Tag: foraging competition

What drives individual specialization?

Clara Bird, PhD Student, OSU Department of Fisheries, Wildlife, and Conservation Sciences, Geospatial Ecology of Marine Megafauna Lab

When I wrote my first blog on individual specialization well over a year ago, I just skimmed the surface of the literature on this topic and only started to recognize the importance of studying individual specialization. The question, “is there individual specialization in the PCFG of gray whales?” is the focus of my first thesis chapter and the results will affect all my subsequent work. Therefore, the literature and concepts of individual specialization are a focus of my literature review and studies.

In my previous blog I focused on common characteristics of individuals that are similarly specialized as an underlying driver of individual specialization. While these characteristics (often attributable to age, sex, or physical traits) are important to consider, I’ve learned that the list of drivers of individual specialization is long and that many variables are dynamic. Of all the drivers I’ve learned about, competition is among the most common.

Competition is a major driver of individual specialization, and a common driver of competition is resource availability. When resource availability decreases, whether caused by increasing population density or changing environmental conditions, competition for that resource increases. As competition increases, individuals have a choice. They can choose to engage in competition, either by racing, fighting, or sharing [1], which can be costly, or they can diffuse the competition by focusing on a different resource.  This second approach would be considered niche partitioning in the prey dimension. Niche partitioning is a way for individuals to share ecological space by using different resources. Essentially, individuals can share habitat without having to engage in direct competition by pursuing different prey types [2]. 

This switch to different prey types can change the degree of individual specialization present in the population (Figure 1). But the direction of the change is not constant. If all individuals were pursuing the same prey type under low competition conditions but then switched to different alternate prey types under high competition, then individual specialization would increase (Figure 1a). This direction has been observed across a range of species including sharks [3], otters [4]–[7], dolphins [8], [9], stickleback fish [10], [11], largemouth bass [12], banded mongoose [13], fur seals [14], and baleen whales [15].

However, if individuals were pursuing different prey types under low competition conditions (maybe because of underlying differences such as age or sex) but then switched to the same alternate prey types under high competition, diet overlap would increase, and individual specialization would decrease (Figure 1b). Furthermore, an individual might not switch to an entirely new prey type but instead add prey items to its diet [16]. This diet expansion under competition would also decrease individual specialization. Fewer studies have reported this direction but it’s been found in the common bumblebee [17] and in several neotropical vertebrate species [18], [19].

Figure `1. Figure 3 from Araújo et al. 2011 [20]. Illustration of how ecological mechanisms may affect the degree of individual specialization. Arrows linking resources to individual consumers indicate resource consumption (relative thickness indicates proportional contribution). 
Horizontal arrows indicate the sign (positive or negative) of the effect on the degree of individual specialization. (a) Consumers share the same preferred resource (dark gray tangle) but have different alternative resources (white and light gray triangles). As the preferred resource becomes scarce, consumers switch to different alternatives, increasing the degree of individual specialization. (b) Alternatively, consumers have distinct preferred resources, so that as resources become scarce, individuals converge to the alternative resource (dark gray triangle), reducing diet variation.

Interestingly, its hypothesized that individual specialization driven by competition is one of the factors that facilitates the formation and existence of stable groups [21]. For example, a study on resident female dolphins in Sarasota Bay, FL, USA found that females with high spatial overlap used distinct foraging specializations [8](Figure 2). This study illustrates how partitioning prey enabled spatial and social coexistence. A study on banded mongooses reached a similar conclusion [13]. They found that specialization was highest in the biggest groups (with the most competition) and not explained by sex, age, or other inherent differences. They hypothesized that specialization increasing with competition reduced conflict and allowed the groups to remain stable. This study also highlighted the role of learning to determine an individual’s specialization.

Figure 2. A bottlenose dolphin.
Source: https://sarasotadolphin.org

Learning drives the distribution of knowledge throughout a population, which can lead to either specialization or generalization. ‘One-to-one’ learning, where one individual learns from one demonstrator, tends to promote individual specialization [21]. This form of transmission drives specialization because the individuals who learn the specialization tend to then carry on using, and eventually teaching, that specialization [6]. A common example of ‘one-to-one’ learning is vertical transmission from parent to offspring. It has been shown to transmit specializations in dolphins [22] and otters [6]. ‘One-to-one’ learning can occur outside of parent-offspring pairs; non-parent-offspring ‘one-to-one’ learning has been shown to drive specialization in banded mongooses [13](Figure 3).

However, other forms of social learning can promote more generalized foraging strategies. ‘Many-to-one’ or ‘one-to-many’ learning  can reduce the presence of specialization in species [13], [21] as can the presence of conformity in a group [23], [24].

Figure 3. A group of banded mongooses.
Source: http://socialisresearch.org/about-the-banded-mongoose-project/

The multiple drivers of specialization and their dynamic quality means that it is important to contextualize specialization. For example, a study on four species of neotropical frogs found varying degrees of specialization across multiple populations of each species [18]. The degree of specialization was dependent on a variety of drivers including predation and both intra- and inter-specific competition. Notably, the direction of the relationship between degree of specialization and each driver was species specific. This study highlights that one species may not always be more specialized than another, but that a populations’ specialization is context dependent.

Therefore, it is important to not only be aware of the degree of specialization present in a population, but to also understand its dynamics and drivers. These relationships can then be used to understand how, and why, a population may react to competition from other species, predators, and changes in resource availability [20].  A population’s specialization can also affect the specialization of other populations and community dynamics [25], therefore, it’s important to consider and study individual specialization on both the population and community level. I am excited to start using our incredible six-year dataset to start investigating these questions for PCFG gray whales, so stay tuned for results!

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References

[1]       M. Taborsky, M. A. Cant, and J. Komdeur, The Evolution of Social Behaviour. Cambridge: Cambridge University Press, 2021. doi: 10.1017/9780511894794.

[2]       E. R. Pianka, “Niche Overlap and Diffuse Competition,” vol. 71, no. 5, pp. 2141–2145, 1974.

[3]       P. Matich et al., “Ecological niche partitioning within a large predator guild in a nutrient-limited estuary,” Limnol. Oceanogr., vol. 62, no. 3, pp. 934–953, 2017, doi: https://doi.org/10.1002/lno.10477.

[4]       S. D. Newsome et al., “The interaction of intraspecific competition and habitat on individual diet specialization: a near range-wide examination of sea otters,” Oecologia, vol. 178, no. 1, pp. 45–59, May 2015, doi: 10.1007/s00442-015-3223-8.

[5]       M. T. Tinker, G. Bentall, and J. A. Estes, “Food limitation leads to behavioral diversification and dietary specialization in sea otters,” Proc. Natl. Acad. Sci., vol. 105, no. 2, pp. 560–565, Jan. 2008, doi: 10.1073/pnas.0709263105.

[6]       M. T. Tinker, M. Mangel, and J. A. Estes, “Learning to be different: acquired skills, social learning, frequency dependence, and environmental variation can cause behaviourally mediated foraging specializations,” Evol. Ecol. Res., vol. 11, pp. 841–869, 2009.

[7]       M. T. Tinker et al., “Structure and mechanism of diet specialisation: testing models of individual variation in resource use with sea otters,” Ecol. Lett., vol. 15, no. 5, pp. 475–483, 2012, doi: 10.1111/j.1461-0248.2012.01760.x.

[8]       S. Rossman et al., “Foraging habits in a generalist predator: Sex and age influence habitat selection and resource use among bottlenose dolphins (Tursiops truncatus),” Mar. Mammal Sci., vol. 31, no. 1, pp. 155–168, 2015, doi: https://doi.org/10.1111/mms.12143.

[9]       L. G. Torres, “A kaleidoscope of mammal , bird and fish : habitat use patterns of top predators and their prey in Florida Bay,” vol. 375, pp. 289–304, 2009, doi: 10.3354/meps07743.

[10]     M. S. Araújo et al., “Network Analysis Reveals Contrasting Effects of Intraspecific Competition on Individual Vs. Population Diets,” Ecology, vol. 89, no. 7, pp. 1981–1993, 2008, doi: 10.1890/07-0630.1.

[11]     R. Svanbäck and D. I. Bolnick, “Intraspecific competition drives increased resource use diversity within a natural population,” Proc. R. Soc. B Biol. Sci., vol. 274, no. 1611, pp. 839–844, Mar. 2007, doi: 10.1098/rspb.2006.0198.

[12]     D. E. Schindler, J. R. Hodgson, and J. F. Kitchell, “Density-dependent changes in individual foraging specialization of largemouth bass,” Oecologia, vol. 110, no. 4, pp. 592–600, May 1997, doi: 10.1007/s004420050200.

[13]     C. E. Sheppard et al., “Intragroup competition predicts individual foraging specialisation in a group-living mammal,” Ecol. Lett., vol. 21, no. 5, pp. 665–673, 2018, doi: 10.1111/ele.12933.

[14]     L. Kernaléguen, J. P. Y. Arnould, C. Guinet, and Y. Cherel, “Determinants of individual foraging specialization in large marine vertebrates, the Antarctic and subantarctic fur seals,” J. Anim. Ecol., vol. 84, no. 4, pp. 1081–1091, 2015, doi: 10.1111/1365-2656.12347.

[15]     E. M. Keen and K. M. Qualls, “Respiratory behaviors in sympatric rorqual whales: the influence of prey depth and implications for temporal access to prey,” J. Mammal., vol. 99, no. 1, pp. 27–40, Feb. 2018, doi: 10.1093/jmammal/gyx170.

[16]     R. H. MacArthur and E. R. Pianka, “On Optimal Use of a Patchy Environment,” Am. Nat., vol. 100, no. 916, pp. 603–609, 1966, doi: 10.1086/282454.

[17]     C. Fontaine, C. L. Collin, and I. Dajoz, “Generalist foraging of pollinators: diet expansion at high density,” J. Ecol., vol. 96, no. 5, pp. 1002–1010, 2008, doi: 10.1111/j.1365-2745.2008.01405.x.

[18]     R. Costa-Pereira, V. H. W. Rudolf, F. L. Souza, and M. S. Araújo, “Drivers of individual niche variation in coexisting species,” J. Anim. Ecol., vol. 87, no. 5, pp. 1452–1464, 2018, doi: 10.1111/1365-2656.12879.

[19]     M. M. Pires, P. R. Guimarães Jr, M. S. Araújo, A. A. Giaretta, J. C. L. Costa, and S. F. dos Reis, “The nested assembly of individual-resource networks,” J. Anim. Ecol., vol. 80, no. 4, pp. 896–903, 2011, doi: 10.1111/j.1365-2656.2011.01818.x.

[20]     M. S. Araújo, D. I. Bolnick, and C. A. Layman, “The ecological causes of individual specialisation,”Ecol. Lett., vol. 14, no. 9, pp. 948–958, 2011, doi: https://doi.org/10.1111/j.1461-0248.2011.01662.x.

[21]     C. E. Sheppard, R. Heaphy, M. A. Cant, and H. H. Marshall, “Individual foraging specialization in group-living species,” Anim. Behav., vol. 182, pp. 285–294, Dec. 2021, doi: 10.1016/j.anbehav.2021.10.011.

[22]     S. Wild, S. J. Allen, M. Krützen, S. L. King, L. Gerber, and W. J. E. Hoppitt, “Multi-network-based diffusion analysis reveals vertical cultural transmission of sponge tool use within dolphin matrilines,” Biol. Lett., vol. 15, no. 7, p. 20190227, Jul. 2019, doi: 10.1098/rsbl.2019.0227.

[23]     L. M. Aplin, D. R. Farine, J. Morand-Ferron, A. Cockburn, A. Thornton, and B. C. Sheldon, “Experimentally induced innovations lead to persistent culture via conformity in wild birds,” Nature, vol. 518, no. 7540, pp. 538–541, Feb. 2015, doi: 10.1038/nature13998.

[24]     E. Van de Waal, C. Borgeaud, and A. Whiten, “Potent Social Learning and Conformity Shape a Wild Primate’s Foraging Decisions,” Science, Apr. 2013, doi: 10.1126/science.1232769.

[25]     D. I. Bolnick et al., “Why intraspecific trait variation matters in community ecology,” Trends Ecol. Evol., vol. 26, no. 4, pp. 183–192, Apr. 2011, doi: 10.1016/j.tree.2011.01.009.

Understanding How Nature Works

By: Erin Pickett, MS student, Oregon State University

They were climbing on their hands and knees along a high, narrow ridge that was in places only two inches wide. The path, if you could call it that, was layered with sand and loose stones that shifted whenever touched. Down to the left was a steep cliff encrusted with ice that glinted when the sun broke down through the thick clouds. The view to the right, with a 1,000ft drop, wasn’t much better.

–The Invention of Nature by Andrea Wulf

This is a description of Alexander von Humboldt and the two men that accompanied him when attempting to summit Chimborazo, which in 1802 was believed to be the highest mountain in the world. The trio was thwarted about 1,000 ft from the top of the peak by an impassable crevice but set a record for the highest any European had ever climbed. This was a scientific expedition. With them the men brought handfuls of scientific instruments and Humboldt identified and recorded every plant and animal species along the way. Humboldt was an explorer, a naturalist, and an observer of everything. He possessed a memory that allowed him to recount details of nature that he had observed on a mountain in Asia, and find patterns and connections between that mountain and another in South America. His perspective of nature as being interconnected, and theories as to why and how this was so, led to him being called the father of Ecology. In less grandeur terms, Humboldt was a biodiversity explainer.

Humboldt sketched detailed images like this one of Chimborazo, which allowed him to map vegetation and climate zones and identify how these and other patterns and processes were related. Source: http://www.mappingthenation.com/blog/alexander-von-humboldt-master-of-infographics/

In a recent guest post on Carbon Brief, University of Connecticut Professor Mark Urban summarized one of his latest publications in the journal Science, and called on scientists to progress from biodiversity explainers to biodiversity forecasters.  Today, as global biodiversity is threatened by climate change, one of our greatest scientific problems has become accurately forecasting the responses of species and ecosystems to climate change. Earlier this month, Urban and his colleagues published a review paper in Science titled “Improving the forecast for biodiversity under climate change”. Many of our current models aimed at predicting species responses to climate change, the authors noted, are missing crucial data that hamper the accuracy and thus the predictive capabilities of these models. What does this mean exactly?

Say we are interested in determining whether current protected areas will continue to benefit the species that exist inside their boundaries over the next century. To do this, we gather basic information about these species: what habitat do they live in, and where will this habitat be located in 100 years? We tally up the number of species currently inhabiting these protected areas, figure out the number of species that will relocate as their preferred habitat shifts (e.g. poleward, or higher in elevation) and then we subtract those species from our count of those who currently exist within the boundaries of this protected area. Voilà, we can now predict that we will lose up to 20% of the species within these protected areas over the next 100 years*.  Now we report our findings to the land managers and environmental groups tasked with conserving these species and we conclude that these protected areas will not be sufficient and they must do more to protect these species. Simple right? It never is.

This predication, like many others, was based on a correlation between these species ranges and climate. So what are we missing? In their review, Urban et al. outline six key factors that are commonly left out of predictive models, and these are: species interactions, dispersal, demography, physiology, evolution and environment (specifically, environment at appropriate spatiotemporal scales) (Figure 1). In fact, they found that more than 75% of models aimed at predicting biological responses to climate change left out these important biological mechanisms. Since my master’s project is centered on species interactions, I will now provide you with a little more information about why this specific mechanism is important, and what we might have overlooked by not including species interactions in the protected area example above.

Figure 1: Six critical biological mechanisms missing from current biodiversity forecasts. Source: Urban et al. 2016
Figure 1: Six critical biological mechanisms missing from current biodiversity forecasts. Source: Urban et al. 2016

I study Adelie and gentoo penguins, two congeneric penguin species whose breeding ranges overlap in a few locations along the Western Antarctic Peninsula. You can read more about my research in previous blog posts like this one. Similar to many other species around the world, both of these penguins are experiencing poleward range shifts due to atmospheric warming. The range of the gentoo penguin is expanding farther south than ever before, while the number of Adelie penguins in these areas is declining rapidly (Figure 2). A correlative model might predict that Adelie penguin populations will continue to decline due to rising temperatures, while gentoo populations will increase. This model doesn’t exactly inform us of the underlying mechanisms behind what we are observing. Are these trends due to habitat shifts? Declines in key prey species? Interspecific competition? If Adelie populations are declining due to increased competition with other krill predators (e.g. gentoo penguins), then any modelling we do to predict future Adelie population trends will certainly need to include this aspect of species interaction.

Figure 2. A subset of the overall range of Adelie and gentoo penguins and their population trends at my study site at Palmer Station 1975-2014. Source: https://www.allaboutbirds.org/on-the-antarctic-peninsula-scientists-witness-a-penguin-revolution/
Figure 2. A subset of the overall range of Adelie and gentoo penguins and their population trends at my study site at Palmer Station 1975-2014. Source: https://www.allaboutbirds.org/on-the-antarctic-peninsula-scientists-witness-a-penguin-revolution/

Range expansion can result in novel or altered species interactions, which ultimately can affect entire ecosystems. Our prediction above that 20% of species within protected areas will be lost due to habitat shifts does not take species interactions into account. While some species may move out of these areas, others may move in. These new species may potentially outcompete those who remain, resulting in a net loss of species larger than originally predicted. Urban et al. outline the type of data needed to improve the accuracy of predictive models. They openly recognize the difficulties of such a task but liken it to the successful, collective effort of climate scientists over the past four decades to improve the predictive capabilities of climate forecasts.

As a passionate naturalist and philosopher, there is no doubt Humboldt would agree with Urban et al.’s conclusion that “ultimately, understanding how nature works will provide innumerable benefits for long-term sustainability and human well-being”. I encourage you to read the review article yourself if you’re interested in more details on Urban et al.’s views of a ‘practical way forward’ in the field of biodiversity forecasting. For a historical and perhaps more romantic account of the study of biodiversity, check out Andrea Wulf’s biography of Alexander von Humboldt, called The Invention of Nature.

 *This is an oversimplified example based off of a study on biodiversity and climate change in U.S. National parks (Burns et al. 2003)

References:

Burns, C. E., Johnston, K. M., & Schmitz, O. J. (2003). Global climate change and mammalian species diversity in US national parks. Proceedings of the National Academy of Sciences, 100(20), 11474-11477.

Urban, M. 14 September 2016. Carbon Brief. Guest post: How data is key to conserving wildlife in a challenging environment. From: https://www.carbonbrief.org/guest-post-data-key-conserving-wildlife-changing-climate (Accessed: 22 September 2016)

Urban, M. C., Bocedi, G., Hendry, A. P., Mihoub, J. B., Pe’er, G., Singer, A., … & Gonzalez, A. (2016). Improving the forecast for biodiversity under climate change. Science, 353(6304), aad8466.

Wulf, A. (2015). The Invention of Nature: Alexander Von Humboldt’s New World. Knopf Publishing Group.