To save a species from extinction, it is vital to have a firm grasp on the species’ distinctive characters, in other words its natural history. To obtain this natural history information, 10 important factors need to be considered:
To save a species from extinction, it is vital to have a firm grasp on the species’ distinctive characters, in other words its natural history.
- Population biology: How many individuals are there in the population? How many males, females, juveniles, breeding adults, and individuals past breeding age are there? What is the species’ life expectancy? How have these aspects changed over time? (see also Chapter 9)
- Habitat: In what kind of environment can the species be found? How do these ecosystems change over time and space? Does the species have a complex life history that requires multiple habitats (e.g. frogs that live on land generally need water for breeding)? What factors are important to maintain suitable habitat?
- Distribution: Where in the world can the species of concern be found? At what rate is its distribution increasing/decreasing? What factors drive these increases/decreases?
- Morphology: What are the defining traits, or range of traits, of the species’ appearance? How do the species’ unique morphological characteristics help it survive? Are there closely-related species that appear similar (i.e. cryptic species) and with which it can be misidentified?
- Limiting resources: What types of resources does the species need to survive? Are any of these resources in short supply? Does the distribution of these important resources change over time and space?
- Physiology: Are there any special requirements the species’ physical and biochemical processes need for it to grow, survive, and reproduce? What are the conditions under which meeting these requirements is especially hard?
- Behaviour: How do individuals act or behave (Box 11.1)? Is the species sedentary, nomadic, or migratory? Do individuals group together, disperse at random throughout landscapes, or space themselves out at regular distances? How do these behaviors help it survive?
- Genetics: How much do genes vary within the species? How are the species’ genetics linked to its morphology, physiology, and behavior? Are there local genetic adaptations? Is the genetic variation in key traits sufficient to allow the species to adapt to environmental changes? Are there any deleterious genetic concerns? (Section 8.7.1)
- Biological interactions: In what ways do individuals of the species interact with each other and with other species? Which of these interactions are critical for survival? Are there any competitors, predators, parasites, or diseases affecting the species?
- Interactions with humans: How sensitive is the species to human activity? Do humans use the species in any way? Is the species sustainably harvested? Is the species associated with human-wildlife conflict (Section 14.4)?
Adrian M Shrader
Mammal Research Institute,
Department of Zoology and Entomology,
University of Pretoria, South Africa.
When considering the management and conservation of wild animals, topics linked to population and community ecology (e.g. carrying capacity, Hayward et al., 2007a) often come to mind. This is not surprising, as these disciplines consider broad patterns of population dynamics (e.g. birth rates and mortality rates), which are key to achieving management and conservation goals. While this information is necessary, in many instances, it fails to explain the mechanisms behind the patterns observed and answer key questions. For example, why do species prefer specific habitats? Why do some herbivores adjust their home ranges with the seasons? To answer these sorts of questions, we need to understand an animal’s behavioral ecology.
Take for example the challenge of understanding the impacts that elephants cause within protected areas. A standard way to assess these impacts is to record which tree species are damaged and how many trees are affected (e.g. broken branches, bark stripping) (Boundja and Midgley, 2010). While this provides information on the trees most vulnerable to elephant damage, it does not explain why elephants are damaging the trees. Is it because the trees are a key part of the elephants’ diet, or are these trees just abundant across the landscape and in the way of a moving herd? To answer these questions, we turn to behavioral ecology. By observing foraging elephants, or by walking down their feeding paths after they have left, we can determine the animals’ diet, and generate an acceptability index (number eaten ÷ number available) of each tree species (Shrader et al., 2012). These data allow us to better understand the reasons behind elephant damage.
Other situations where behavioral ecology can help include reintroductions, population management, and human-animal conflict mitigation. For example, in South Africa, oribi are locally threatened by habitat loss and poaching. One conservation strategy is to relocate individuals away from known threats. Oribi are grassland specialists (Figure 11.A) that require both short and tall grasslands—therefore, release sites require a mosaic of these habitats. Moreover, within grasslands oribi perceive woodland patches to be dangerous, and tend to avoid feeding within 15 m of them (Stears and Shrader, 2015). If we do not consider how oribi utilise their environment, our estimate of available habitat at a release site may be greater than the area utilised. This mistake could reduce relocation success.
With regards to population management, behavioral ecology is central to the conservation of southern white rhinoceros (Ceratotherium simum simum, NT) in the Hluhluwe-iMfolozi Park, South Africa. Within the park, the management policy incorporates space use and social ecology of the rhinos to facilitate population regulation (i.e. dispersal). To do this, the population can grow in the central core of the park. When rhino numbers get too high in the core, individuals naturally disperse into surrounding low-density areas, at which point they are captured by wildlife officers and transported to other areas. Thus, rhino behavior itself is used to indicate when there are too many individuals within the fenced park (Linklater and Shrader, 2017).
Finally, behavioral ecology has helped reduce human-elephant conflict through the understanding that elephants are afraid of bees and will avoid feeding close to them. To capitalise on this fear, fences that incorporate beehives were designed and constructed around agricultural fields in northern Kenya, which helped reduce crop damage from raiding elephants. Of 32 raids recorded in the area, only one was at a farm with a beehive fence (King et al., 2011). These examples showcase how behavioral ecology can support, expand, and strengthen management and conservation of wildlife. These same principles can be applied to protect a wide range of animals across Africa, and elsewhere.
Understanding the natural history of a species directly informs conservation strategies. For example, if we know where a species occurs and what its habitat needs are, we are in a better position to prioritise which areas need to be protected or how ecosystems need to be restored. Similarly, if we know that an important food resource is missing, perhaps during a drought or due to human activities, conservationists could provide supplemental feeding until the limiting resource has recovered (Figure 11.2). Depending on the species in question, some factors play a more important role than others. For example, managing a disease outbreak may play a more important role in the conservation of a widespread migratory bird (that can spread diseases to other species), while managing for genetic diversity may play a more prominent role in the conservation of a small population of fishes restricted to only one lake. For many widespread species, different factors affect different subpopulations. In such cases each subpopulation might need to be managed as its own evolutionary significant unit (ESU; see e.g. Dubach et al., 2013) to retain unique local adaptations and genetic markers.
Obtaining natural history data
Conservationists rely on several resources and techniques to obtain natural history information. Initial steps often involve reviewing published and unpublished literature to understand what is known (and not known) about a species. Literature reviews do have some drawbacks: they can take a long time, may uncover contradictory information, and may lack critical information relevant to a local area or specific population. For this reason, and especially when decisions need to be made under tight schedules, conservation biologists may need to speed up their initial species review by sourcing natural history information from subject matter experts who are familiar with the species or ecosystem of concern.
Conservation biologists are increasingly recognizing the importance of traditional ecological knowledge—detailed insights that rural people have on the species around them.
Conservation biologists are also increasingly recognizing the importance of traditional ecological knowledge (TEK)—detailed insights that rural people have on the ecology, behavior, and distribution of the species around where they live (Shackeroff and Campbell, 2007; Brook and McLachlan, 2008). For example, while termites are often considered a pest by people living in urban settings, scientists are increasingly relying on TEK to understand the important contributions of termites to food security to human health, as well as to learn about ecological sustainable methods for their control when needed (Sileshi et al., 2009).
While literature reviews, expert opinions, and traditional ecological knowledge are important first steps to collect natural history information, the most reliable method remains fieldwork, where multiple individuals from the population of concern in the area of interest are observed repeatedly over time. Indeed, most of natural history information we have today was obtained during detailed notetaking by naturalists—biologists who dedicate much of their time to better understand the natural world—in the field.
Unfortunately, there are still major gaps in our understanding of the living world. Consequently, a very large number of threatened species, including better-known groups (e.g. reptiles, Tolley et al., 2016), lack the kinds of data necessary to ensure that we can give them the best chance of survival. Filling these gaps is also becoming harder since it is costly and sometimes logistically impossible (or dangerous) for naturalists to spend an extended period in the field. There is also a trade-off in the breadth and depth of data collection possible: the more area one covers, the less detailed the data; conversely, when one collects more detailed data, the scope of the study is reined in for logistical constraints. Further, there is also a limit to the number of organisms any one individual observer can study at any one time.
Recent technological advances have greatly increased our ability to overcome the logistical constraints that impede conservation fieldwork. One of the most useful developments involves the miniaturisation (and reduced costs) of animal-borne biologging devices, such as radio telemetry and GPS tags (Kays et al., 2015). Previously reserved for projects with large grants that focused on large animals, the big clunky devices of a few decades ago have made way for devices small enough to fit comfortably on animals as small as beetles and frogs. Some biologging devices are now also solar-powered and transmit data through Earth observation satellites in real time, allowing researchers to track the behaviors of several organisms at a time from the comfort of their offices. Even better, some tracking technologies also collect environmental data and movement data simultaneously, allowing us to better understand how wildlife responds to changing environmental conditions. These new and sophisticated datasets can then be used to better understand threats to species (e.g. Scantlebury et al., 2014; Childress et al., 2016) and inform management of protected areas (e.g. Maxwell et al., 2011).
Species distribution modelling (SDM), also known as environmental niche modelling, is becoming increasingly popular for determining a species’ distribution and habitat needs. SDMs overlay species location data, obtained during field work or using biologging devices, onto a selection of relevant environmental variables (e.g. forest cover, elevation, soil type) using GIS software, after which special modelling algorithms estimate the species’ ecological niche and distribution (Figure 11.3, see also Figure 10.3). This information enables conservation biologists to identify previously unknown habitat patches (which may represent undiscovered and unprotected populations) or empty habitats (which may be used in translocations, see Section 11.2). The appeal of SDMs lies in the availability of user-friendly software packages that can use very limited datasets. For example, one study from West Africa successfully combined market survey data and SDM to determine the potential for sustainable extraction of 12 medicinal plant species (van Andel et al., 2015). Another study used SDMs to develop a holistic picture of diversity and endemism patterns of nearly all 250 African bat species (Herkt et al., 2016). While distribution modelling offers very useful conservation tools, it is important to learn about the different techniques under the guidance of an expert to avoid making costly mistakes (McPherson et al., 2006; Pearson et al., 2006).
Species distribution modelling, also known as environmental niche modelling, is becoming increasingly popular for determining a species’ distribution and habitat needs.
Experimentation offers powerful methods to better understand competing theories and hypotheses, and to gain insight into how specific management actions may influence population dynamics. Experimentation is often associated with controlled environments such as laboratories; however, this is often impossible and sometimes even unethical to perform laboratory experiments on threatened species. Instead, conservation researchers may opt for natural experiments, which allows for the target species or population to be studied in its natural ecosystem.
A chronosequence study is a special type of natural experiment that overcome the long-term commitment some studies require to attain meaningful results. Also called space-for-time experiments, chronosequence studies allow us to infer long-term trends over a short study period using study systems that share similar qualities but are differently aged. Chronosequence studies are particularly popular when studying ecological restoration projects (Section 10.3) since some ecological processes often require many decades to develop (Bonnell et al., 2011). In one such example, conservation biologists needed only three summers worth of vegetation surveys to show that some species recolonise coastal dune forests in the Maputaland-Podoland-Albany Biodiversity Hotspot only after 100 years since disturbance (Wassenaar et al., 2005).
Sometimes, despite their best efforts, biologists may still fail (or may not have enough time) to obtain much needed natural history information during a critical period. To overcome such a challenge, biologists have, at times, used natural history information of a substitute species (which is different from surrogate species, Section 13.3.5) to fill data gaps for a rare species (Caro et al., 2005). An example of this application comes from the USA where researchers used behavioral observations of a common butterfly to predict dispersal of another closely related butterfly that was too rare to properly study (Hudgens et al., 2012). It is important to note that using information from substitute species does have serious limits (Henry et al., 2019). For example, considering that different populations of a single species may have very different environmental needs and adaptations, using data from a different species may be even less useful. Care must therefore be taken when using data from substitute species with proper acknowledgement of the assumptions and uncertainty this approach adds to one’s research.