Our world is a dynamic place. This constant, change, has myriad manifestations, some of which we view as negatively impacting on us and others as positively impacting on us. It should come as no surprise, therefore, that monitoring these changes to understand and prepare for their implications is not a novel endeavor, but a human enterprise that has undergone a long evolutionary process.
Perhaps the earliest form of what we would consider statistically based monitoring arose around the turn of the 17th century in the midst of the worst years of the plague. During this time, the lord mayor of London mandated that parish clerks compile “bills of mortality” to keep track of the ravages of the disease (Mlodinow 2008). From this monitoring data, a man by the name of John Graunt not only created the first life table, but also reached several groundbreaking conclusions about the prevalence and utility of the normal distribution (Mlodinow 2008). Over time as our values have shifted and we have been forced to confront distinct changes and challenges in our environment, the targets of monitoring have expanded. We still monitor human health, of course, but now also monitor the economy, our education systems, technological advances, and wildlife and their habitat.
The latter has been the topic of this book and we have attempted to provide a fairly consummate and practical overview of the current state of monitoring wildlife and their habitat. We are not the first group to have tackled this topic, neither was the author before us, nor the author before that publication, nor the one before that; indeed, every few years a new monitoring book is published to concisely update practitioners, researchers, and interested parties of the developments in the field. In other words, just like monitoring in a general sense, the monitoring of wildlife and habitat has undergone a long process of evolution that continues unabated even today. In fact, we are currently living in a time during which climate change, the state of international politics, and rapid scientific and technological advancement make the number of changes in our environment and the rate at which they occur astonishing. Thus, over the past thirty years, ecological monitoring and ecology in a more general sense have developed at a particularly impressive pace and incorporated a number of novel monitoring methods and mathematical ways of thinking (Moore et al. 2009).
So given these historical precedents and the volatility of our reality, where is the field headed? What techniques, technology, and mathematics are going to usurp those popular amongst today’s scientists? Will there be any changes in how monitoring data are applied? In this final chapter, we attempt to tackle some of these difficult questions and provide our interpretation of what the indicators of the present might mean for the future of monitoring.
Monitoring populations over time through the use of genetic analyses is not necessarily a cutting-edge idea, but the increasing affordability and precision of testing DNA, along with the increasing prevalence of fully sequenced genomes, are slowly creating a more practical, defensible, and widely used system for monitoring animal populations (Schwartz et al. 2006). Increased use frequently begets increased innovation and this certainly appears to be the case with genetic monitoring.
Schwartz et al. (2006) discuss the field as having two distinct approaches. The first is to undertake diagnostic assays in order to identify “individuals, populations, species and other taxonomic levels” (Schwartz et al. 2006). Data generated from iteratively sampling DNA and undertaking such assays for a population can be used in traditional population models estimating abundance or vital rates. In comparison with many traditional Capture-Mark-Recapture (CMR) techniques, this can be done in a relatively non-invasive manner (i.e. through using hair from hair snares or fecal samples) and may help to both reduce biases associated with capturing animals and to resolve the controversy and difficulty of capturing rare and elusive species. Diagnostic assays may also prove increasingly helpful in the monitoring of species’ range shifts and rates of hybridization as alterations in habitat and forced migration due to anthropogenic changes such as urban sprawl and climate change become more acute (Schwartz et al. 2006).
The second approach uses the monitoring of population genetic metrics such as effective population size, changes in allele frequencies, or estimates of changes to genetic diversity based on expected genetic heterozygosity, as indicators of more traditional population metrics (Schwartz et al. 2006). This approach will be particularly helpful if evolutionary principles can be reliably correlated to population dynamics such that inferences can be made about wild populations. For instance, think of the implications of being able to defensibly compare characteristics of DNA extracted from museum specimens with the DNA of wild specimens; this would allow retrospective monitoring and, potentially retrospective BACI experimental designs.
There are many other exciting potential applications of genetic monitoring. As a recent example, researchers used genetic analysis to estimate the population density and distribution of grizzly bears in and around Glacier National Park (Kendall et al. 2008, Kendall et al. 2009). Hair samples were collected through two sampling methods including systematically distributed, baited, barbed-wire hair traps and unbaited bear rub trees found along trails. The researchers estimated there was an average number of over 240 bears in the study area resulting in a density of 30/bears/1,000 km2. These non-invasive genetic methods provided critical baseline information for managing one of the few remaining populations of grizzlies in the contiguous United States, and holds promise for monitoring other large mammals through similar methods (Kendall and McKelvey 2008).
Genetic information could provide state wildlife agencies with an abundance of new information on how hunters are affecting game populations over time. This could allow them to more carefully regulate hunting in a way that maintains more genetically diverse and economically desirable populations. Using DNA analyses to monitor mixed-species fish stocks (i.e. some species of salmon), bird flocks (i.e. black ducks vs. mallard ducks), or mammal populations (i.e. New England cottontail vs. Eastern cottontail) that include rare and common species that are difficult to differentiate from one another but are nonetheless harvested due to their economic value could also lead to improved hunting regulations. Indeed, DNA analyses could provide insight into temporal or spatial patterns that are unique to each species, which could serve as a basis for more specific harvest regulations that effectively conserve the rare species. A similar approach has been effective with sockeye salmon in British Columbia (Beacham et al. 2004).
Kilpatrick et al. (2006) used DNA analyses to monitor the blood inside mosquitoes and were able to correlate a shift in feeding behavior from birds to mammals with patterns in West Nile virus outbreaks in North America. Using a genetic monitoring approach to other zoonotic diseases has enormous potential, especially if recent upward trends in urban wildlife populations and the transmission of their diseases to urban citizens are substantiated (Tsukada et al. 2000). Finally, the application of evolutionary principles to genetic monitoring in a general sense will almost certainly provide invaluable insights into how we manage and conserve populations and their habitat.
Despite the enormous potential, there are still a number of limitations to the use of DNA in monitoring. These range from the additional expense of iteratively undertaking DNA assays, the ease with which fraudulent samples can be inserted into the collected data, the prevalence and implications of genotyping errors on any inferences derived from monitoring, and a lack of powerful statistical tools to assess genetic metrics (Schwartz et al. 2006). Yet as additional research is undertaken and more sophisticated simulation software that models these metrics is derived, genetic monitoring will almost certainly help us to carry out wildlife and habitat monitoring more comprehensively.
Monitoring Environmental Change with Remote Sensing
Regardless of one’s personal opinions or conclusions concerning climate change, that its current and potential impact on our world is an increasing societal, political, and economic concern is undeniable. Further, the science linking climate change to inflated atmospheric levels of greenhouse gases is incontrovertible (IPCC 2007). In light of this, many governments and environmental organizations have either mandated or proposed tighter restrictions on society’s CO2 emissions via cap and trade systems, carbon taxes, stricter automobile regulations, or international treaties (Stavins 2008). As discussed in the Introduction to this textbook, these governments and organizations are going to want to know if the money spent designing and implementing these strategies to curb emissions is attaining their objectives. An increase in the monitoring of CO2 emissions in terms of prevalence and strategies, therefore, can only be expected.
One of the most recent innovations involves the use of satellite technology designed specifically for this purpose. In 2009, the Japanese government launched the Greenhouse Gases Observing Satellite (GOSAT), which is expected to collect useful data on global patterns of greenhouse gas emissions (GOSAT Project 2008). A similar U.S. initiative ended in failure (the satellite went into the ocean rather than space), but it seems likely that further efforts will be undertaken (Morales 2009).
Directly related to CO2 emissions is the carbon sequestration capacity of forests. This has historically been monitored to assess the impacts of deforestation on atmospheric CO2 levels, but also represents a way to monitor a locale’s contributions to mitigating carbon emissions through conservation and a means to generate data to justify programs to pay for this ecological service. The traditional approach is to undertake limited destructive harvesting in order to measure the capacity of individual trees to store carbon, carry out a ground-based forest inventory, and then use these two sets of data in conjunction with one another to make inferences about an entire forest’s, region’s, or country’s carbon sequestration capacity (Gibbs et al. 2007). Yet given that forest inventories are almost always local in extent by necessity, and that small changes in a tree’s characteristics may translate into large changes in carbon sequestration capacity, this approach is rife with potential biases. Although techniques to reduce them based on empirical studies of soils, topography, or climate have been advanced, extrapolating these local data across larger scales can still be tenuous (Gibbs et al. 2007). This has resulted in efforts to more effectively utilize remotely sensed data, which allows for the collection of information specific to each individual habitat type across a region. The typical approach to derive estimates for a forest’s capacity to sequester carbon with remotely sensed data is to measure proxies, such as all individual tree heights and crown diameters, and then apply allometric relationships between these proxies and carbon sequestration generated from ground-based studies (Gibbs et al. 2007). Nonetheless, this approach is also vulnerable to several biases and the reliability of such remotely sensed data in dense forests, such as many of those in the tropics, is questionable (Gibbs et al. 2007). Thus, despite significant advances, especially with the use of Radar sensors and light detection and ranging systems (LiDAR), significant work remains to be done before a comprehensively reliable system is created.
Such advances regarding the monitoring of carbon sequestration, as well as more general advances regarding the monitoring of greenhouse gas emissions in terms of sampling techniques and methods of analyzing data that is global in its extent should be expected. If the monitoring of climate change continues to reveal the enormous importance of the ocean in mitigating the impacts of this global phenomenon, we may also see the design of a rigorous system to measure and monitor the ocean’s capabilities as a carbon sink.
Advances in Community Monitoring and the Internet
If, as indicated in Chapter 3, community-monitoring becomes even more prevalent than it currently is, it is likely that monitoring techniques designed to attain a high degree of scientific rigor in the hands of the public as well as capture and keep the attention of non-scientists will become even more common. Many such community-oriented innovations to date have simply been variations of time-tested monitoring approaches such as avian syrveys (e.g. atlases) or simplified tools to measure a river’s nitrate and phosphate levels. There are also a number of efforts underway to increase the rigor of techniques historically popular amongst citizens, yet frowned upon by scientists. For instance, track-based monitoring has become increasingly adapted in recent years as sign, such as black bear bites and claw marks on trees, has been incorporated into designs previously based solely on track and scat counts (S. Morse, pers. comm.). Such efforts to include indicators that can withstand precipitation and are not as strongly impacted by variations in substrate reduce the potential for certain biases that have always plagued tracking techniques.
Several entirely novel innovations have come about with increases in the public availability of satellite imagery, increasing internet access, and the ease with which many citizens can now undertake sophisticated mapping exercises. For instance, the Green Map System enables citizens to create maps of their hometowns and insert data indicating the area’s most sustainable options for visitors and citizens alike (Green Map). Open source style maps on the system’s website allow users to monitor changes in these locales on the ground and update maps when needed. These projects are akin to the monitoring of communities by communities, and researchers have only begun to scratch the surface of using social networking internet sites (e.g., Facebook) for community-based monitoring projects.
The internet will continue to allow communities to monitor their own natural resources and local animal populations in novel and exciting ways. As Google Maps, Google Earth, and Google Oceans are refined, more of these interactive, democratic community-monitoring and mapping projects will likely evolve in unexpected ways. The participatory monitoring of wildlife populations and their habitats in a public forum is one way for humankind to conceptualize and rigorously keep abreast of our impacts on the ecosystems in which we are embedded and the enormous scale on which they act.
A New Conceptual Framework for Monitoring
Although the monitoring of wildlife and their habitat and the particular scientific theories that inform it draw heavily from current ideas in ecology, there is also a clear disconnect between ecologists in academia and many who design and implement monitoring programs. Indeed, there tends to be a time lag between the implementation of new ideas in strict ecological research and the subsequent implementation of those ideas in ecological monitoring and management. This is likely due to the conflict between the inevitable uncertainty and theoretical basis of many new concepts in ecology and the need for land managers and practitioners carrying out monitoring to be confident in their protocols and to accomplish specific objectives that are planned far in advance (Moore et al 2009). To put it simply, land managers and those given a mandate to monitor often have to minimize the risk involved with their projects to maximize their job security. Given this relationship between ecological thinking and monitoring and management, will the key concepts in contemporary ecological thinking manifest themselves in the world of monitoring wildlife and their habitat? If so, how?
A Reflection on Ecological Thinking
Moore et al (2009) undertook a Delphi study using a panel of professional ecologists to determine where those concepts currently stand. The most unanimously agreed upon ideas were that:
- Disturbances are extremely prevalent and historically contingent phenomena that can impact ecosystems,
- Considering multiple levels and how each impacts on the ecosystem and on one another is integral to understanding an ecosystem, and finally,
- Simple biodiversity is a poor measure and functional diversity is largely what determines the future characteristics of the ecosystem (Moore et al 2009).
These ideas, particularly the concept of ecosystems changing via disturbance and consisting of multiple levels, have supported a strong movement toward conceptualizing ecosystems as complex, dynamic, open systems rather than the more parochial views of the past (Moore et al 2009).
There is, of course, no means of telling how or if these concepts will be involved in future monitoring programs. However, if not only ecological thinking, but also contemporary societal values are taken into account, it seems likely that many of these ideas will be integrated into ecological monitoring. In a general sense, contemporary societal values relative to wildlife and their habitats are increasing in complexity due to the global scale on which our current environmental crises and issues act. Global climate change, the globalization of our economy, and an increasing desire to buy “green” products has citizens more carefully monitoring how their everyday behaviors impact on the global environment. In other words, citizens are beginning to incorporate many of the ecological science ideas discussed by Moore et al (2009) in their lives. For instance, citizens have begun to display the belief that the actions that cause disturbances today will partially determine the state of the ecosystem in the future. Indeed, many strive to emit less CO2 under the assumption that it will make for a more agreeable global climate with fewer health complications in the future (Fay Cortez and Morales 2009, Terrapass 2009). Also, citizens are behaving in ways that display a belief that an ecosystem is impacted by several different interacting levels; there is a strong movement, for example, to buy “green” products that advertise how their producers support conservation elsewhere (UPFRONT 2009). There is a consumer movement based on the idea that environmentally friendly producers in a particular locale can be supported by broader economic activity and that the interaction of and activity on both levels impacts the environment. There is also a strong movement to eradicate invasive species based on the assumption that native species are more ecologically healthy and support higher diversity than invasive species (Ruiz and Carlton 2003). Finally, the breadth of these conservation activities, which occur in the grocery market, the gas station, and the local farm, indicate that citizens are implementing, whether consciously or not, a more systems-based approach to the global environment. Conservation and preservation are no longer defined by fencing off protected areas and strictly regulating access and use, but by a variety of consumer behaviors, personal decisions, and lifestyle changes.
Given that both ecologists and citizen behaviors and beliefs, which are driven by their values, are largely congruous, and that these are important determinants of the current state of monitoring wildlife and their habitats, it seems highly likely that monitoring will also begin to exhibit similar trends. This means that a more systems-based approach to monitoring may become prevalent. Such approaches monitor indicators at different scales and levels and seeks to integrate not simply components of the local ecosystem, but a broader ecosystem that involves the impacts of human beings on several levels. The typical scale of monitoring may become even larger given widespread concern about global warming and the shifts in flora and fauna that it will cause.
To be sure, monitoring has already exhibited some of these trends. The Breeding Bird Atlas and TRANSECT programs, for instance, have very large geographical scopes. Project BudBurst, albeit designed for younger students, enlists citizens across the United States to monitor the phenophases of their local plants over time, which they hope will create an informative series of maps that describe trends in plant growth that can be compared with climate data to look for correlations (Project Budburst 2009). Further, as indicated earlier in this chapter, the indicators that wildlife and habitat monitoring programs utilize and the manner in which they do so are expanding. This includes looking at novel indicators on a small scale (DNA) to those on a larger scale (CO2 emission). If a systems-based approach becomes the norm, monitoring programs that include a variety of both new and old techniques that all address different levels impacting a locale may become the norm.
Dealing with Complexity and Uncertainty
The likely transition of ecological monitoring to a more holistic endeavor that seeks to track changes in multiple animal populations and ecosystems will undoubtedly have a number of analytical challenges in the future. As has been discussed throughout this book, natural systems and populations are dynamic and complex. This complexity changes over time, and in an unpredictable fashion, yet scientists are expected to make predictions of these systems based on the data they collect and their management actions. This is the ultimate moving target in a system full of uncertainty.
Recently, ecologists have turned to advanced mathematics and statistics to aid them in dealing with this dynamic uncertainty. As an example, Chades et al. (2008) proposed partially observable Markov decision processes (POMDP) as an approach for placing resource allocation and monitoring decisions into an objective decision-making process. The authors model three possible scenarios regarding the management of the Sumatran tiger within the Kerinci Seblat region including population management, population surveys to assess whether it is still extant in the region, or cease all conservation efforts and focus resources elsewhere (Chades et al. 2008). The approach identifies which approach should be made each year, for a series of years, given the current belief about the state of the population (extinct or extant). The POMDP approach has several advantages to decision-making in monitoring and may have much to offer population monitoring and adaptive management in the future (MacKenzie 2009). What is becoming increasingly clear, however, is that the monitoring of animal populations and their habitats will rely on quantitative and statistical advancements for dealing with uncertainty. This poses a significant problem for many ecologists and managers who are not professionally trained in advanced statistics or computational mathematics, but are responsible for studying and managing natural resources. Consequently, the future of monitoring may involve non-traditional collaborations between ecologists, managers, computational scientists, and statisticians. As an example, Computational Sustainability is an emerging field that aims to apply techniques from computer science, information science, operations research, applied mathematics, and statistics for balancing environmental, economic, and societal needs for sustainable development. This field promises to have a major influence in ecological research and monitoring in the near future and in the development of computational and mathematical models for decision making in natural resources management. The advantage of these types of collaborations and approaches is that it often involves combinatorial decisions for the management of highly dynamic and uncertain environments. The first annual conference in Computation Sustainability was held in June 2009 at Cornell University and brought together over 200 computer scientists, applied mathematicians, statisticians, biologists, environmental scientists, biological and environmental engineers, and economists. The future of monitoring and predicting complex ecological systems may very well depend on these types of partnerships.
Monitoring is a process of gaining in formation and revising approaches to management based on the information gained. This book and others like it have been and will continue to be a part of the monitoring process. Recent approaches that show considerable promise for expansion and proliferation in use among monitoring processes are DNA approaches, community monitoring systems, and systems-based frameworks for collecting and synthesizing monitoring data. Open source monitoring frameworks allow direct input and utilization of monitoring data on that benefits many stakeholders simultaneously and allow many minds to contribute solutions to complex problems based on the data available.
Analytical approaches also will have to adapt to these changing systems to allow rigorous analysis of a steady flow of incoming data so that stakeholders can interpret results to address their goals and objectives. Results will need to directly quantify uncertainty, and they will need to be easily synthesized into systems based projections of current and likely future conditions. Synthetic approaches must extend beyond biologists and ecologists to economists, social scientists, and mathematicians, among others, to build team approaches to addressing the complex challenges facing wildlife populations and the habitats on which they survive.
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