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Biodiversity Changes in Q-Time H. John B. Birks University of Bergen University College London University of Oxford.

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Presentation on theme: "Biodiversity Changes in Q-Time H. John B. Birks University of Bergen University College London University of Oxford."— Presentation transcript:

1 Biodiversity Changes in Q-Time H. John B. Birks University of Bergen University College London University of Oxford

2 Introduction Biodiversity has many meanings – ‘the variety of life’ - simplified here to ‘taxonomic richness’ or ‘number of taxa in a certain region’ Biodiversity deceptively simple to study. In fact frustrating difficult because it varies over spatial, temporal, and taxonomic scales, and its study abounds in unknowns

3 Ecologists recognise many types of diversity community (-diversity) between community (-diversity) landscape (-diversity) between landscapes (-diversity) regions (-diversity) These scales all vary over time Odgaard 2007 Robert Whittaker ( )

4 Three main time-scales in biodiversity research Most ecologists interested in time-scales of days, weeks, months, years, decades, or even centuries – Real-time or Ecological-time Palaeobiologists and palaeoecologists interested in time- scales of hundreds, thousands, and millions of years. Deep-time – pre-Quaternary sediments and fossil record to study evolution and dynamics of past biota over a range of time-scales, typically >10 6 years. Q-time or Quaternary-time – uses tools of paleobiology (fossils, sediments) to study ecological responses to environmental changes at Quaternary time-scales ( years) during the past 2.6 million years. Concentrates on last 50,000 years, the window dateable by radiocarbon- dating. Also called Near-time (last 1-2 million years).

5 Biodiversity textbooks tend to discuss real-time or ecological-time (ecology) and deep-time (palaeobiology) but largely ignore Q-time (palaeoecology) Why? 1.Partly fault of Q-time palaeoecologists or long- term ecologists who present their data as overly complex pollen diagrams 2.Many palaeoecologists do not realise that they can use the palaeoecological record as a natural laboratory to explore biotic responses (including biodiversity) under a range of past conditions. The palaeoecological record as an ecological laboratory. “Coaxing history to conduct experiments” (Deevey 1969)

6 High-resolution palaeoecological records provide unique information on species dynamics and their interactions with environmental change spanning hundreds or thousands of years

7 Pollen reflects broad-scale diversity patterns Trees and shrub pollen richness in relation to elevation from equator to 70N Flenley (1996)

8 Donald Rumsfeld, US Secretary of Defense 12 February 2002 “There are known knowns. There are things we know we know. We also know there are known unknowns; that is to say, we know there are some things we do not know. But there are also unknown unknowns. There are things we do not know we don’t know.” Rumsfeld’s epistemological analysis reveals three classes. Addition of a fourth – unknown knowns – gives us a useful scheme for identifying and addressing sources of uncertainty in biodiversity research in terms of congnisance, ignorance, knowledge, and uncertainty.

9 Cognisance (or awareness) Ignorance KnowledgeKnown knowns Unknown knowns UncertaintyKnown unknowns Unknown unknowns Jackson (2012)

10 Known knowns – more or less solid facts, observations, or inferences based on best available evidence (e.g. richness decreases with elevation) Known unknowns – sources of error and uncertainty. Try to minimise and estimate. Things we know but that we do not know adequately (e.g. sampling uncertainties) Unknown unknowns – represent ignorance, often lead to major scientific surprises (e.g. unimodal peak in richness at mid-elevations). Constantly becoming known. Unknown knowns – things we know so well that we are no longer explicitly aware we know them (e.g. hidden and unquestioned assumptions, old literature now ignored or not available in electronic format – “ignorance creep”).

11 Connor et al. (2012)

12 Derive summary statistics from these pollen- stratigraphical data Taxonomic richness – number of pollen and spore types found if all counts the same size ( or  diversity). Rarefaction analysis Compositional turnover or change between samples ( diversity) evenness

13 Q-Time as a Whole (last 2.6 million years) At least 17 glacial phases, with each glacial- interglacial cycle about 125,000 years Jackson & Overpeck (2000)

14 There is no norm in the Quaternary “There is no norm there” (Gertrude Stein 1937) There is no norm in the Quaternary as we know that climate varies continually at all relevant ecological timescales from inter-annual to multi-millennial There is no modal condition for the past century, past millennium, Holocene, or Quaternary Few terrestrial ecosystems have existed in situ for more than the past 12,000 years: most are considerably younger, some only arising within the past few centuries Environmental and ecological changes are the norm The most natural feature of Planet Earth in Q-time is its continual flux. Expect biodiversity therefore to vary in space and time

15 Quaternary long assumed to be an important time for genetic diversification, speciation, and extinction as a result of major and rapid environmental changes The available Q-time plant record shows No evidence for speciation except for recent ‘microspecies’ Only one global extinction other than those caused by human impact Much evidence for stasis

16 Main feature is species persistence and evolutionary stasis retained by rapid migrations and resulting mixing of gene pools Stasis is interesting because it came about in unlikely circumstances when one would have expected responses of speciation and extinction to be important in response to Milankovitch oscillations (orbital forcing). “ ‘Is there any point to which you would wish to draw my attention?’ ‘To the curious incident of the dog in the night-time.’ ‘The dog did nothing in the night-time.’ ‘That was the curious incident,’ remarked Sherlock Holmes.” Arthur Conan Doyle (Silver Blaze)

17 Dawson et al. (2011) Modes of biotic response to environmental change Very useful framework to view biotic responses

18 Q-Time in last 10,000 Years (Holocene) Crose Mere, central England Birks & Line (1992)

19 Isle of Skye Crofting Blanket bog Birks & Line (1992)

20 Three sites on Azores showing impact of human colonisation in late 15 th century. Rise in richness, fall in endemics, increase in compositional turnover. Major impact following colonisation Connor et al. (2012)

21 What External Drivers can be Influencing Biodiversity Changes in Q-Time?

22 Long-term studies can also identify potentially important indirect drivers such as economic forces in upland systems. Combined pollen analysis from small bogs in livestock farms, documentary data, estate records, and econometric statistical modelling techniques at 11 sites in biogeographical zones in the Scottish uplands. Asked what are the principal drivers of pollen richness (a surrogate for plant diversity) in last 400 years?

23 North-west Scotland Hanley et al. (2008)

24 NW Scotland Central Scotland Hanley et al. (2008) Palynological richness through time

25 ‘Best’ predictor (in a statistical sense) for diversity change is livestock prices, a proxy for grazing pressure.  grazing led to  diversity, as did land abandonment. Intermediate disturbance gives highest diversity. Technological change such as introduction of new breeds, not significant predictor. Unique study that combines natural and social sciences to show that long-term management of Scottish upland areas should focus on grazing pressures as a key driver. Highlights problems of establishing ‘baselines’ or ‘natural’ target levels for biodiversity. See also Hanley et al J Environ Economics & Management 57: 5-20 where economics, palaeoecology, and climate history are combined.

26 Central Swiss Alps – introduction of farming in the Valais ca years ago Colombaroli et al. (2012)

27 Generalised additive model response surface showing richness (z-axis) in relation to charcoal (fire) and silica (erosion) Colombaroli et al. (2012)

28 Ecologists increasingly recognise the importance of habitat and landscape diversity (eco- diversity or eco-complexity) in influencing species diversity within landscapes.

29 Can reconstruct the landscape-scale diversity from long-term ecological data if you have many sites from a small area. Reconstruct past vegetation types first. Smith & Cloutman 1988 Phil Trans R Soc B 322: Waun-Fignen-Felen, Black Mountain Range of S Wales Amazingly detailed study with 13 pollen diagrams and over C dates from a 15 ha bog Black Mountains, S Wales Blanket-mire

30 Smith & Cloutman (1988) Vegetation mosaic through time BP = years before present (AD 1950)

31 Smith & Cloutman (1988)


33 Main vegetation units at Waun-Fignen-Felen area Age x10 3 ( 14 C yrs BP) Dryland Grassland++ Betula/Corylus scrub++++ Corylus woodland+ Mixed woodland+++++ Corylus scrub++ Total Wetland Reed-swamp+++++ Fen carr woodland+++++ Blanket bog+++++ Acid peat bog++ Damp heath+ Total Decrease in landscape () diversity with expansion of blanket peat Important implications for landscape management and habitat restoration, especially oceanic areas. Landscape homogenisation.

34 Major drivers of biodiversity change are linked to human impact and/or soil changes Not surprising as we live in ‘cultural landscapes’ Climate appears to be major driver of biodiversity at times of rapid climate change from, for example, late-glacial to Holocene at 11,700 years ago

35 Pollen richness Chironomid-inferred temperature Pollen turnover in 250 year intervals and changes in chironomid temperatures in same intervals Highest richness in earliest Holocene, decreases with expansion of Betula about yr BP, rises to constant level by yr BP. Maximum richness at ‘intermediate’ temperatures (= productivity) Increase in compositional turnover with rapid climate change Willis et al. 2010

36 Q-time biodiversity research important in scenario planning for management and conservation and in basic ecological research such as determining of biodiversity change and turnover over hundreds or thousands of years.

37 Where are we in terms of cognisance (awareness), ignorance, knowledge, and uncertainty in Q-time biodiversity research? Known knowns – taxonomic richness given the data, but how representative or complete are our data? Unknown knowns – uncertainties in primary data and in richness estimates and biases due to differential pollen representation and, uneven taxonomy, (species, genus, family levels) Known unknowns – ‘ignorance creep’ Assumptions stated in Birks & Line (1992) already ignored! Q-time biodiversity ignored Unknown unknowns – abilities to interpret the observed patterns. Little understanding theory for the spatial and temporal scales of Q-time biodiversity. What are the relationships today between modern vegetation richness and pollen richness? Vivian Felde’s on-going PhD study in Setesdal

38 Acknowledgements Vivian Felde Anne Bjune John-Arvid Grytnes Kathy Willis John Line Pim van der Knaap Hilary Birks Thomas Giesecke

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