert in forest entomology. The author of over 150 published studies, including four monographs. Award: V. I. Vernadsky Award for Excellence in Ecological Education.

Elena N. Palnikova, D.Sc. (Agriculture), Professor of the Department of Ecology and Forest Protection at the Siberian State Technological University. Graduated from the Faculty of Biology at the Krasnoyarsk State University. Between 1978 and 1982, a graduate student at the V. N. Sukachev Institute of Forest and Wood SB USSR AS (Academic Adviser – A. S. Isaev). An expert in forest entomology. The author of over 100 published studies, including one monograph.

Anton V. Kovalev, Ph.D. (System Analysis). Senior Researcher of International Scientific Center for Organism Extreme States Research (Krasnoyarsk Scientific Center). Graduated from the Faculty of Automatization and Robototechnic at the Siberian Technological State University. Between 1999 and 2002, a graduate student at the V. N. Sukachev Institute of Forest SB RAS (Academic Adviser – V. G. Soukhovolsky). An expert in system analysis of ecological processes. The author of over 100 published studies, including one monograph.

Introduction

An insect outbreak is one of the first critical events in ecological systems described in world literature (Exodus 10:12). Until now, however, prediction and control of insect populations damaging forest stands and agricultural crops has remained an unresolved issue. The current insect outbreak situation can still be described with the biblical quote: “… When it was morning, the east wind had brought the locusts …”.

Research in insect population dynamics is important for more reasons than just protecting forest communities. Insect populations are among the main ecological units included in the analysis of stability of ecological systems. Moreover, it is convenient to test new methods of analyzing population and community stability on the insect-related data, as by now ecologists and entomologists have accumulated large amounts of such data.

In this book, the authors analyze population dynamics of quite a narrow group of insects – forest defoliators. We hope, though, that the methods we propose for the analysis of population dynamics of these species may be useful and effective for analyzing population dynamics of other animal species.

Below is a brief description of each chapter in the book.

Chapter 1 is, rather predictably, a review of the literature on modeling forest insect population dynamics. Section 1.3 provides a brief description of the phenomenological theory of population dynamics (Isaev et al., 1984; Isaev et al., 2001).

Chapter 2 discusses the issue that is seldom addressed in the literature – the choice of the way of describing insect population dynamics. In our opinion, for each definite task in the analysis of insect population dynamics, there is a specific way of data presentation: as a time series, a phase portrait, the “Lamerey stairs”, or potential function. Therefore, we discuss different ways of presenting survey data, as related to the purposes of the analysis.

We think that a necessary condition for the successful analysis of processes occurring in forest ecosystems is a certain irreverence towards the field data. As field ecologists, we know very well how much effort it takes to carry on insect population surveys on the same plot in the forest for many years. On the other hand, we are aware of the inaccuracy of the field data and the inevitable errors in estimates of the density of population dispersed over a vast area. Survey data should not be regarded as something incontrovertibly true but rather as a basis for research activities. These activities should include repair and transformation of the field data, based on the theoretical concepts developed in this research. Before using the survey data for analysis, they need to be “cleaned” as much as possible, to remove the inevitable errors of surveys, without distorting the time series. Our experience shows that it is important not only to collect the data but also to treat them properly. Therefore, Chapter 2 gives a detailed description of field data repair and transformation. This chapter focuses on the methods used to process survey data and transform an arbitrary time series into the stationary time series, which can then be studied by using standard techniques of correlation and spectral analysis.

Chapter 3 is devoted to the analysis of weather effects on the development of outbreaks of taiga defoliating insects. This subject has been extensively discussed in the literature, especially in the last decades, as related to the possible global climate change. Here we present our understanding of these processes.

Chapter 4 analyzes spatial coherence of population dynamics of the same insect species in different habitats and the temporal coherence of population dynamics of several insect species in the same habitat. Such analysis can be used to reveal interactions between species associated with, for example, competition for food and to estimate possible responses of different species to external impacts such as changes in weather and geophysical parameters.

Chapter 5 describes parasite – host interactions for populations of forest insects and their parasites in different outbreak phases.

In Chapter 6, we present a model of food consumption by insects, which links population dynamics with food properties. We propose a quasi-economic approach to describing food consumption and introduce indicators of food consumption analogous to costs in economics. In this way, we relate the energy and population approaches to the description of the processes in the forest – insect system and approach evaluation of fecundity of individuals – very important parameters for analysis and forecast of insect population dynamics.

Chapter 7 is devoted to modeling time series of forest insect population dynamics by using autoregressive models. The chapter describes models of population dynamics of the larch bud moth and other species of the defoliating insect community in forests of the Alps, the pine looper in Europe, defoliating insects in the Siberian pine forests, the European oak leaf-roller in European Russia, and the gypsy moth in the South Urals. For autoregressive models, we introduce parameters of stability, stability margin, and robust stability, which are used to assess the risks of “removal” of the species from the community. These models serve as a basis for developing adaptive methods for short-term forecasts of forest insect population dynamics.

Chapter 8 deals with a new method of describing and modeling forest insect population dynamics, based on the presentation of critical events in the population as first- and second-order phase transitions. Using the models of phase transitions, we managed to introduce conditions of the occurrence of forest insect outbreaks, describe the patterns of insect migrations in the forest during an outbreak, and characterize the susceptibility of populations to weather effects.

We consider in Chapter 9 methods of short-, medium-, and long-term forecasting of insect population dynamics based on the approaches described in the previous chapters and methods of assessing the risk of the tree stand damage and death caused by insect outbreaks. In addition to that, Chapter 9 contains a brief discussion of the problems associated with controlling the risks of insect attacks and making decisions about extermination measures based on forest entomological monitoring. We may have given too little consideration to these issues, and they will need to be discussed more thoroughly in a future study.

Finally, in Chapter 10, we discuss the effects of possible global climate change on population dynamics of defoliating forest insects. We use ADL-models and phase transition models developed in this book to assess the risks of outbreaks under various scenarios of climate change.

We hope that this book will be useful to specialists in ecology, entomology, ecological modeling, and forest protection as well as to undergraduate and graduate students of ecology and entomology.

We are grateful to our former and current Ph.D. students – S. Astapenko, Y. Bekker, O. Bulanova, P. Tsikalova, T. Iskhakov, I. Kalashnikova, P. Krasnoperova, V. Kuznetsova, M. Meteleva – for their assistance in different stages of the research. We specially appreciate out deceased colleagues – Yuri P. Kondakov and Viktor M. Yanovsky, with whom we had studied forest insect population dynamics for many years.

Our studies were supported by very many grants of Russian Foundation for Basic Research No. 96-04-48340, 99-04-49450, 00-04-48990, 02-04-48769, 02-04-62038, 03-04-49723, 03-04-62037, 04-04-49821, 08-04-00217, 08-04-07052, 09-04-00412, 10-04-08236, 11-04-00173, 11-04-08064, 15-04-01192.