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Advantages of sexual reproduction

In any case, the advantages of sexual reproduction must be considerable to outweigh the obvious disadvantage of breaking up the successful genotypes of parents and grandparents that survived long enough to reproduce. These advantages accrue to the population as a whole, however, while many evolutionary biologists insist that selection pressures are exerted only on individuals. Perhaps that need not be a rigid rule.

At a recent Santa Fe Institute meeting, John Maynard Smith, who teaches at the University of Sussex, was commenting on this issue, when Brian Arthur, chairing the session, recalled the occasion when they first met. Both men have a background in engineering. Maynard Smith became an aircraft designer and then took up evolutionary biology, to which he has made some remarkable contributions. Brian, who grew up in Belfast, went into operations research and then economics, becoming a professor at Stanford and the founding director of the economics program at the Santa Fe Institute. The first encountered each other at a scienfice meeting in Sweden, where Maynard Smith remarked in the course of a lecture that while sex had obvious advantages for a polulation, it was not clear what it did for the individual. Brian called out from the audience, “What a very English view of sex!” Maynard Smithm without missin a beat, replied, “I gather from your accent that you’re Irish. Well, in England at least we have sex.”

Inclusive fitness and the selfish gene

A further complication in utilizing the concept of fitness arizes in higher organsisms that make use of sexual reproduction. Each such organism conveys only half its genes to a given offspring, while the remaining half derive from the other parent. The offspring are not clones, but merely close relatives. And the organism has other close relatives, the survival of which can also contribute to the propagation of genes similar to its own. Thus biologists have developed the notion of “inclusive fitness”, which takes account of the extent to which relatives of a given organism survive to reproduce, weighted according to the closeness of the relationship. (Of course inclusive fitness also takes account of the sirvival of the organism itself.) Evolution should have a general tendency to favor genotypes exhibiting high inclusive fitness, especially through inherited patterns of behavior that promote the survival of an organism and its close relatives. Tha tendency is called “kin selection”, and it fits nicely with a picture of evolution in which organisms are merely devices “used” by genes to propagate themselves. That point of view has been popularized under the name of the “selfish gene.”

Recall that effective complexity is the length of a concise description of the regularities of a system.

Recall that effective complexity is the length of a concise description of the regularities of a system. Some of those regularities can be traced back to the fundamental physical laws governing the universe. Others arise from the fact that many characteristics of a given part of the universe at a given time are related to one another through their common origin in some past incident. Those characteristics have features in common; they exhibit mutual information. For example, automobiles of a given model resemble one another because they all originate from the same design, which contains many arbitrary features that could have been chosen differently. Such “frozen accidents” can make themselves felt in all sort of ways. Looking at coins of King Henry VIII of England, we may reflect upon all the references to him not only on coins but in charters, in documents relating to the seizure of abbeys, and in history books and how those would all be different if his elder brother Arthur had survived to mount the throne instead of him. All those references depend on the same frozen accident.

If the interference between each pair of coarse-grained histories is zero...

If the interference between each pair of coarse grained histories is zero, either exactly or to an exceedingly good approximation, then all the coarse grained histories are said to decohere. The quantity $D$ of each coarse-grained history and itself is then a true probability, with the additive property. In practice, quantum mechanics is always applied to sets of decohering coarse-grained histories, and that is why it is able to predict probabilities. ($D$, by the way, is called the decoherence functional; the wod “functional” indicates that it depends on histories.)

The algorithmic information content of each alternative history of the universe...

The algorithmic information content of each alternative history of the universe evedently receives a tiny contribution from the simple fundamental laws, along with a gigantic contribution from all the quantum accidents that arise along the way. But it is not only the AIC of the universe that is dominated by those accidents. Although they are chance events, their effects contribute heavily to effective complexity as well.

The effective complexity of the universe is the length of a concise description of its regularities. Like the algorithmic information content, the effective complexity receives only a small contribution from the fundamental laws. The rest comes from the numerous regularities resulting from “frozen accidents.” Those are chance events of which the particular outcomes have a multiplicity of long-term consequences, all related by their common ancestry.

Limerick on the speed of light

There was a young lady named Bright
Who could travel much faster than light.
She set out one day, in a relative way,
And returned home the previous night.

Extinction risk depends strongly on factors contributing to stochasticity

Extinction depends on four kinds of stochasticity. Models that do not incorporate all four kinds might greatly undererstimate extinction risks.

General situation

Four categories of stochasticity:

• demographic stochasticity: birth and death at the level of individuals (stochasticity underlying equal production rates)
• environmental stochasticity: variation in population-level birth and death rates among times or locations
• the sex of individuals

• demographic heterogeneity: variation in vital rates among individuals within a population (e.g. individuals of different size might have different mating chances/reproduction rates)

• often demographic stochasticity dominates environmental stochasticity (see Southern fulmar example in George’s paper)
• the first stochastic models showed that populations could become extinct even if deterministic models concluded they would persist indefinitely
• long term growth rate of stochastic population differs from deterministic population
• in small populations demographic stochasticity becomes more important because of potential dramatic effects in individual level that would cancel out in larger populations
• in large populations environmental stochasticity might be dominant because the whole population is affected at once
• strictly speaking: stochastic isex determination $\subset$ demographic heterogeneity $\subset$ demographic stochasticity
• most current (as of 2008) models of extinction risk include only females
  • but: stochastic sex ratio can increase variation in population growth above the demographic effects of femals alone
  • males contribute to density regulation; lack of males reduces female mating success
• demographic variance $\supset$ demographic stochasticity $+$ sex ratio stochasticity $+$ demographic heterogeneity
• demographic heterogeneity can affect demogrpahic variance in either direction and thus increase or decrease the extinction risk

Modeling

• usually deterministic skeleton (often Ricker model) plus noise
• here: stochastic Ricker model, all four stochastic categories directly involved (no determinstic plus noise, but immediately stochastic)
  • compare: exponential lifetime distribution nearly impossible to model by determinstic mean plus a stochastic noise
• variance of individuals in next generation increases with additional stochasticity sources
• classical Poisson-Ricker model (pure demographic stochasticity) has lowest variance: great potential for underestimation of extinction risk
• if total variance is held constant:
  • environmental stochasticity: maximum variance in number of individuals in next generation as in the deterministic case (equilibrium), variance parameter is density independent
  • demographic stochasticity: maximum variance further left, at lower population size; higher risk of extiction; variance parameter density dependent
• not only increases the variance of number of individuals in the next generation with increasing stochasticity, the point of maximum variance moves further left and sets smaller populations at even higher risk of extinction
• higher reproduction rate $R$ also increases contribution of nonlinear dynamics to variance in population fluctuations which has a negative effect on survival (if $R$ greater than Ricker bifurcation point, even drastically)

Incubation experiment

• model with all four sources of stochasticity fitted best
• classical model with only demographic stochasticity fitted worst
• demographic heterogeneity dominated environmental stochasticity
  • models without demographic heterogeneity absorb it in the variance parameter of environmental stochasticity and attribute the effect wrongly

Results

• species currently regarded at risk from environmental stochasticity might be at much higher (undetected) risk from demographic stochasticity
• specifically important for small populations

Using heterogeneity indices to adjust basal area – Leaf area index relationship in managed coniferous stands

Abstract (excerpt)

The structure of contemporary managed forests is complex and deviates from experimental forests which are usually even-aged monocultures and single-storied. To apply theoretical growth and yield functions on managed forests, adjustments are required, especially for leaf area index (LAI) which is a key biophysical variable in process-based growth models. To asses this, the performance of canopy LAI in modelling the basal area (BA, measured at breast height = 1.30m) of managed boreal forests dominated by Norway spruce (Picea abies (L). Karst) and Scots pine (Pinus sylvestris L.) was investigated by heterogeneity analysis. The study was based on the assumption that canopy LAI and BA are strongly related and are vital for estimating stand productivity and growth. Managed forests were represented by field data from the 2016 and 2017 Swedish National Forest Inventory (NFI) campaigns.

• including ground-based stand structural heterogeneity (SSH, described by species decomposition, coefficient of tree diameter variation, tree social status, height-diameter ratio) improved LAI-BA and BA-LAI models
• including spectral hereogeneity (SPH, described by begetation and textural indices) improved BA-LAI fit as well, suggesting potential use of Sentinel-2 data (Google Earth Engine) in future grwoth models

1. Introduction

• stem growth per unit of absorbed light positively related to per unit grwoth of tree canopy LAI
• LAI: half of the total surface area of green leaves or needles per unit of ground horizontal surface area
• PAI: LAI + other light blocking elements (e.g. twigs, branches, stems)

In recent years, mixed-species forests have been recognised and a number of studies relating mixture effects on productivity (Pretzsch et al., 2015; Pretzsch and Schuetze, 2016), provision of multiple ecosystem services (Gamfeldt et al., 2013; Forrester and Pretzsch, 2015; Felton et al., 2016) and as an adaptation strategy to cope with climate change (Bolte et al., 2009; Kolstrom et al., 2011) have been published.

• lack of growth models resulted in theoretical growth functions, usually suited for even-aged, single-storied monocultures
• adaptions needed when applying LAI-conversion functions to managed stands

2. Materials and methods

Sweden’s forest covers about 28 million hectares representing 69% of the total land area of the country and it is characterized by a north–south gradient (from 55° N to 69° N) with temperate to boreal vegetation zones, respectively (Skogsdata, 2018). Productive forestland covers about 23.5 million hectares with coniferous species largely dominant (Pinus sylvestris L., 39% and Picea abies (L). Karst, 27%). Birch species (Betula pendula and Betula pubescens) also accounts for 12% of the total productive forest area. On the other hand, the share of the area by mixed-species forests have also increased with 14% and 8% of mixed conifers and mixed conifer/broadleaves on productive forest lands, respectively (Fridman and Westerlund, 2016).

• data restrictions: dbh $\geq 10$ cm, tree height $\geq 4$ m, forest age $\leq 100$ yr
• table with data from NFI (2016–2017):
  • basal area (m^2 / ha)
  • tree height (m)
  • stand age (yr)
  • stem density (trees / ha)
  • species proportion (% of basal area, % of stem number)
  • H-D ratio
  • coefficient of tree diameter variation (CVD)
  • tree social status
  • …
• stand structural variables assumed to influence relationship between measured BA and modelled LAI:
  • species composition and dominance
  • tree size heterogeneity
  • stem density
  • tree height
  • stand age
  • variables describing site characteristics: latitudinal gradient, temperature sum, humidity
• tree dominance: $\geq 65$% or $\geq 70$% proportion in stems or BA (not respectivelty but alternatively)
• site heterogeneity:
  • temperature sum: sum of average daily mean temperature exceeding 5$^{\circ}$C during vegetation period
  • humidity: difference between precipitation and loss of water by transpiration and evaporation during growing season period where temperature exceeds 5$^{\circ}$C
• BA(LAI) is Michaelis-Menten type relation, here LAI is the resource and BA the production variable

3. Results

For both species, general stand structural variables such as tree height, stand age, stem density and heterogeneity variables by CVD, tree social status and H-D ratio were the most significant variables in the final models. For Scots pine, the addition of temperature sum was also significant to the final model.

4. Discussion

Many literature references are mentioned here with classical relationships between LAI and stem diameter and so on. Probably all of those relationships hold in our model by construction.

Recent surveys from the Swedish NFI reports that about 20% of the productive forest-land is a mixture of Scots pine and Norway spruce (Fridman et al., 2014; Holmström et al., 2018). Similarly, multi-layered Scots pine stands with understory layer of Norway spruce and birch created by mass regeneration and size stratification have been observed in central and northern Sweden (Lundqvist et al., 2019).

For Norway spruce, the LAI was higher in the monocultures (100% of the total BA) than in the mixtures because of the lower tree heights observed in the mixtures (Fig. 1E).

Scots pine is a shade intolerant species and therefore, exhibits dominance in mixtures (Goude et al., 2019).

All the size heterogeneity variables had negative coefficients (Table 3) and this supports earlier reports of the negative correlation between tree size inequality and stand productivity (Zeller et al., 2018; Sun et al., 2018).

Species-specific sizes and crown allometry of dominant (larger) trees show greater light interception and light-use efficiency than for smaller trees in heterogenous stands (Pretzsch, 2009; Gspaltl et al., 2013; Binkley et al., 2013a).

Personal conclusions

We can use data from NFI (Table 1) for to initialize stands and to compare later stages of our stands with. In any case we would need to come up with temperature sums and humidities for our stands.

We can use provided formulas to compute stand characteristics of our modeled stands:

• species composition: Eq. (1)
• species dominance: Eqs. (2) and (3)
• tree size heterogeneity: Eqs. (4)–(6)

Moving beyond the incorrect but useful paradigm: reevaluating big-leaf and multilayer plant canopies to model biosphere-atmosphere fluxes – a review

Abstract

This article reviews the scientific debate of big-leaf vs multi-layer canopy modeling for land surface models. Comparisons with flux tower measurements show supremacy of good multi-layer models (5-10 layer suffice).

• big leaf: one-layer canopy

Important vertically resolved quantities:

• canopy air temperature
• specific humidity
• wind speed
• water leaf potential (for dry soils)

1. Introduction

George Box’s often quoted statement that “all models are wrong but some are useful” was part of a discussion regarding the process of building scientific models (Box, 1979). He argued for parsimonious models because “simplicity illuminates, and complication obscures” and because “indiscriminate model elaboration is in any case not a practical option because this road is endless”.

• dual source canopy: one leaf layer, separate fluxes for leaves and soil
• wind speed decreases with canopy depth
• relatively dense forests: daytime air temperature varies, mod-canopy maximum a few degrees warmer than in understort
• vertical profiles in leaf water potential can create water-stressed leaves in upper canopy with reduced stomatal conductance and photosynthesis
• heterogeneity in leaf temperature
• CLM5: one canopy layer but > 20 soil layers (despite poorly known necessary vertical thermal and hydraulic parameters)
• Raupach (1991):
  • canopy-atmosphere models (CAM, multi-layer): locally useful
  • simplified canopy-atmosphere models (SCAM): globally useful
• profound differences in microclimate between overstory and understory: important for ecological impact studies
• today:
  • better numerical solutions, CAM might also globally be useful
  • better surface flux modeling

2. Background

• multi-layer models compute generally: radiative transfer, stomatal conductance, leaf energy fluxes,turbulent diffusion; coupled to temperature and water vapor concentration, CO$_2$ concentration at each layer
• big leaf: Penman-Monteith equation for evaporation, atmosphere governed by
  • $R^{‘}_n$: net energy availabe to canopy after accounting for soil and biomass heat storage
  • $\delta^{‘}$: saturation deficient at some height above canopy
  • $g_a$: buld aerodynamic conductance for scalar transport (assumed equal for heat and water vapor, only for convenience)
  • $g_c$: bulk stomatal conductance (canopy conductance)
• relationships between multi-layer and single-layer parameters not uniquely determined
• physical meaning of Penman-Monteith equation gets lost if accumulated over several layers, in particular $g_c$ is impossible to be precisely specified
• leaf nitrogen gradient in canopy less steep than light gradient
• both types of models can be separated into sunlit and shaded leaves
• also leaf inclination angle and leave age cohorts added later to multi-layer models
• green leaf area index and brown stem area index:
  • leaf surface: transpiration and photosynthesis
  • leave and stem surface (plant area index): radiative transfer, canopy evaporation, sensible heat flux, momentum absorption
• plant canopy models must parameterize turbulent fluxes between vegetation and atmosphere
  • MOST: Monin-Obukhov Similarity Theory
  • RSL: roughness sublayer, region within and just above canopies where observed flux-gradient relationships depart from MOST
• Lagrangian approach to turbulent eddies
• localized near-field theory

CLM5 also requires a complex iterative calculation of surface fluxes and canopy temperature (Lawrence et al., 2019). Up to 40 iterations are allowed in CLM5, but convergence is not guaranteed, in which case arbitrary adjustments to fluxes are implemented to achieve energy conservation.

• vertical distribution of leaf area most important
• in data-rich world of today it can be measured (opposing to soils)
• also leaf angular inclination, vertical nitrogen profile

3. The multilayer canopy model

Model revised from Bonan et al (2018) to allow for a generalized continuum of 1 to N layers and is identified as CLM-ml v1.

• mesic habitat: moderate or well-balanced supply for moisture
• xeric habitat: dry

4. Comparison of one-layer and multilayer canopies

In general, the multilayer model’s fluxes come much closer to the observations.

Reasons

• sunlit leaves dominate upper canopy, most shaded leaves in lower canopy
• $V_{\text{cmax}}$ declines with canopy depth down to 1/2
• daytime air temperature peaks in mid-canopy and decreases by 1 degree or more in lower canopy
• nighttime minimum temperature in upper canopy
• daytime specific humidity less in upper canopy
• wind speed decreases with canopy depth
• much of solar radiation absorbed in upper canopy
• leaf transpiration peaks in upper canopy: lower mid-day leaf water potential in upper canopy
• leaves in upper canopy 2 degrees warmer than in lower canopy

Important variabales

• within-canopy air temperature, specific humidity, wind speed profiles
• sensible heat flux most sensitive to temperatire profile, sunlit and shaded proportion sensible to wind speed (not the total)
• latent heatflux sensible to temperature and humidity
• GPP differences between multi-layer and big leaf driven by their light profiles, low response to well-mixed assumption, temperature, humidity and wind profiles

Number of layers

• better results with increasing number of layers
• little immprovements beyond 10 layers
  • valid for simply-structured canopies (not tropical ones)
  • might be model dependent
• proportion of absorbed solar radiation between sunlit and shaded leaves independent of number of layers

5. Discussion

The results presented in this study show that resolving vertical profiles of air temperature, specific humidity, and wind speed in forest canopies reduces surface flux biases compared with a single canopy layer.

6. Conclusions

The modeling analyses presented herein show that the dismissal of multilayer canopy models as too complex is no longer valid. Multilayer canopy models are not a complication that obscures; they are instead a simplification that illuminates the processes controlling surface flux calculations compared with land surface models such as CLM5. Multilayer canopy models are a necessary model elaboration, not the indiscriminate model elaboration that Box (1979) believed was endless and argued against.

Indeed, a comparison of technical descriptions reveals the multilayer canopy (Bonan et al., 2018) to be the simpler, not more complex, implementation of surface fluxes compared to CLM5 (Lawrence et al., 2019).

CLM5 has an ecosystem demography component (FATES; Fisher et al., 2015; Koven et al., 2020), but in coupling FATES to CLM5 the vertically-structured canopy, utilized for photosynthesis and light competition in FATES, is reduced to a canopy leaf area index and a canopy conductance for CLM5’s big-leaf surface flux formulation (Lawrence et al., 2019). A more consistent coupling between the models would utilize the vertical structure of FATES in a multilayer model of surface fluxes.

Personal conclusion

In general, multilayer canopy models are much better than big leaf models. However, for our purpose of computing GPP, we can omitt many of the overwhelmingly complicated processes and concentrate on the light profile.

How to avoid having your manuscript rejected: Perspectives from the Editors-in-Chief of Soil Biology and Biogeochemistry

In this editorial piece the editors in chief of SBB explain to what authors should pay attention before submitting papers in general, and to SBB in particular.

1. Introduction

• papers are for the readers, not the authors (which in fact is not true): do readers gain novel insights and new knowledge?

Checklist

• journal’s target audience
• methods and analyses support interpretations and conclusions
• conclusions well-grounded in the data and conclusive
• speculation is clearly identified as such
• clear writing

2. SBB’s criteria are based on our aims and scope

Soil Biology & Biochemistry publishes scientific research articles of international significance which describe and explain fundamental biological and biochemical features and processes occurring in soil systems.

1. Audience
   • what does the reader already know: leave it out and take it for granted
2. Questions
   • no case studies, only broadly relevant questions
3. Focus and scale
   • drivers of processes of soil biochemistry, not just soil as a “brown box”
4. Novelty and importance
   • well-studied processes in new circumstances or with new methods
   • incremental science: larger synthesis, new insights, deeper understanding
   • show why and how the work is novel
5. Language
   • English: AE tends to shorter sentences (laconic authors such as Mark Twain and Ernest Hemmingway), BE tends to longer and more elaborate prose (Jane Austen and Thomas Hardy)

3. An Editor’s approach: How we apply these standards when we get a manuscript

• first check for: international relevance, fundemantality, soil systems
• language check: title, highlights, opening paragraphs
• first impressions matter
• first check happens in short blocks of free time, if there are problems the paper is put on the backburner

Common issues that lead to a “Desk Reject”

1. Telling the wrong story
   • message must meet the journal’s scope
   • opening paragraph defines a problem to solve
   • conclusions will come back to this problem
   • opening problem defines target audience
   • for SBB: be general here!
2. No real question or hypothesis
   • define specific objectives at end of introduction: What question will you answer?
   • identify a knowledge gap
   • be as precise as possible
3. Poor presentation
   • read the *Guide to Authors**
   • use little color, good palettes, large font sizes, line numbers
4. Weak conclusions
   • show what the work has contributed and has it has advanced understanding
   • avoid “more research is needed…”: point out what you have done, not what you have not done
   • discuss limitations earlier
   • be precise, what are the exact implications?
   • “Show, don’t tell”.
   • first and last words are most powerful

4. Conclusions

• check journal’s scope
• read as an editor/reviewer/reader
• ironically, typo “concecus” in last words of the paper