Monthly Archives: February 2017

On Biodiversity and Ecosystem Function

I begin with a quote from Seddon et al. (2016):

By 2012, the consensus view based on 20 years of research was that (i) experimental reduction in species richness, at any trophic level, negatively impacts both the magnitude and stability of ecosystem functioning [12,52], and (ii) the impact of biodiversity loss on ecosystem functioning is comparable in magnitude to other major drivers of global change [13,54].”

The references are to Cardinale et al. (2012), Naeem et al. (2012), Hooper et al. (2012), and Tilman et al. (2012).

The basic conclusion of the literature cited here is that with very extensive biodiversity loss, ecosystem function such as primary productivity will be reduced. I first of all wonder which set of ecologists would doubt this. Secondly, I would like to see these papers analysed for problems of data analysis and interpretation. A good project for a graduate class in experimental design and analysis. Many of the studies I suspect are so artificial in design as to be useless for telling us what will really happen as natural biodiversity is lost. At best perhaps we can view them as political ecology to try to convince politicians and the public to do something about the true drivers of the mess, climate change and overpopulation.

Too many of the graphs I see in published papers on biodiversity and ecosystem function look like this (from Maestre et al. (2012): data from 224 global dryland plots)

There is a trend in these data but zero predictability. And even if you feel that showing trends are good enough in ecology, the trend is very weak.

Many of these analyses utilize meta-analysis. I am a critic of the philosophy of meta-analysis and not alone in wondering how useful many of these are in guiding ecological research (Vetter et al. 2013, Koricheva, and Gurevitch 2014). Perhaps the strongest division in deciding the utility of these meta-analyses is whether one is interested in general trends across ecosystems or predictability which depends largely on understanding the mechanisms behind particular trends.

Another interesting aspect of many of these analyses lies in the preoccupation with stability as a critical ecosystem function maintained by species richness. In contrast to this belief, Jacquet et al. (2016) have argued that in empirical food webs there is no simple relationship between species richness and stability, contrary to conventional theory.

Finally, another quotation from Naeem et al. (2012) which raises a critical issue on which ecologists need to focus more:

“In much of experimental ecological research, nature is seen as the complex, species-rich reference against which treatment effects are measured. In contrast, biodiversity and ecosystem functioning experiments often simply compare replicate ecosystems that differ in biodiversity, without any replicate serving as a reference to nature. Consequently, it has often been difficult to evaluate the external validity of biodiversity and ecosystem functioning research, or how its findings map onto the “real” worlds of conservation and decision making. Put another way, what light can be shed on the stewardship of nature by microbial microcosms that have no analogs in nature, or by experimental grassland studies in which some plots have, by design, no grass species? “ (page 1403)

And for those of you who are animal ecologists, the vast bulk of these studies were done on plants with none of the vertebrate browsers and grazers present. Perhaps some problems here.

Whatever one’s view of these research paradigms, no questions will be answered if we lose too much biodiversity.

Cardinale, B.J., Duffy, J.E., Gonzalez, A., Hooper, D.U., Perrings, C., Venail, P., Narwani, A., Mace, G.M., Tilman, D., Wardle, D.A., Kinzig, A.P., Daily, G.C., Loreau, M., Grace, J.B., Larigauderie, A., Srivastava, D.S. & Naeem, S. (2012) Biodiversity loss and its impact on humanity. Nature, 486, 59-67. doi: 10.1038/nature11148

Hooper, D.U., Adair, E.C., Cardinale, B.J., Byrnes, J.E.K., Hungate, B.A., Matulich, K.L., Gonzalez, A., Duffy, J.E., Gamfeldt, L. & O/’Connor, M.I. (2012) A global synthesis reveals biodiversity loss as a major driver of ecosystem change. Nature, 486, 105-108. doi: 10.1038/nature11118

Jacquet, C., Moritz, C., Morissette, L., Legagneux, P., Massol, F., Archambault, P. & Gravel, D. (2016) No complexity–stability relationship in empirical ecosystems. Nature Communications, 7, 12573. doi: 10.1038/ncomms12573

Koricheva, J. & Gurevitch, J. (2014) Uses and misuses of meta-analysis in plant ecology. Journal of Ecology, 102, 828-844. doi: 10.1111/1365-2745.12224

Maestre, F.T. et al. (2012) Plant species richness and ecosystem multifunctionality in global drylands. Science, 335, 214-218. doi: 10.1126/science.1215442

Naeem, S., Duffy, J.E. & Zavaleta, E. (2012) The functions of biological diversity in an Age of Extinction. Science, 336, 1401.

Seddon, N., Mace, G.M., Naeem, S., Tobias, J.A., Pigot, A.L., Cavanagh, R., Mouillot, D., Vause, J. & Walpole, M. (2016) Biodiversity in the Anthropocene: prospects and policy. Proceedings of the Royal Society B: Biological Sciences, 283, 20162094. doi: 10.1098/rspb.2016.2094

Tilman, D., Reich, P.B. & Isbell, F. (2012) Biodiversity impacts ecosystem productivity as much as resources, disturbance, or herbivory. Proceedings of the National Academy of Sciences 109, 10394-10397. doi: 10.1073/pnas.1208240109

Vetter, D., Rücker, G. & Storch, I. (2013) Meta-analysis: A need for well-defined usage in ecology and conservation biology. Ecosphere, 4, art74. doi: 10.1890/ES13-00062.1

On Ecological Predictions

The gold standard of ecological studies is the understanding of a particular ecological issue or system and the ability to predict the operation of that system in the future. A simple example is the masting of trees (Pearse et al. 2016). Mast seeding is synchronous and highly variable seed production among years by a population of perennial plants. One ecological question is what environmental drivers cause these masting years and what factors can be used to predict mast years. Weather cues and plant resource states presumably interact to determine mast years. The question I wish to raise here, given this widely observed natural history event, is how good our predictive models can be on a spatial and temporal scale.

On a spatial scale masting events can be widespread or localized, and this provides some cues to the important weather variables that might be important. Assuming we can derive weather models for prediction, we face two often unknown constraints – space and time. If we can derive a weather model for trees in New Zealand, will it also apply to trees in Australia or California? Or on a more constrained geographical view, if it applied on the South Island of New Zealand will it also apply on the North Island? At the other extreme, must we derive models for every population of particular plants in different areas, so that predictability is spatially limited? We hope not and work on the assumption of more spatial generality than what we can measure on our particular small study areas.

The temporal stability of our explanations is now particularly worrisome because of climate change. If we have a good model of masting for a particular tree species in 2017, will it still be working in 2030, 2050 or 2100? A physicist would never ask such a question since a “scientific law” is independent of time. But biology in general and ecology in particular is not time independent both because of evolution and now in particular because of changing climate. But we have not faced up to whether or not we must check our “ecological laws” over and over again as the environment changes, and if we have to do this what must the time scale of rechecking be? Perhaps this question can be answered by determining the speed of potential evolutionary change in species groups. If virus diseases can evolve quickly in terms of months or years, we must be eternally vigilant to consider if the flu virus of 2017 is going to be the same as that of 2016. We should not stop virus research and say that we have sorted out some universal model that will become an equivalent of the laws of physics.

The consequences of these simple observations are not simple. One consequence is the implication that monitoring is an essential ecological activity. But in most ecological funding agencies monitoring is thought to be unscientific, not leading to progress, and more stamp collecting. So we have to establish that, like the Weather Bureau every country supports, we must have an equivalent ecological monitoring bureau. We do have these bureaus for some ecological systems that make money, like marine fisheries, but most other ecosystems are left in limbo with little or no funding on the generalized assumption that “mother or father nature will take care of itself” or expressed more elegantly by a cabinet minister who must be nameless, “there is no need for more forestry research, as we know everything we need to know already”. The urge by politicians to cut research funding lives too much in environmental research.

But ecologists are not just ‘stamp collectors’ as some might think. We need to develop generality but at a time scale and a spatial scale that is reliable and useful for the resolution of the problem that gave rise to the research. Typically for ecological issues this time scale would be 10-25 years, and a rule of thumb might be for 10 generations of the organisms being studied. For many of our questions an annual scale might be most useful, but for long-lived plants and animals we must be thinking of decades or even centuries. Some practical examples from Pacifici et al. (2013): If you study field voles (Microtus spp.) typically you can complete your studies of 10 generations in 3.5 years (on average). If you study red squirrels (Tamiasciurus hudsonicus), the same 10 generations will cost you 39 years, and if red foxes (Vulpes vulpes) 58 years. If wildebeest (Connochaetes taurinus) in the Serengeti, 10 generations will take you 80 years, and if you prefer red kangaroos (Macropus rufus) it will take about 90 years. All these estimates are very approximate but they give you an idea of what the time scale of a long-term study might be. Except for the rodent example, all these study durations are nearly impossible to achieve, and the question for ecologists is this: Should we be concerned about these time scales, or should we scale everything to the human research time scale?

The spatial scale has expanded greatly for ecologists with the advent of radio transmitters and the possibility of satellite tracking. These technological advances allow many conservation questions regarding bird migration to be investigated (e.g. Oppel et al. 2015). But no matter what the spatial scale of interest in a research or management program, variation among individuals and sites must be analyzed by means of the replication of measurements or manipulations at several sites. The spatial scale is dictated by the question under investigation, and the issue of fragmentation has focused attention on the importance of spatial movements both for ecological and evolutionary questions (Betts et al. 2014).

And the major question remains: can we construct an adequate theory of ecology from a series of short-term, small area or small container studies?

Betts, M.G., Fahrig, L., Hadley, A.S., Halstead, K.E., Bowman, J., Robinson, W.D., Wiens, J.A. & Lindenmayer, D.B. (2014) A species-centered approach for uncovering generalities in organism responses to habitat loss and fragmentation. Ecography, 37, 517-527. doi: 10.1111/ecog.00740

Oppel, S., Dobrev, V., Arkumarev, V., Saravia, V., Bounas, A., Kret, E., Velevski, M., Stoychev, S. & Nikolov, S.C. (2015) High juvenile mortality during migration in a declining population of a long-distance migratory raptor. Ibis, 157, 545-557. doi: 10.1111/ibi.12258

Pacifici, M., Santini, L., Di Marco, M., Baisero, D., Francucci, L., Grottolo Marasini, G., Visconti, P. & Rondinini, C. (2013) Database on generation length of mammals. Nature Conservation, 5, 87-94. doi: 10.3897/natureconservation.5.5734

Pearse, I.S., Koenig, W.D. & Kelly, D. (2016) Mechanisms of mast seeding: resources, weather, cues, and selection. New Phytologist, 212 (3), 546-562. doi: 10.1111/nph.14114