Biological traits

Biological traits analysis is a valuable tool for measuring ecosystem function. Based on the characteristics of individual organisms, it is also a powerful way to measure ecosystem health and vulnerability to human activities.

The importance of species and their function

Until recently, decreases in biodiversity were simply seen as loss of species. Now, there is a shift towards the concept of functional diversity - the number, type and distribution of functions performed by an ecosystem’s organisms - as a complement to species diversity. If an ecosystem has two or more species performing the same function, the loss of one might not have an effect. However, if all the species performing that function are lost, the function itself is lost. This is often followed by a loss of ecosystem services. For example, the switch from coral- to algal-dominated reefs in Jamaica in the 1980s was likely caused by a drop in the number of herbivorous fish (fishing) followed by the mass deaths of herbivorous sea urchins.
Organisms living in certain areas can create places for other organisms to:

What are biological traits and how can we use them

Biological traits are characteristics of an organism that affect how it interacts with its environment and if it will be susceptible to a particular stressor. Traits can be important for defining response to stressors, recovery from stressors and/or effects on ecosystem functioning. They can include adaptations related to feeding habit, behaviour and life-history. For marine benthic species, traits that can be used to assess sensitivity to stress include: motility, living position, feeding mode, growth form above the sediment surface, fragility, and size.

Generally-used traits

Table 1: Biological trait categories can be related to ecosystem effect, response to stressors and recovery.

Categories

Effect

Response

Recovery

Adult size (e.g., <2mm)

y

y

y

Adult-juvenile interactions (e.g., positive)

y

 

y

Adult longevity (e.g., 25 – 50 yrs)

y

 

y

Reproductive frequency (e.g., semi-continuous)

 

 

y

Developmental type (e.g., planktonic)

 

 

y

Environmental position (e.g., >5cm below sediment surface, surface etc)

y

y

 

Living habit (e.g., encrusting, foliose, open burrows, etc)

y

y

 

Feeding habit (e.g., suspension feeder)

y

y

 

Juvenile post-settlement movement/method (e.g., swimmer)

y

y

y

Adult movement/method (e.g., crawler)

y

y

y

Sediment and solute transport (e.g., diffusive mixing)

y

 

 

Sediment stabilisation (e.g., armours bed)

y

 

 

Structural Fragility (e.g.,flexible)

 

y

 

How human activity impacts the seafloor habitats

Globally, soft seafloor habitats are seriously affected by human activities like pollution, mining, dredging and trawling. The biological traits of organisms – e.g. having a large, fragile body and limited mobility, or feeding on algae in the water - can make them sensitive to specific human activities that often happen in the same locations and stress organisms living there in a variety of ways. The complex, species-rich ecosystems also contribute substantially to how the greater ecosystem functions, which means they are important to the goods and services that marine ecosystems provide humans. It is also crucial to highlight the importance of maintaining habitat variety to support ecosystem functioning. For example, a study compared two locations in New Zealand likely to have different species pools: Northern Hauraki Gulf and Tonga Island Marine Reserve. It found that both locations had similar biological traits, even though the two sites only had 112 of the 374 observed taxa in common. The study found differences in the specific traits important for ecosystem function across the habitats studied. To learn more, read the study on "Predicting the effect of habitat homogenization on marine biodiversity"

A technique to study the effects of multiple stressors

Our multiple use of the natural resources creates multiple stressors that frequently interact. Predicting the effects of stressors is not a simple matter of adding up single effects. In fact, the more stressors, the more it is likely that some form of threshold response will occur. Conducting experiments to investigate responses to multiple stressors in real ecosystems is difficult and expensive. Furthermore, the inherent heterogeneity of seafloor ecosystems means the diversity of responses captured by a field experiment may only represent a small subset of the habitats or communities actually being impacted.To reduce the cost, we have developed a technique based on biological traits that allows us to predict non-linear effects for a number of stressors using knowledge of how various species react to a specific stressor. This technique can use pre-existing information on organims that live on the seafloor in a specific place. 

Making predictions

For a stressor of interest (e.g. benthic trawling disturbance), we can predict which traits will be sensitive, as well as the magnitude of sensitivity, and assign scores. For example, we know that benthic trawling physically disturbs the seabed, therefore organisms that protrude from the sediment and are sedentary will be most affected (highest sensitivity), whereas organisms living deeper in the sediment profile will be less affected (low-medium sensitivity), and organisms that are mobile and can escape, and that might utilise increased food availability after the disturbance may be unaffected (neutral) or may benefit (positive sensitivity) (Figure 1).

  • If we accumulate the responses of the individual species to a single stressor, do we end up with a non-linear response to the stressor?
  • If we simply summed the responses to three different stressors, would we end up with non-linear responses that are greater than the sum of the responses?

We trialled this for three common stressors in coastal and oceanic zones (extraction of species or sediment, sedimentation and suspended sediment) and looked at the response of total abundance and the number of species living in and on the seafloor. 

Figure 1: Decision tree used to assess sensitivity of taxa to stress associated with benthic trawling disturbance (from Hewitt et al., 2018). 

Figure 2. Non-linear ecosystem responses to increasing stressor scenarios when three known stressors occur individually and in combination. Continuous lines are responses to a single stressor, dashed lines are responses to a combination of all three stressors. We observed non-linear responses of total abundances and number of taxa to individual stressors. When stressors were combined, we again observed non-linear responses, with thresholds occurring prior to those observed for single stressors.

 

 

Using biological traits to define sensitive habitats

Biological trait analysis has a long history of use, mainly in terms of allowing comparisons across areas as demonstrated above, and more recently using “effect” traits to explain the importance of biodiversity loss. That is, using BTA to assess not only whether an impact occurs but also the relative importance of that impact to ecosystem functioning (Oug et al. 2012).  But the biological traits of organisms – e.g. having a large, fragile body and limited mobility, or feeding on algae in the water - also can predispose them to be susceptible to a particular stressor, for example organisms that feed by filtering out algae from the water will be susceptible to increases in suspended sediment in the water resulting from dredging, land run off, and bottom trawling.

These “response” traits can be used within a risk assessment or conservation prioritisation framework to assess the sensitivity of habitats or areas to a planned activity.  If we can derive sensitivity of species from biological traits this would mean that we could then calculate the sensitivity of areas where it would be difficult to conduct experiments due to for example depth, risk to gear and human life- as long as samples from surveys were available. This could also allow us to assess sensitivity at scales larger than the normal scale of experiments.

In order to do this we need two things:

  1. A technique to categorise species sensitivity to specific stressors
  2. methods to accumulate up from a species to all of the species living in an area

 

Categorise species sensitivity to specific stressors

Research and other knowledge can be amalgamated to select relevant “response” traits. For example, Pearson & Rosenberg (1978) characterised different stages of organic enrichment in marine systems based on sediment dwelling depth, burrowing activity, body size, mobility, and life span.  We have used literature searches and expert opinion to select the response traits for three important stressors (sediment extraction, sedimentation and suspended sediment).  We then converted these traits into decision trees for each stressor that would categorise a species to one of 5 categories from most to least sensitive.Table 1: Response traits for sediment extraction, sedimentation and suspended sediment.

 

 

Table 2: Response traits for sediment extraction, sedimentation and suspended sediment.

Trait category

Expression

Adult movement

*Sedentary, *Crawler, *Slow burrower, *Fast burrower

Living position

*Middle (between 2-5 cm), *Top (in top 2 cm), *Epibenthic, *Bentho-pelagic

Feeding mode

*Suspension/filter, *Sub-surface deposit, *Surface deposit, *Predator, *Scavenger, *Herbivore, Miner/Borer, *Photo synthesisers

Growth form above surface

*None, *Tubes, *Branching, *Erect, *Encrusting

Fragility

*Highly breakable, *Limited flexibility, *Extremely flexible

Size

*0-10 mm, *11-20 mm

 

Figure 3: Decision trees used to assign a taxon’s level of sensitivity, based on its biological traits, to (a) an extraction method penetrating to 5 cm deep, (b) sedimentation and (c) suspended sediment. Solid lines follow an answer “yes” to the trait expression question, dashed lines follow “no” answers.  

Accumulate from a single species to all of the species living in an area

Two main methods have been proposed for this- weighted abundance and abundance of sensitive species. We recommend adding a third method - number of highly sensitive species - because species richness (number of species) has been widely accepted as an indicator of resilience.

  • Number of highly sensitive species is exactly that- the number of species that are categorised as “most sensitive”
  • Abundance of sensitive species sums the abundance of all species that are categorised as “most sensitive”
  • Weighted abundance begins by summing the abundance of all species in each category and then multiplying that abundance by a weighting for the category with the most sensitive category having the greatest weight.

 

We validated this technique on video transect data collected from a relatively pristine region of the seafloor.  Specifically, we looked at whether our predictions of sensitivity were different for each stressor. Regardless of accumulation method we were able to distinguish differences in sensitivity at a site to different stressors.  

 

Investigating multiple stressor effects on seafloor communities using biological traits analysis 

We live in a world where our natural environment is the focus of multiple uses- creating multiple stressors. These multiple stressors frequently interact and as a result predicting their effects is not a simple matter of adding up the single effects.  Indeed, the more stressors the more likelihood that some form of threshold response will occur. Not surprisingly, the ability to detect and predict if, or when, thresholds in ecosystem response to stressors occur is in high demand.

However, conducting experiments investigating responses to multiple stressors in real world ecosystems is difficult and expensive (particularly in the marine environment). Furthermore, the inherent heterogeneity of seafloor ecosystems means that the diversity of responses captured by a field experiment may only represent a small subset of the habitats or communities actually being impacted. 

We are developing a simple, relatively cheap technique to predict non-linear effects for combinations of stressors using knowledge of how species sensitivities to a specific stressor is driven by their biological traits.  This technique can use pre-existing information of the plants and animals that live on the seafloor in a specific place. 

As we state above, traits can be important for defining response to stressors, recovery from stressors and/or effects on ecosystem functioning. For marine benthic species, traits that can be used to assess sensitivity to stress include motility, living position, feeding mode, growth form above the sediment surface, fragility, and size.

We wondered whether if we accumulated over the responses of the individual species to a whole community  we would end up with a non-linear response to the stressor. And then if we simply summed across the responses to three different stressors, whether we would end up with non-linear responses that were greater than the sum of the responses. We trialled this for the three common stressors in coastal and oceanic zones discussed above- extraction of species or sediment, sedimentation, and suspended sediment- and looked at the response of total abundance and the number of species living in and on the seafloor.  

 

References

Bremner, J., Rogers, S.I., Frid, C.L.J., 2003. Assessing functional diversity in marine benthic ecosystems: a comparison of approaches. Marine Ecology Progress Series 254, 11-25.

Hewitt, J. E., C. J. Lundquist, and J. Ellis. 2018. Assessing sensitivities of marine areas to stressors based on biological traits. Conservation Biology 33:142-151. Doi:10.1111/cobi.13181

Hewitt, J.E., Thrush, S.F., Dayton, P.D., 2008. Habitat variation, species diversity and ecological functioning in a marine system. Journal of Experimental Marine Biology and Ecology 366, 116-122.

Hewitt, J.E., Julian, K., Bone, E.K., 2011. Chatham-Challenger Ocean Survey 20/20 Post-Voyage Analyses: Objective 10 – Biotic habitats and their sensitivity to physical disturbances. New Zealand Aquatic Environment and Biodiversity Report No. 81.

Micheli, F., Halpern, B.S., 2005. Low functional redundancy in coastal marine assemblages. Ecology Letters 8, 391-400.

Oug E, Fleddum A, Rygg B and Olsgard F (2012). Biological Traits Analyses in the Study of Pollution Gradients and Ecological Functioning of Marine Soft Bottom Species Assemblages in a Fjord Ecosystem. Journal of Experimental Marine Biology and Ecology, 432 94-105.

Pearson T H and Rosenberg R (1978). Macrobenthic Succession in Relation to Organic Enrichment and Pollution of the Marine Environment. Oceanography and Marine Biology: an Annual Review, 16 229-311.

Petchey, O.L., Gaston, K.J., 2006. Functional diversity: back to basics and looking forward. Ecology Letters 9, 741-758.

Thrush, S.F., Gray, J.S., Hewitt, J.E., Ugland, K.I., 2006. Predicting the effects of habitat homogenization on marine biodiversity. Ecological Applications 16 (5), 1636-1642.

 

Contact

Strategy Manager - Coasts & Estuaries
Principal Scientist - Marine Ecology
Figure 1: example of how horse mussels (Atrina zelandica) can alter the structure of a muddy habitat by providing a hard surface for other organisms to grow on. [NIWA]
Figure 2: reworked sediment from the burrowing activity of heart urchins (Echinocardium cordatum). [Drew Lohrer, NIWA]
Figure 3: seafloor chamber with a stirrer motor and associated water intake / output (top and bottom), and an oxygen meter (left). [Drew Lohrer, NIWA]
Figure 4: non-metric MDS plot showing that trait differences between habitats (represented by different shapes) within a location are greater than the differences between two locations (filled vs non-filled). [Hewitt et al, 2008]

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