Generally, the holidays are a time for rest and relaxation.  Christmas dinner, college football bowl games, perhaps a little alpine skiing are some of my favorite things to do during winter holiday breaks, which I usually spend in my home state of Colorado.  This year I decided to undertake a project that aims to develop a new way to measure root respiration.  The idea uses foundational gas exchange theory and the amazing Li-Cor photosynthesis technology (Li-6800).  

Although the Li-6800 was designed to measure leaf-level photosynthetic CO2 flux, the same basic principles apply when measuring respiration - the metabolic process that consumes oxygen and releases carbon-dioxide as organisms turn food into energy.  In fact, when doing a CO2 response curve on a leaf, the instrument measures photorespiration, when the [CO2] is at zero.  

Root respiration has been hypothesized as a potential key axis in the hypotheical root economical spectrum.  Maximum rate of photosynthesis is a main axis in the leaf economical spectrum.   Plants with an acquisitive root strategy (high SRL, high root N, thin diameter, and low stele to cortical ratio), should (by definition) have higher rates of metabolic activity (i.e. respiration) than plants with more conservative roots.  Currently, most of the data on root respiration rates are either: 1) derived from measuring soil respiration rates which use of collars that include or exclude roots (typically fine and/or coarse roots) (e.g. using a Li-Cor Li-8100 or similar), or 2) measure respiration rates on excised roots in a closed respiration chamber.  The first method makes it hard to relate respiration rates to root morphology (SRL, root N content etc.), and in the second method, it is unknown whether respiration rates are affected by root excision, and if rates of root respiration vary temporally or with environment.

Thus, I wanted to design a way to repeatedly measure rates of fine root respiration on living entire root systems (ERSs - those containing at least 3 root orders of the most distal fine roots), in a similar way to which we can measure ambient rates of photosynthesis over time on a single leaf.  The first step was to design a root box, which houses the living ERS.  The design (Figure A) would have to encapsulate the root and surrounding soil in a unit that is easily accessed at the top of the soil A horizon (first 10 cm in soil profile; i.e. just under the organic layer, depending on the the depth of lateral surface roots of the tree you want to measure).  The second step is to design a custom respiration chamber that attaches to the Li-Cor 6800 that is capable of measuring the respiration of the root box.  It is a nested design, where the root box is gently lifted from the soil where it rests, and inserted into the respiration chamber for measurement, then returned.  Root box controls will have to be used to measure rates of soil respiration (microbial and fungal).  Root system respiration can then be estimated by calculating the difference. 

Figure A: conceptual figure for root box and custom respiration chamber design (for Li-6800).

With the idea conceptualized, I set out to envision construction and buy materials.  I settled on making the root boxes out of plastic buckets that could be sliced into about 1" square strips.  I used aluminum meshing for the bottom.  This should allow water and nutrient flow and fungal/ microbial activity to occur inhibited in the soil.  I went with a heavy duty 1/2" acrylic sheeting for the chamber.  I bought some clamp latches and a mixing fan, and some acrylic bonding solvent.  All items were found pretty easily on Amazon.  ​So, I went into the holiday break with the idea, the materials were purchased and ready, but needed to do the construction during the break, because several large tools were needed (i.e. a table saw and a drill press).  My father (architect and handyman) has a workshop with these tools, and helped me with the construction.  Thanks Dad!

Overall, I think the construction came out well (pictures below).  Whether or not the design will give good data is another thing, entirely.  Let's see!    Comments welcome, thanks for reading, & happy new year! ​♠

On Monday morning, Ariel Lugo took the stage.  Admittedly, I am a fan of Ariel, but I think all in attendance were able to take something away from his plenary.  He spoke to many aspects of the changing nature of ecology, and showed how many of the old-school concepts from his graduate days in the 1970's (aka the Odum days) were still applicable today, with some re-thinking and re-furbishing.  One key point, is that the human can no longer be viewed as being separate from the ecological system.  In the past, disturbance was viewed as being separate from the [eco]system itself, and ecologists would study the resultant effects to gain understanding about the system.  Lugo proposed a paradigm shift (that is likely already underway), that incorporates disturbance, and system resistance and recovery from disturbance, as inherit properties to the system.  A deepening, contexutal, and holistic understanding of where the thresholds in ecosystem resistance and resilience are is required for building sustainable & flexible "socio-eco-techo"- logical systems in the Anthopoce.  buen mensaje!

After the plenaries, the meeting always gets going.  This was my 3rd ESA (I am on a 2 year cycle for ESA, 50th Baltimore, 52nd Ft. Lauderdale, and now 54th New Orleans).  A buzz engulfed the Ernst M. Moore Convention Center where the A/C was on full throttle amid the humid southern air and flavor of the Big Easy.  Folks ran to-and-fro, as did I.  Many old connections were re-kindled and new-ones were ignited.  Below I share some of my notes on the sessions I attended, mostly for my records, but if you were unable to attend they might serve as a highlight reel:

• Courtenay Ray (grad student in the Blonder Lab at ASU) delivered an animated and informative talk on the ecological explanations for and the demographics of an alpine plant community in Colorado.  She was interested in whether there is an ecological advantage to forming mat roots and whether this affects species vital rates.  Results were mixed, and she has some new experiments underway to tease the governing factors apart, including some ant seed-dispersal/seed-preference trials.  link to abstract
• Zoe Cardon (from Woods Hole) gave a very nice talk on hydraulic redistribution in plants and soil, and how incorporating proper representation of those processes into CLM models improves their predictions  link to abstract
• Zequing Ma (Chinese Academy of Sciences) presented his recent work on Global Root trait bio-geography.  A very nice talk on how root diameter is a key axis of root trait variation, globally.   link to abstract
• Alan Knapp (Colorado State) gave an excellent talk, where he acknowledged how all of us forest ecologists have "grassland envy" because of the relative ease in which NPP estimates can be obtained in grasslands vs. forests.  He also addressed whether precipitation experiments addressing climate change actually capture ecosystem responses (despite their variability).  link to abstract
• Martijin Slot (STRI Panama) gave a well-delivered talk on the drivers of the photosynthetic response to warming in Tabebuia rosea.  Stomatal conductance is main driver, but it a bit counter-intuitive, because of the "indirect effect of warming that causes stomatal closure through increasing VPD".  His results confirmed a stomatal closure limit of ~65% RWC or ~4MPa. Among his talk an the others in that session, there seemed to be a consensus building that you can get up to a 0.4 deg. C shift in Topt with warming, so there is some ability for plants to acclimate photosynthesis to increasing temperatures. link to abstract
• Pete Manning (Bik-F) presented work done with his team (SDiv-2, including Dylan Craven) on the nature of biodiversity and ecosystem function, specifically resilience and resistance and stability.  They tested the hypothesis that slow plant communities (sensu the Leaf Economics Spectrum) were more stable.  Results were supportive and nuanced, depending on community composition (phylogenetics) and community synchrony and compensatory trends in relation to ecosystem recovery.  link to abstract
• Benton Taylor (Columbia) showcased his results from some potted competition experiments with the N-fixing tree Pentaclethra macroloba and another non-fixing species done at La Selva Biological Station in Costa Rica.   Root nodule formation and N-fixation occurred to a greater degree in treatments with high light.  He presented biomass partitioning results, which led to his conclusion that "producing nodules is more than offset by the reduced fine root production" (i.e., there no structural cost to N-fixers to produce nodules).  pretty cool.  look for his paper to emerge in Nature Plants. link to abstract
• Andrew Quebbeman (Columbia) gave a very nice talk on how topographic heterogeneity influence our estimates of green house gas (GHG) fluxes in tropical forests.  He measured GHG emissions and soil moisture from soil across the Luquillo Forest Dynamics plot (which has high photographic variation), and concluded that both were important drivers of GHG fluxes. link to abstract
• Tim Perez (U Miami) gave a talk on the ecological consequences of heat tolerances in leaves of tropical trees.  Tim has measured the heat tolerances for hundreds of tropical tree species.  The heat tolerance is temperature at which photosynthesis the leaf breaks down, and Tim measures this by exposing leaves to high-temperature water baths and then measuring chlorophyll fluorescence.  He was interested in seeing if leaf-level photosynthetic heat tolerances could explain decelerating growth rates of trees in La Selva.  There were some promising results.  look for more to come from Tim and check out his website here.  link to abstract
• Freddie Draper (ITCB post-doc in collaboration with Greg Asner's lab) presented his work on the hyperdominance of Amazonian flora at different canopy strata.  His research (compiling data from the ATDN plots, Al Gentry's work and other sources) shows that few species are [hyper]dominant at all life stages in the Amazon.  Mytales, Piperales, and Melastomes dominate the understory, palms in the midstory.  link to abstract
• Rich Norby (Oak Ridge National Lab) gave a stimulating talk on the historical perspective of rising CO2 emissions.  He started in 1954 and walked through the developing understanding of the critical points of understanding in determining ecosystem responses to eCO2, mainly temperature effects on photosynthesis and growth and NPP.  Several seminal papers (Kramer 1981 Bioscience, Mooney 1991 Bioscience, and others) are now on my reading list. Thanks Rich... and, "If you know any millionaire friends that want to fund Amazon FACE", tell them to contact Rich.   link to abstract
• Colleen Iveresen (Oak Ridge National Lab) took attendees on a tour of three climate change experiments: Oak Ridge FACE, the SPRUCE project and the NGEE Arctic site.  One cool contrast between the Oak Ridge FACE and the SRUCE experiment she highlighted was that under elevated CO2 conditions the roots of the temperate trees increased biomass and depth, however in the boreal  bog, roots were found shallower in the flooded soil profile to maintain access to oxygen.  She also highlighted the FRED initiative (data available at http://roots.ornl.gov).  link to abstract
• Danien Bonal (CRNS INRA) summarized findings from Paracou, French Guiana, on water use between tree and lianas in a very cool talk.  Lianas had more negative deuterium isotopic values after they did a isotopic water injection into the soil, meaning that lianas were more efficient at taking soil water than trees.  Interestingly, they showed results that lianas effectively suck water away from the roots of trees.  link to abstract   link to paper here
  • On Thursday morning, I camped out for the NGEE Tropics session on model-data integration, which was killer.  I've said before, I am not a modeler, but Man, is that stuff intellectually awesome!  The FATES (Functionally-Assembled Terrestrial Ecosystem Simulator) model is published, check it out here.  github here for those more modeling inclined. 
  • Brad Christoffersen (U Texas) presented some interesting data from Panama on tropical tree hydraulic responses to the 2015-2015 El Niño drought.  He showed increasing variability in sap-flux with increasing water deficit, and a hump-shaped relationship of sapflow with increasing VPD  link to abstract
  • Xiantiao Xu (Harvard) presented a pretty intricate model of canopy leaf phenology for tropical forests integrating a sun-shade leaf model with a Vcmax and leaf lifespan model.  link to abstract
  • Jerome Chave (CRNS) talked about his model (the TROLL as he calls it) which is a dynamic IBM, that uses tree allometry, mainly and is able to model tropical forest dynamics (especially forest structure) well.  link to abstract
  • Jennifer Holm (Berkeley National Lab) - talked about model validation of FATES using data from ZF-2 in the Amazon.  She was testing AGB recovery of the model in response to logging.  The model performed relatively well, but underestimated the growth of large trees after timber harvest, leading her and Jeff Chamber to come up with the 'idling respiration' hypothesis for large trees in tropical forests.  I thought that was cool. link to abstract
  • Tom Powell (Berkeley National Lab) also presented on model testing with FATES and ED.  link to abstract
    • there were a few other talks in that session, apologies if I missed yours, the notes are convoluted there, I must have needed a coffee. 
  • David Medvigy (Notre Dame) switched it up in the session to talk about nutrient addition experiments in tropical dry forests.  He is currently part of a team doing the second known one ever.  The first, from Mexico, showed a doubling of stem biomass in response to added N&P. Their results did not show the same.  He spoke to the use of the MEND and N-Com models for modeling nutrient use by tropical trees.  He showed some results from simulations and concluded that there is probably no increase in wood production in dry forests under fertilization.  This is congruent with our understanding of tropical wet forests at the moment, but is likely context-dependent in relation to the severity of nutrient limitation.  link to abstract
• Ryota Aoyagi (postdoc at STRI Panama) showed some really cool experimental results on Phosphorus and Panamian trees (work he is doing with Ben Turner at STRI).  Species that are low-P specialists showed greater ability to uptake phosphorus and an increase in fine root biomass (allocation to fine roots) in response to increased levels of P.  This was one of my favorite talks of the conference, awesome work.  link to abstract
• Lars Hedin started his talk with Ma Nature paper on root traits and then presented a first-principles model on the evolutionary stable strategies of root-mycorrhizal associations.  The evolutionary simulations he showed return two divergent and stable strategies.  He showed that you can recover stable coexistence between AM and ECM trees with abundances that vary with latitude (recovering the natural distribution of trees in N. America).  link to abstract
• Brian McGill gave perhaps the most attended single 20-min talk at ESA.  The room (one of the bigger ones) was packed.  He presented the case on "why we have to unpack biodiversity and how to unpack it".  The basic premise was that S (sp. richness) is lousy" because it's 1-dimensional, scales non-linearly with increasing scale, and ignores abundance, spatial scale, and density in community data. He showcased of a new R package "mobr", which allows for several methods to "make S more multidimensional using rarefaction, N (number of individuals), and pi (probability of species encounter).  link to abstract
• Kelly Andersen presented the data up to date on the Amazon Fertilization Experiment (AFEX), where they have been adding N, P, and cations to the soil for about two-years now.  They are measuring lots of ecosystem responses including soil respiration, photosynthesis, growth and tracking nutrients.  At this point the fertilization has led to increased respiration and it is getting into the plants.  There should be some more results in the coming years, and the big question is will there be a growth response in this very-P-limited system.     link to abstract
• Kristine Cabuaguo was my last attended talk where I took notes (I had my talk shortly after).  She showed how the microbial community composition of the rhizophere can affect plant functioning, using experimental drought treatments with bacterial inoculations on potted Clusia pratens.  Proline, a protein associated with resistance to drought and other stressors, was highest in plants inoculated with all beneficial bacteria.  There was a lot more in there, with enzyme assays and other stuff; a very nice presentation.    link to abstract

​There were other talks that were awesome, too.  Some of which I was unable to attend (and wished I could), and a few of which I attended but my notes are either too disorganized to make sense or my chicken-scratch handwriting is illegible to decipher. 
Regardless, until the ESA (or whichever meeting you attend), take care, keep working, and research on! ​♠

In any scientific field, staying abreast of the scientific literature that is constantly emerging is a full-time job.  Not to mention, reading older, still-relevant work.  It can feel like one is treading water to simply stay afloat.  Here, I summarize some of the my recent favorites to emerge in the field of tropical forest ecology, thus far in 2018.  I categorize the papers into three groups related to my interests - roots, tropical forest dynamics & functional traits, and forest modeling, plus a fourth for miscellaneous cool stuff.  

Roots: 
Research on roots is rapidly gaining headway, which is encouraging, as I was bit hesitant to focusing my PhD below-ground initially. 

• Adriana Corrales, Terry Henkle and Matthew Smith had a nice Tansley Review in New Phytologist on the state of knowledge of ectomycorrhizal fungi as they relate to tropical ecosystems.  see article here.
• Interested more in arbuscular fungi and their symbiotic relationships with plants?  A very nice review of this evolutionary do-si-do emerged in separate Tansley Reveiw by British- and French- affiliated authors (led by Christine Strullu-Derrien).  check it out here.
• Amandine Erktan, Luke McCormack, and Catherine Roumet edited a special issue on roots in Plant & Soil, with many nice works.  See all the articles here.
• In March, roots took center stage in an article in led by Ziqing Ma and many other heavy-hitters from the root ecology world.  Ma et al. found that roots have evolved to become thinner over time, and that root evolution has radiated in the tropics to create the greatest degree of functional variation.  link to the article in Nature Letters here.
• Zhu Mao et al. had a nice study on root tensile strength, morphology and anatomy from 4 species of tropical trees at Xishangbanna Tropical Botanical Garden in Annals of Botany here.
• Also appearing in the Annals of Botany, Marcin Zadworny et al. published a study linking root morphological traits of temperate trees to hydraulic strategies. check it out here 
• Additionally, the FRED (Oak Ridge National Lab's FINE ROOT ECOLOGICAL DATABASE) 2.0 is out.   via twitter: @Colleen_Iversen.  original publication here. 

Tropical Forest Dynamics & Functional Traits: 

• Top spot on the list goes to the special issue in Biotropica on the phenology of tropical trees / forests.  Edited by Katharine Abernethy, Irene Mendoza (her paper here), and Bryan Finegan. 
  • Emma Bush had a paper in there, as did Joe Wright (here), but I especially liked this paper by Colin A. Chapman et al. using long-term data from an African forest to illustrate how ENSO and solar radiation fail to completely predict patterns in fruiting and flowering, highlighting the need for further research into the phenology of tropical trees.    
• An instant favorite of mine was this recent work by Loorens Poorter.  It's sure to be a highly cited classic in no time.  
• Jie Yang et al. in Trends in Ecology & Evolution on trait-performance variability. study here. 
• Amy Zanne dissected some of the bio-geographic patterns in plant functional traits in this study. 
  • building on a favorite from late last year - Ian Wright Science - link here.
• At the beginning of the year, Bene Bachelot, had a nice study linking fungi to seedling dynamics in the Luquillo Forest Dynamics Plot in Ecology.  link here.
• John B. Vincet et al. in JTE with a study on forest dynamics on unstable soil in a New Ginean forest dynamics plot link here.
• Rob Salaguro-Gomez and others produced a very nice cross-journal special issue on the application of functional traits to the population dynamics of various organisms.  link to BES special issue here.
  • I especially liked Ben Blonder's paper that measured many traits and environmental variables for an alpine plant community.  here.
• Hop on the con-specific negative density-dependence train:  several responses to Joseph LaManna's Science paper have emerged.  here & here, with respective responses, here & here.
  • also, Natalia Umaña and others had a paper on CNDD in Proceedings B. 
• Matteo Detto identifies changes in the abiotic environment that coincide with ENSO and increased phenological production at BCI, Panama. Nature Communications study here.  
• like Lianas?  Marco Visser had a very nice Journal of Ecology paper on liana-tree host-parasite dynamics. here.
• All the individual of a single species (Beilschmiedia roxburghiana)  in the Xishuanbanna 20-ha plot were genotyped confirming a genetic spatial structure consistent with density dependence and dispersal limitation.  study in Ecology Letters here.
• Cool study by Yi Jin on how the effects of topography and light can modulate to permit species co-existence in a Chinese forest.  Journal of Ecology article here. 

Modeling*: 

• check out Nate McDowell's Tansley Insight on tropical moist forest tree mortality.
  • don't want to read the paper? Check out Nate's talk at STRI Panama where he explains his paper.
• Sean Michealetz uses a Metabolic Theory framework to link photosyntheic prodution across scales in his Tansley Insight. 
• Integral projection models for tropical forest trees help forecast population dynamics out in this demonstrative study by Jessie Needham.  
• Tom Powell from the NGEE-Tropics team had this paper published on the Ecosystem Demography model. 

Miscellaneous cool stuff: ​

• Large Trees Rule!  A cross site comparative study from the CTFS ForestGEO plot newtwork (led by the Jim Lutz lab at Utah State University) illustrates just how imporant they are. link to Global Ecology and Biogeography.  
• Study confirms that Legumes dominate in dry and early-succesional forests.  check it out here.
• Study on rainfall interception by urban trees in the Jardin Botanico of San Juan, PR by Christopher Nytch.  here. 
• Two new ICTB (International Center for Tropical Botany) papers out on metabolomic approaches in tropical trees: 
  • One by Jason Vleminckx in Frontiers in Plant Science here.
  • and one by FIU's Diego Salazar in Nature Ecology & Evolution here.
• The effects of the 2016 El Niño drought on soil bio-geochemistry and gas flux at Luquillo were published in Nature Communications (Christine O'Connell et al.) here.
• PNAS meta-analysis led by Shihong Jia on herbivore exclusion experiments here.
• Y. MAHLI on the Anthropocene.  recommended read here. 
• The Shenzen Declaration on Plant Sciences (from December of last year, but still worthy of making the list).

*Admittedly, I am not a modeler, however I am gaining a deeper appreciation for that art.  

None of these lists are exhaustive, but rather a few of the papers which I have enjoyed reading over the last several months.  And, guaranteed there are more to come.  Please feel free to share some of your favorites in the comments.  
Happy reading! ♠️

The diversity of root forms among plants is mind-boggling.  Plant roots come in all shapes, sizes and colors, which likely permits them to exploit unique niches in the soil and coexist.  It was not until I started looking more closely at the roots of tropical trees that I was able to appreciated the diversity of root forms (and hypothetically function, although linking root morphologies to their functional trade-offs is an ongoing scientific endeavor).  It is hypothesized that Angiosperm roots have divergently evolved from a common ancestor with an intermediate root (an inner stele) diameter (1,2).  Some Angiosperms in the more-basal clades (e.g., Magnolids) developed thicker more-fleshy roots, while higher-order clades developed thinner, more-lignified roots.  

Secondly, plant roots have evolved in the soil environment, which is loaded with a diversity of bacteria and fungi and other organisms (soil fauna, etc.).  They, therefore, associate with both bacteria and fungi in a wide-variety of relationships, some of which are beneficial.  One of the most-potentially beneficial relationships among roots and bacteria is nitrogen fixation.  Nitrogen fixation occurs by bacteria (either those in the genus Rhizobium (rhizobial symbioses) or Frankia (actinorhizal symbioses), 3) housed in root nodules, or modified root structures specifically designed for the functional association with the bacteria.  There is a whole body of literature on the biological mechanism by which the formation of root nodules is induced by the the bacterial infection/presence (see 4,5), which makes the interaction all that more intriguing.   What's more is that the association is rare among plants.  

Legumes are the masters of rhizobial nitrogen fixation (e.g., Peanuts, Soybeans, etc.), which has made them favorites in agricultural crop rotations for centuries because they replenish soil nitrogen.  Rhizobium bacteria are found in about 80% of Legumes and the plants in the genus Parapsonia (3).  Actinorhizal symbioses occur in about 200 species from 8 families (3).  Both rhizobial and actinorhizal associations are found only in the Rosids 1 clade, which means they share a common ancestor (3).   The Figs (in the Moraceae family) are also in this group, although it has been thought that bacterial associations with them are non-existent.  I must say, that I seen a fair number of Ficus roots, and none have been nodulated. Futhermore, I found at least one reference that reports that plants in the Moraceae cannot form root nodules (see image below from 3): 

That was why I was perplexed when I presumably dug up the roots of a Ficus in a forest near Xishuangbanna Tropical Botanical Garden and found nodules (shown below).  

I say presumably dug up, because it is always possible that I got the root of a neighboring tree or liana or something other than the target tree, despite my best efforts to trace the root from the trunk of the target tree.  Jury still out on whether this came from a Ficus or not.  I will need to double check; a cool nodule none the less ∴ #nodular, bro! ♠️

            There is mounting evidence that tropical trees are becoming more and more affected by global climate change, with droughts (1) and increased temperatures becoming more prevalent (2), causing increased tree physiological stress and mortality in tropical forest (3, 4).  The global surface temperature increase by 2100 relative to 1850-1900 is predicted to between 0.3 and 4.8°C, depending on the emissions scenario, with half of the scenarios predicting a >2°C warming (5).  A >2°C warming will affect extensive areas of tropical forests, and there is great uncertainly whether tropical trees will be able to acclimate (6) and how the effects of warming large areas of tropical forest may feedback to affect the earth system (7).  When considering an individual tree, population of individual trees, or communities of tree species, the options are: adapt (i.e., migrate; 8) , acclimate, or go extinct(9).  There is evidence that tropical trees are migrating upslope along elevational profiles in areas where mountains exist (10), resulting in community thermophilization, albeit at low rates, 0.002-0.022 °C year-1 for adult trees(11).  Therefore, the question remains: how will the millions of trees inhabiting the extensive areas of lowland tropical forests (e.g. the Amazon) respond?

            The ability to rapidly and effectively acclimate to the projected increase in the temperature in the tropics has typically be thought of as unlikely(2).  Regardless, using the tenants of classical niche theory in relation to the realized thermal nice, I will entertain three theoretical possibilities of how tropical tree may adapt to climate change.  First, species migration is not needed if niche breath is large, highly plastic, or does not interact directly with the environmental stressor (temperature).  Second, I contend that the underappreciated molecular plasticity in gene expression (i.e. transcriptomic variability) allows species to interact and manipulate their functional niche space in response to an environmental stressor, permitting a large adaptability at evolutionary timescales.  Third, species may acclimate to an environmental stressor if it is spatially or temporally variable, allowing patch dynamics in susceptibility and recovery to drive stress-resistant selection. 

          The evidence that the temperature rise in tropical forest will happen abruptly enough to push tropical trees beyond their physiological limit is mixed, but mounting, and contains some uncertainty (1, 4, 12, 13).  The current ambient air temperature for most tropical evergreen forests lies below 28°C, in some cases well below that mark; areas where air temperatures exceed 28°C tend to support grassland and more-xeric deciduous woody plant communities, in part because these areas are drier(14).  This speaks to the cofounding effects of total precipitation and climate seasonality in understanding temperature responses; mainly, that although the optimal temperature for light saturating photosynthesis for seven tropical forest areas (four Asian and three Amazonian study plots) peaked around 28°C, there was substantial variability among them when accounting for vapor pressure deficit.  Nonetheless, modeled net ecosystem exchange for tropical forests declines above 28°C (14).  Recent work on leaf-level photosynthetic rates of tropical trees shows the optimal temperature for assimilation to be 29°C (15).  Thus, individual trees may be slightly more resistant to temperature increase, than understood when modeling the productivity whole forest s (12).  

         Some of the best, recent evidence suggests that tropical tree species are sensitive to warming, with relative growth and assimilation rates definitely declining above 30°C, and that later successional species with lower photosynthetic capacities being more vulnerable (16).  Slot and Winter (16) grew tree seedlings of three tropical tree species in controlled environments at 25, 30, and 35 °C, and all three species showed some degree of acclimation by increasing their thermal optimum of photosynthesis and reducing respiration rates.  But, increases in thermal optimums of photosynthesis lagged the environmental temperature increase, and rates of photosynthesis still declined significantly in the warmest treatment (with several seedlings dying completely).  Therefore, the thermal niche of tropical trees is somewhat plastic, and species can make physiological adjustments to increasing temperature, up to a point (probably around 30-32°C) (15, 16).  It is unlikely that tropical forest areas will warm fast enough to where such physiological adjustments will not permit the acclimatization by tropical trees; therefore, we can view tropical trees, and tropical forest albeit a bit less so, to have a wide thermal niche.  No doubt, there will likely be stress and mortality of individuals that exist at the edge of the thermal niche(10, 11), for example the warmest locations of the species distribution, or areas undergoing habitat change via increased drought.  Yet, tropical tree species do show physiological adaptability and the overall extinction risk related to thermal stress for tropical forests and most of the species they harbor should remain low.

             Generally, research on the genetic variability in tropical trees has focused on the genomic diversity of trees in space, focusing primarily on their ecological interactions (17).  Analyzing the genomic diversity within communities is undoubtedly one of the major modern biological challenges, and it is becoming increasingly feasible to sequence and assemble transcriptomes (18) and genomes for tropical tree species (19).  Recent transcriptomic evidence using tropical tree communities showed that individual trees can regulate the expression of genes associated the pest and pathogen defense in relation exposure risk (20).  Trees that existed at higher densities with conspecifics had greater levels of gene expression associated with plant defense, showing that negative density dependence operates at both at physiological level via plant-pant interactions and invokes an underlying genetic response.  One can envision this operating with respect to thermal or drought stress, where individuals exposed to the stressor develop an increased ability to respond physiologically via genetic or transcriptomic memory.  In fact, transcriptomic expression by species subjected to experimental drought was a greater predictor of species co-occurrence in space than phylogenetic of functional trait relatedness in a Wisconsin forest (21).  Identifying how gene expression relates to the physiological adaptations of tropical trees subjected to thermal stress is a promising avenue in better understanding the biological processes underlying their responses to increasing temperatures. 

           Lastly, both the physiological (12, 14, 16) and genetic adaptations (20, 21) interact in time and space in a directionally stochastic fashion.  Increased temporal and spatial variability of rainfall and temperature, with the general increasing trend in temperature, are the two main predictions of climate change forecasts(5).  With respect to temperature, I predict persistence in realized niche thermal nice for tropical trees with increased temporal variability in species performance.  Stressful years will increase in frequency and magnitude, but so will years suitable to high performance, and reproduction of tropical trees.  For example high irradiance ENSO years will likely drive forest-wide reproductive bursts in fruit production (22).  If species can adapt physiologically and genetically, at least in the transcriptomic expression of genes associated with physiological adaptation to increased temperature, the logical pathway for evolutionary selection and diversification of species adapted to higher temperatures becomes reasonable.  The patch dynamics (sensu 23) of individual performance, or fitness in relation to thermal stress, and species interactions in space will then determine if tropical trees persist in the coming centuries.  Thus, it becomes increasingly important to maintain large areas of intact tropical forest in well-conserved condition.
​
            In conclusion, despite the doomsday rhetoric among some scientific camps and the undeniable facts of anthropogenic climate change, the thermal niche for tropical tree species is broad in most contexts, especially that of the typical lowland tropical forest community.  This allows species to shift within their realized thermal niche, at least with respect to projected temperature increase.  Furthermore, temperature increase. as a stressor. is spatially and temporally variable allowing ample time for physiological and genetic acclimation by species.  Such responses will interact over time and in space to determine the ability of tropical species to evolve in the face of increasing temperatures.  There is evidence that plant communities have responded to climate change in the past (24), yet uncertainty surrounds whether they will continue to do into the future. ♠

Works Cited
1. S. L. Lewis, P. M. Brando, O. L. Phillips, G. M. van der Heijden, D. Nepstad, The 2010 amazon drought. Science 331, 554-554 (2011).
2. T. M. Perez, J. T. Stroud, K. J. Feeley, Thermal trouble in the tropics. Science 351, 1392-1393 (2016).
3. N. McDowell et al., Drivers and mechanisms of tree mortality in moist tropical forests. New Phytologist,  (2018).
4. W. R. Anderegg et al., Tropical nighttime warming as a dominant driver of variability in the terrestrial carbon sink. Proceedings of the National Academy of Sciences 112, 15591-15596 (2015).
5. IPCC Climate change 2014 Synthesis Report-Summary for Policymakers (2014).
6. M. A. Cavaleri, S. C. Reed, W. K. Smith, T. E. Wood, Urgent need for warming experiments in tropical forests. Global Change Biology 21, 2111-2121 (2015).
7. V. K. Arora et al., Carbon–concentration and carbon–climate feedbacks in CMIP5 Earth system models. Journal of Climate 26, 5289-5314 (2013).
8. I.-C. Chen, J. K. Hill, R. Ohlemüller, D. B. Roy, C. D. Thomas, Rapid range shifts of species associated with high levels of climate warming. Science 333, 1024-1026 (2011).
9. K. J. Feeley, M. R. Silman, Extinction risks of Amazonian plant species. Proceedings of the National Academy of Sciences 106, 12382-12387 (2009).
10.  K. J. Feeley et al., Upslope migration of Andean trees. Journal of Biogeography 38, 783-791 (2011).
11. A. Duque, P. R. Stevenson, K. J. Feeley, Thermophilization of adult and juvenile tree communities in the northern tropical Andes. Proceedings of the National Academy of Sciences 112, 10744-10749 (2015).
12. M. E. Dusenge, D. A. Way, Warming puts the squeeze on photosynthesis–lessons from tropical trees. Journal of experimental botany 68, 2073 (2017).
13. C. Huntingford et al., Simulated resilience of tropical rainforests to CO 2-induced climate change. Nature Geoscience 6, 268 (2013).
14. Z.-H. Tan et al., Optimum air temperature for tropical forest photosynthesis: Mechanisms involved and implications for climate warming. Environmental Research Letters 12, 054022 (2017).
15. M. Slot, K. Winter, In situ temperature response of photosynthesis of 42 tree and liana species in the canopy of two Panamanian lowland tropical forests with contrasting rainfall regimes. New Phytologist 214, 1103-1117 (2017).
16. M. Slot, K. Winter, Photosynthetic acclimation to warming in tropical forest tree seedlings. Journal of experimental botany 68, 2275-2284 (2017).
17. C. O. Webb, Exploring the phylogenetic structure of ecological communities: an example for rain forest trees. The American Naturalist 156, 145-155 (2000).
18. S. R. Strickler, A. Bombarely, L. A. Mueller, Designing a transcriptome next‐generation sequencing project for a nonmodel plant species1. American Journal of Botany 99, 257-266 (2012).
19.  E. K. Lee et al., A functional phylogenomic view of the seed plants. PLoS Genetics 7, e1002411 (2011).
20. J. Zambrano et al., Neighbourhood defence gene similarity effects on tree performance: a community transcriptomic approach. Journal of Ecology 105, 616-626 (2017).
21.  N. G. Swenson et al., Tree co-occurrence and transcriptomic response to drought. Nature communications 8, 1996 (2017).
22.  M. Detto, S. J. Wright, O. Calderón, H. C. Muller-Landau, Resource acquisition and reproductive strategies of tropical forest in response to the El Niño–Southern Oscillation. Nature communications 9, 913 (2018).
23. R. Levins, Some demographic and genetic consequences of environmental heterogeneity for biological control. American Entomologist 15, 237-240 (1969).
24. M. B. Bush, M. R. Silman, D. H. Urrego, 48,000 years of climate and forest change in a biodiversity hot spot. Science 303, 827-829 (2004).

Density dependence, or a correlation of per capita population growth rate with population size, is a biological phenomenon that is ubiquitous in nature.  Density dependence may either be positive or negative, depending if ∆N(b-d), increases or decreases as population size (N) increases (where b and d are births and deaths, respectively).  Cases of positive density dependence (i.e., Alee effects) are rare in nature, however Negative Density Dependence (NDD; sometimes called a Malthusian-Darwinian process) is quite common.  Since the first basic identification that the human population is limited by food production (Malthus 1798), the study of density dependence has become increasingly nuanced.  The nuances of density dependence and it workings stem from the fact that it is impossible to identify process (i.e., the mechanism underlying density dependence) solely from pattern (i.e., a time series of population counts), and density dependence can be difficult to detect.  Long time-series data at the appropriate scale for population fluctuations of the study organism are needed.    

A classic example of how density dependence operates, can be understood from the data presented by Davidson and Andrewartha (1948) on Thrips imaginis and their host plants.  Davidson and Andrewartha sought to describe the interannual variability in the population growth of the thrips using an exponential (i.e. density independent) growth function.  Although, the fit of the exponential growth function described the data relatively well, Frederick Smith (1961) incorporated the inherit seasonality in the data with a piece-wise (i.e., multiple) regression with slopes that varied with winter degree days and amount of spring day.  He demonstrated that the population growth data for the thrips were highly seasonal (i.e., density dependent), with population growth distributed normally around November/December.  This can be seen visually from the data table represented in the original paper by Davidson & Andrewartha.  The thrips go through boom and bust cycles that are regulated by seasonality. 

Aside from thrips, NDD appears many times in the body of ecological literature.  In tropical forest ecology, the classic example is the famous “Janzen-Connell” hypothesis (now more of theory).  Around the boom of when population biology was in the renaissance of density dependence (i.e., the 1970s), Dan Janzen and Joe Connell were both walking around tropical forests, independently.  They observed that seedling densities were highest immediately below the tree crown.  This phenomenon can be attributed to dispersal limitation of seeds originating from the parent tree.  Trees have many dispersal mechanisms including wind dispersal (amenochory), animal dispersal (zoochory), and mechanical dispersal (autochory).  However, gravity dispersal is certainly the most common mode of dispersal by trees in that all seeds will fall to the ground in the absence of a seed disperser, demonstrating why most reproductive seeds do not fall from the parent tree. 

Janzen (1970) and Connell (1978) thought about density dependence and its implications for the maintenance of diversity, and came to the theoretical conclusion that that seedling survival increases with seed dispersal distance.  The mechanism by which increased mortality of seedlings closer to the parent tree occurs is understood to be pathogen and/or herbivore related (Comita et al. 2010).  One can envision how common species are more frequently consumed by generalist herbivores, which target seedlings directly beneath the parent tree.  Therefore, greater dispersal distances result in higher rates of seedling survival, and thereby maintain species diversity in highly-diverse tropical forests.  Furthermore, NDD in tropical forests can be a mechanism for population regulation for many species. 

​Recent work (Lebrija‐Trejos et al. 2016; Uriarte et al. 2018) has focused on how NDD is mediated via water availability in tropical seedlings, resulting in interacting effects of moisture and biotic interactions (likely belowground fungi or pathogens) on seedling distribution patterns and diversity.  At Barro Colorado Island in Panama, Conspecific NDD was stronger in years with higher soil moisture.  Also, trees with larger seed mass exhibited decreased levels of conspecific NDD, suggesting that seeds with greater seed mass have weaker NDD-regulation (i.e., a less negative slope of the density dependence as population size increases.  Similarly, at Luquillo, Puerto Rico, the positive effects of increased solar radiation on seedling survival and growth were more pronounced, favoring drought tolerant species (e.g., lianas) and light-demanding pioneers and palm seedlings.  In conclusion, NDD is a strong regulating force in population dynamics for many organisms and should be examined considering abiotic and biotic drivers of population change. ♠

Comita, L. S., H. C. Muller-Landau, S. Aguilar, and S. P. Hubbell. 2010. Asymmetric density dependence shapes species abundances in a tropical tree community. Science 329:330-332.
Connell, J. H. 1978. Diversity in tropical rain forests and coral reefs. Science 199:1302-1310.
Davidson, J., and H. Andrewartha. 1948. Annual trends in a natural population of Thrips imaginis (Thysanoptera). The Journal of Animal Ecology:193-199.
Janzen, D. H. 1970. Herbivores and the number of tree species in tropical forests. American Naturalist:501-528.
​Lebrija‐Trejos, E., P. B. Reich, A. Hernández, and S. J. Wright. 2016. Species with greater seed mass are more tolerant of conspecific neighbours: a key driver of early survival and future abundances in a tropical forest. Ecology Letters 19:1071-1080
Malthus, R. T. 1798. An Essay on the Principle of Population. As it affects the future improvement of society, with remarks on the speculations of Mr. Godwin, M. Condorcet, and other writers. London.
Smith, F. E. 1961. Density dependence in the Australian thrips. Ecology 42:403-407.
Uriarte M., R. Muscarella, and J. K. Zimmerman. 2018. Environmental heterogeneity and biotic interactions  mediate climate impacts on tropical forest regeneration.  Global Change Biology 24: e692-e704.

Nutrients, the environment, plant leaves, and photosynthesis
Nutrients, chemical compounds or ions in the environment, are necessary for plant survival, growth and reproduction.  Plants must photosynthesize to survive; photosynthesis is the passive uptake of carbon-dioxide and water for the autotropic production of carbohydrates using sunlight.  Therefore, plants require water, carbon-dioxide and sunlight.  Carbon (C), Hydrogen (H), and Oxygen (O) an all be readily obtained from the air and water.  Over three quarters of the air is Nitrogen (N), in the form of di-nitrogen gas, but this form is inaccessible to plants.  Other nutrients must come from the soil, and therefore the performance of plants is directly related to the degree of fertility of the soil they occupy.  Phosphorus (P), N, and Potassium (K), are the three principle macronutrients that together or separately can limit plant performance.  N is needed for photosynthetic activity in the leaves, P promotes root development and K is used throughout the plant, and in conjunction with N usually, in photosynthesis and to promote overall plant health, like protecting against temperature extremes or defending against disease.  Several other micronutrients are necessary for plant growth, including Calcium, Magnesium, Sulfur and other trace metals, but these are rarely limiting to the physiological functioning of plants.  Agriculturists have long realized the positive effect of increasing macronutrient concentrations in the soil on plant performance, however relating nutrients to plant performance and identifying the mechanisms by which plants increase physiological activity has been one of the main endeavors of plant ecophysiology over the last several decades. 

From a more biological perspective, nutrients are the building blocks of life.  Nucleic acids, and proteins are rich in Nitrogen and Phosphorus.  Photosynthesis, at the leaf level, is the combination of a complex photo- and bio-chemical process (i.e., light and dark reactions).  These reactions require a suite of specialized biological machinery, for example the thylakoids of the chloroplasts or the many specialized proteins and enzymes involved in photosynthesis, like the powerhouse protein of the Calvin-Benson cycle: Ribulose-1,5-bisphosphate carbolxylase/ oxygenase (RuBisCO).  RuBisCO is probably the most abundant protein on earth; it is a very large protein with the molecular weight of 560,000 Dalton and is comprised of 15-50% chloroplast protein (Vapaavuori 1986), thus can be considered to be molecularly rich in N and, to a lesser degree, P.  Variation in the amount of RuBisCO or other photosynthetically-functional proteins and enzymes contained in plant leaves affect their nutrient concentrations and photosynthetic performance.  Accordingly, identifying how leaf tissue nutrient concentrations vary and correlate with measures of photosynthesis has led to a deeper understanding of the variability in plant form and function (e.g., Wright et al. 2004, Reich 2014).

The typical way in which plant performance is measured is via carbon assimilation rates of leaves.  Amazing instruments (e.g., the Li-6800 Portable Photosynthesis System, LiCOR, Lincoln, NE) can quantify the amount of carbon dioxide uptake by a leaf, by measuring its photosynthetic assimilation (A).  By optimizing the abiotic environment of the plant and the immediate environment of leaf for photosynthesis, such as increasing light to a non-limiting level (>1000 μmols m-2 s-1) or controlling the temperature to be at the optimal 29°C (Slot and Winter 2017), one can measure the maximum rate of photosynthesis (Amax).  Amax varies, with C3 plant usually having lower rates than C4 plants (Pons et al. 1998).  For example, Amax for tropical tree species usually ranges between 10 and 20 μmols CO2 m-2 s-1 (see values reported by Slot and Winter 2017), where Amax for a corn plant (Zea mays) can be above 40 μmols CO2 m-2 s-1 (personal observation).  This is due to differences in the biochemical pathways of photosynthesis between C3 and C4 plants; C4 plants have the ability to do carbon dioxide enrichment in bundle sheath cells.  In C4 plants, CO2 moves through the typical light-dependent reactions in the mesophyll and is then transported via malate to the bundle sheath cells where the gas is concentration before being fixed again by RuBisCO in Calvin-Benson cycle.  The release of CO2 (decarboxylation) of malate in the bundle sheath cells creates higher concentrations of CO2, meaning the Calvin-Benson cycle can operate more efficiently, thus increasing Amax (Schulze et al. 2005).  The CO2 enrichment also creates lower rates of photorespiration in C4 plants.  Accordingly, plants vary in the way in which they use CO2, but they also vary in the way in which they use other nutrients, for example N and P.  Notably, leaf N content for corn can range from 17 to 45 mg g-1 in an agricultural setting (i.e., with application of fertilizer Schepers et al. 1992), which is within the global range of leaf nitrogen concentrations for all plants (Reich and Oleksyn 2004).
 
Relationships between plant performance, leaf construction and leaf N
First, leaf concentrations of nutrients, principally N and P, reflect their availability in the soil.  In most terrestrial systems, plant growth is both N and P limited, and on average plants biologically require 10 times as much N as P, when growing optimally (Pons et al. 1998).  Therefore, the N:P in leaf tissues is typically a good indicator of which nutrient is limiting, with N:P ratios <14 signifying N limitation, N:P ratios > 16 corresponding to P limitation and colimitation occurring at ratios between 14 and 16 (Aerts and Chapin III 1999).  But moreover, leaf nutrient concentrations have been a surrogate trait for plant physiological performance (i.e., rates of photosynthesis).  Species that photosynthesize at higher rates and grow faster, uptake nutrients from the soil at greater rates.  A leaf economics spectrum ranges from cheaply-constructed leaves with short lifespans (i.e., little relative return on nutrient investment) to more-expensive, thicker leaves, with high leaf mass per area (LMA) and long lifespans (Wright et al. 2004).  Along the leaf economics spectrum, higher N and P concentrations and faster A rates are associated with leaves of lower LMA (Wright et al. 2004, Reich 2014).  Leaves with lower LMA tend to be associated with grasses, deciduous tree species or forbs, while evergreen trees tend to have higher LMA, lower A rates, and lower N and P leaf concentrations (Westoby and Wright 2006, Reich 2014).  The amount of leaf N is correlated with the amount of leaf P (Niklas et al. 2005), both of which increase as LMA decreases (Wright et al. 2004).  In other words, cheaply-constructed leaves with short lifespans have both higher concentrations of N and P, which covary.

To understand the physiological tradeoffs in leaf performance (i.e., photosynthetic differences), we need to understand the economics of leaf construction.  Leaf construction cost can be defined as the amount of carbon needed to produce a gram of leaf tissue relative to the associated “payback time” needed to recover that carbon via photosynthesis (Karagatzides and Ellison 2009).  Leaves that are carbon-rich, tend to have high construction costs, but also tend to have long leaf lifespans.  Typically, plants will further invest in secondary metabolites or latex into expensive leaves to guard against herbivory and ensure a longer-term return on nutrient investment.  Ecologically, this is a k-strategy.  Carbon-rich leaves tend to have lower maximum rates of photosynthesis (Wright et al. 2004), and species that produce expensive leaves tend to grow slower and have denser wood (Díaz et al. 2016) .  Conversely, an r-strategist produces leaves with a fraction of the carbon investment per gram of leaf tissue, creating a thin, structurally-weaker leaf with a shorter lifespan.  These leaves are also more susceptible to mechanical damage, pathogens or herbivory, however can be reproduced more readily by the plant, because the construction cost is low.  They have higher maximum rates of photosynthesis, and are typically produced by heliophilic species with high growth and mortality rates (Wright et al. 2010).

The basis of the relationships between leaf structure, leaf nitrogen concentrations and rates of photosynthesis arises from the fact that photosynthesis is a rate-limited process that uses both CO2 and macronutrients, mainly N & P.  Most terrestrial ecosystems are either N or P limited, or N and P co-limited (Elser et al. 2007); so let us think of photosynthesis CO2 and nutrient-limited process.  Assuming no light limitation and the fact that leaves want to maximize CO2 assimilation rates, we can decompose photosynthesis into two components: a portion of the process that is CO2-limited, and a portion of the process that is limited by the availability of reactants needed for the Calvin-Benson cycle, or nutrient-limited.  The maximum rate of assimilation during the carboxylation reaction-limited portion of photosynthesis (Vcmax) and the maximum rate of assimilation when the photosynthetic machinery is limited by reactants (i.e., electron transport rate is constant) (Jmax), can give provide information on the relative strength of CO2 or nutrient limitation on leaf-level plant performance.  Wullschleger (1993) compiled estimates of Vcmax and Jmax for 109 C3 plant species and found that Vcmax  ranged from 6 (Picea abies; Norway Spruce) to 194 (Beta vulgaris; garden beet) μmols CO2 m-2 s-1 , and that Jmax  ranged from 17 (again, Picea abies) to 372 (Mahastrtun rotundifolium; a desert annual) μmols CO2 m-2 s-1.  Averages for Vcmax and Jmax across all 109 species were 64 and 139 μmols m-2 s-1, respectively, and when comparing values across plant lifeforms, Vcmax and Jmax were generally higher for herbaceous annals than for wood perennials.  This confirms photosynthetic performance basis for the variation on plant leaf structural investment. 

Because plants use on average 10 times a much N as P, N concentrations have been more popular in investigating physiological relationships to leaf tissue nutrient concentrations.  Leaf N does scale with chlorophyll, because proteins used in the photosynthetic dark reactions and those in the thylakoids are N-rich.  In a brilliant study, John Evans (1989) showed that not all plants use N the same within a leaf, and that differences in N use vary based on the light environment and differences in leaf economics strategies among species.  Specifically, shade-tolerant species on the resource-conservative end of the leaf-economics spectrum allocated a greater proportion of N to thylakoids in the Chloroplast, whereas resource-acquisitive species, with higher rates of photosynthesis allocated less to thylakoid proteins and more to Calvin cycle proteins and enzymes (e.g., RuBP carboxylase).  Therefore, we can understand there to exist a tradeoff at the leaf-level on the relative return in nutrient investment in electron transport (J) relative to carboxylation (V) on assimilation rate (Maire et al. 2012, Walker et al. 2014).  When carboxylation is limiting photosynthesis, an investment in maximizing Jmax has little effect on assimilation rate and leads to electron transport energy not used in photosynthesis.  However, when light is limiting photosynthesis, a greater investment in Jmax relative to Vcmax maximizes photosynthetic rate, yet that greater investment is offset by the cost of energy dissipation when carboxylation is limiting, to avoid inhibition.  Maire et al. (2012) employed this assumption about the relative investment of N in Jmax, its relationship to Vcmax and using species-specific parameter values for specific leaf area, created a leaf-level photosynthetic model that accounted for 93% of the total variance in leaf N across species and environmental conditions.

In conclusion, leaf N varies because of physiological differences related to plant strategy.  Plants with high leaf N have low LMA and high rates of photosynthesis.  The biological explanation for the higher concentrations of leaf N in these species, is in short, that they need it do the work of assimilating CO2 at high rates.
 

Works cited:
 
Aerts, R., and F. S. Chapin III. 1999. The mineral nutrition of wild plants revisited: a re-evaluation of processes and patterns. Pages 1-67  Advances in Ecological Research. Elsevier.
Díaz, S., J. Kattge, J. H. Cornelissen, I. J. Wright, S. Lavorel, S. Dray, B. Reu, M. Kleyer, C. Wirth, and I. C. Prentice. 2016. The global spectrum of plant form and function. Nature 529:167.
Elser, J. J., M. E. Bracken, E. E. Cleland, D. S. Gruner, W. S. Harpole, H. Hillebrand, J. T. Ngai, E. W. Seabloom, J. B. Shurin, and J. E. Smith. 2007. Global analysis of nitrogen and phosphorus limitation of primary producers in freshwater, marine and terrestrial ecosystems. Ecology Letters 10:1135-1142.
Evans, J. R. 1989. Photosynthesis and nitrogen relationships in leaves of C 3 plants. Oecologia 78:9-19.
Karagatzides, J. D., and A. M. Ellison. 2009. Construction costs, payback times, and the leaf economics of carnivorous plants. American Journal of Botany 96:1612-1619.
Maire, V., P. Martre, J. Kattge, F. Gastal, G. Esser, S. Fontaine, and J.-F. Soussana. 2012. The coordination of leaf photosynthesis links C and N fluxes in C3 plant species. PloS One 7:e38345.
Niklas, K. J., T. Owens, P. B. Reich, and E. D. Cobb. 2005. Nitrogen/phosphorus leaf stoichiometry and the scaling of plant growth. Ecology Letters 8:636-642.
Pons, T., H. Lambers, and F. Chapin III. 1998. Plant Physiological Ecology. 2nd Edition edition. Springer-Verlag, New York.
Reich, P. B. 2014. The world‐wide ‘fast–slow’ plant economics spectrum: a traits manifesto. Journal of Ecology 102:275-301.
Reich, P. B., and J. Oleksyn. 2004. Global patterns of plant leaf N and P in relation to temperature and latitude. Proceedings of the National Academy of Sciences 101:11001-11006.
Schepers, J., D. Francis, M. Vigil, and F. Below. 1992. Comparison of corn leaf nitrogen concentration and chlorophyll meter readings. Communications in Soil Science and Plant Analysis 23:2173-2187.
Schulze, E., E. Beck, and K. Müller-Hohenstein. 2005. Plant Ecology. Springer, Berlin.
Slot, M., and K. Winter. 2017. In situ temperature response of photosynthesis of 42 tree and liana species in the canopy of two Panamanian lowland tropical forests with contrasting rainfall regimes. New Phytologist 214:1103-1117.
Vapaavuori, E. 1986. Correlation of Activity and Amount of Ribulose 1, 5-bis phosphate Carboxylase with Chloroplast Stroma Crystals in Water-Stressed Willow Leaves. Journal of Experimental Botany 37:89-98.
Walker, A. P., A. P. Beckerman, L. Gu, J. Kattge, L. A. Cernusak, T. F. Domingues, J. C. Scales, G. Wohlfahrt, S. D. Wullschleger, and F. I. Woodward. 2014. The relationship of leaf photosynthetic traits–Vcmax and Jmax–to leaf nitrogen, leaf phosphorus, and specific leaf area: a meta‐analysis and modeling study. Ecology and Evolution 4:3218-3235.
Westoby, M., and I. J. Wright. 2006. Land-plant ecology on the basis of functional traits. Trends in Ecology & Evolution 21:261-268.
Wright, I. J., P. B. Reich, M. Westoby, D. D. Ackerly, Z. Baruch, F. Bongers, J. Cavender-Bares, T. Chapin, J. H. Cornelissen, and M. Diemer. 2004. The worldwide leaf economics spectrum. Nature 428:821.
Wright, S. J., K. Kitajima, N. J. Kraft, P. B. Reich, I. J. Wright, D. E. Bunker, R. Condit, J. W. Dalling, S. J. Davies, and S. Diaz. 2010. Functional traits and the growth–mortality trade‐off in tropical trees. Ecology 91:3664-3674.
Wullschleger, S. D. 1993. Biochemical limitations to carbon assimilation in C3 plants—a retrospective analysis of the A/Ci curves from 109 species. Journal of Experimental Botany 44:907-920.

In case you were wondering, CTFS stands for the Center for Tropical forest Science, also know as Forest-GEO: www.forestgeo.si.edu/; and is a global network of forest dynamics plots, aimed at documenting the diversity and dynamics of the world's forests. 

In ecology, the understanding of fluctuations in abundances of organisms’ forms the foundational building blocks of population models.  One of the most fundamental models for population growth is the logistic growth model (above),  where the number of individuals at time t: N(t) is a function of the number of individuals at the previous time-step (N sub 0), the carrying capacity (K), and the intrinsic rate of increase, (r sub 0, referred to as r not). [Note e the base of the natural logarithm, aka Euler's constant ~ 2.718]. Sibly et al. (2005) made a major contribution to the rich field of population ecology when they analyzed 1780 time series of population dynamics from 647 taxa, including insect, fish, bird, and mammal species.  They were mainly interested in the relationship between population growth rate (pgr);  and population size (N), or the manner/speed with which pgr slows as a population reaches carrying capacity (K).  Based on the data, they concluded that they could ‘rule out the possibility that the shape of the pgr-density relationship is strongly associated with taxonomy or body size’.  By analyzing the curvature of the pgr-density relationship (i.e., whether shape of the curve was convex or concave), they qualitatively classified the relative rates (or negative slope) at which populations reached carrying capacity into three classes (basically, fast – convex, where θ < 1; slow – concave, where θ > 1, or constant (i.e., linear), where θ ≈ 1).  They then analyzed frequencies of θ, finding that many of the time-series had more frequent negative values for θ, concluding that the majority of populations for the 647 taxa analyzed were ‘living at densities above the carrying capacity of their environments.’
            These conclusions, along their analytical approach, sparked quite a stir in the field population ecology, inciting at least 3 responses.  Reynolds and Freckleton (2005) point out that there may be high degrees of measurement errors, because the time series used in the analyses use estimates of population numbers rather than direct counts.  They contend that it is unclear how θ (pgr in relation to population density) behaves when there are measurement errors, or when density dependence is weak or absent.  Additionally, θ values less than 1 violate one of the primary assumptions of the logistic growth mode: the assumption that density dependence is linear with increasing population size (N).  Reynolds and Freckleton rephrase the conclusions of Sibley et al. to point out that concave population growth rates signify that population decline to carrying capacity is slower than population growth rates are when approaching carrying capacity, a conclusion with implications for management of organismal populations (e.g., fisheries or wildlife). 
            Seven months later, two additional replies to Sibley et al. were published in the same issue of Science.  Joshua V. Ross (2006) pointed out that Sibley et al. failed to note that negative values of θ do not make much sense biologically, and that θ should be constrained to positive values.  Ross clearly explains the math, in that if you take limit of pgr to negative infinity, population size (N) approaches 0 (i.e., extinction), when θ<0 and >0.  Ross revisits the shape of the pgr-density cures, showing population growth to be concave when less than -1 but greater than zero, and convex when greater than 1, a very important caveat.  Additionally, C. Patrick Doncaster (2006) addresses the cases where the pgr-density relationship is less than -1.  He eloquently shows graphically, that θ has a strength parameter, γ, that measures the strength of the density dependence (DD).  When DD operates ‘perfectly’ (i.e., is the only regulating force on population growth and returns populations to K), values of γ are equal to 1.  However, when density dependence overcompensates (i.e., regulating populations to numbers below K when N above K), γ values are >1.  The interesting cases arise when density dependence is weak, or γ values are < 1, in that the nature of exponential population growth means that populations spend more time ascending to carrying capacity than they do descending.  This gets to a faulty conclusion of Sibley et al., in that they failed to account for variability in strength of density dependence.  Doncaster concludes that the shape of the pgr-density relationship reveals more about population regulation than it does about θ, largely due to independent stochastic forces on populations (i.e., all pgr-density relationships become concave when accounting for stochasticity). 
            In this light, I revisit the conclusions of McMahon et al. (2009) in regard to their study of Elephant Seal populations on Marion Island.  Results from the study found θ estimates for the population to be >4 with a pgr-density slope of roughly -1.  McMahon et al. then concluded that there were two distinct phases of population growth, where the population was increasing from 1987-1997, and when the population was decreasing from 1997-2004, and that this was being driven by stochastic changes in the environment associated the Southern Oscillation Index.  Given the information about logistic population growth brought to light by Sibley et al. and their respondents, I suggest that Marion Island seal populations are being regulated by DD.  The two models in the McMahon et al. study found K to be 452 and 483 [breeding female] individuals, and that population was above K from 1986 to 1991.  With a pgr-density curve having a slope of -1, population return to carrying capacity should be constant, unless the strength of the density dependence, γ, increases with population size.  McMahon et al. failed to explore the relationship of γ and N, making it unclear as to whether environmental stochasticity or DD was making the major contribution to regulating Elephant Seal populations.  Elephant Seals are long lived mammals (k-selected organisms), and a time series of 18 years may not be long enough to understand the how the strength of DD in populations scales with the pgr-density curve.  I guess, they will just have to keep counting them to find out.  

References:
Doncaster, C. P. 2006. Comment on" On the regulation of populations of mammals, birds, fish, and insects" III. Science 311:1100-1100.
McMahon, C. R., M. N. Bester, M. A. Hindell, B. W. Brook, and C. J. Bradshaw. 2009. Shifting trends: detecting environmentally mediated regulation in long-lived marine vertebrates using time-series data. Oecologia 159:69-82.
Reynolds, J. D., and R. P. Freckleton. 2005. Population dynamics: growing to extremes. Science 309:567-568.
Ross, J. V. 2006. Comment on" On the regulation of populations of mammals, birds, fish, and insects" II. Science 311:1100-1100.
Sibly, R. M., D. Barker, M. C. Denham, J. Hone, and M. Pagel. 2005. On the regulation of populations of mammals, birds, fish, and insects. Science 309:607-610.

My favorite ecosystem is the subtropical montane Tabonuco forest of the Luquillo Mountains in northeastern Puerto Rico.  The system is described as “aseasonal” and everwet, receiving >3500 mm/year rainfall with temperatures ranging between 20 and 23°C.  The complete food web is documented in Reagan and Waide (1996).  When published this book revolutionized the understanding of food webs, as it was the first book to fully document a food web of tropical rain forest.  Forest species richness is greater than temperate forests, but less than tropical forests at similar latitudes due to an island effect, and measures around 200 species.  Bird and ant richness is on par with other tropical forests, but there are fewer beetles than continental tropical forests.  Faunal richness is low, lacking large-bodied herbivores, however frogs and lizards are present at very high densities.  In fact, the ecosystem has the highest densities of Etherodactylus coqui frogs anywhere in the world (3200 adults and 17000 juveniles/ha). 

First and second-order headwater streams dissect the forest and harbor high densities of freshwater shrimp, land-dwelling crabs and small-bodied fish (e.g., gobies).  Given the community and abundances of organisms, traditional food web theory would predict that the food web of such an ecosystem should be relatively simple, with 3 to 4 trophic levels.  However, researchers found that on average the food web was more complex, with on average 8.5 links per food chain, with several reciprocal loops.  They also found the number of links in the food web was variable across different habitats in the ecosystem, and that within habitats the food web is partitioned into diurnal and nocturnal components with different predator and prey interactions.  Notably, without large herbivores, food chains were longer (not shorter) due to the higher trophic efficiency of cold-blooded organisms (i.e., frogs, lizards) relative to larger warmer-bodied organisms with higher metabolic rates.  Most of the organisms are omnivorous making species interactions strong, but complex and variable. ​

Understanding carbon (C) dynamics in ecosystems is important for understanding ecosystem energy balance over time, the biological processes within an ecosystem, and projecting ecosystem responses to global climate change.  C molecules are a principle element in most biological compounds used by organisms.  At the ecosystem scale, the net C balance is defined as the difference between the amount of C entering versus leaving the system, or the change in C storage over time; and because ecosystems are complex the net ecosystem C balance must quantify the C flux of several important C pools, gaseous, inorganic and organic.  The gaseous C pool is mainly CO2, but also contains methane and other volatile organic C gas derivatives [e.g., isoprenoids].  The inorganic C pool is comprised of elemental forms of C [e.g., carbonates], which are usually found in the soil, that are freely available to plants and microbes.  The organic forms of C are the biologically-bound forms of C in the ecosystem and are found in alive or dead biomass.  The amount of C in each of the C pools depends on the energy balance of the ecosystem and rate of biological processes that can transfer the C from one pool to another.  Photosynthesis, or the anabolic, autotrophic production of organic C compounds using CO2 and sunlight, is the primary process that moves C from its gaseous forms into organic forms.  Decomposition is the breakdown of organic material transforming its C into simpler, inorganic elemental forms. 
            The amount total biomass in an ecosystem is a function of the balance between photosynthesis and decomposition, minus respiration (the amount of C used in metabolic processes), and the residence time of C in the organic vs. inorganic pools (Figure 1, Odum 1969).  As plants sequester C from is gaseous form, some of the C may be transforming it into sugars, carbohydrates which in turn may be returned to gaseous C pool via respiration, but some of the C may be stored or converted to more complex organic forms within the plant (e.g. tissues), thereby fixed into the inorganic C pool.  C from the organic pool must be converted to an inorganic form before eventually returning to the gaseous C reservoir.  Usually, this happens through the decomposition of necromass (or dead biomass), where microbes and other decomposers catabolically break down biological tissues containing organic forms of C, converting them to simpler inorganic forms.  These balance of these processes is termed the net ecosystem C balance, and is limited by the rate of the biological process at the organismal level, and the abundance of that organism in the ecosystem.  The majority of terrestrial ecosystems are C sinks, meaning that the ability of plants to sequester C via photosynthesis largely outweighs ecosystem C release through respiration, decomposition and lateral C transfer (leaching etc.), resulting in the accumulation of biomass over time.  As terrestrial ecosystems mature, the accumulation of biomass asymptotes as the net positive difference between productivity and respiration peaks and then decreases.
            Plants have a variety of strategies for accumulating C via photosynthesis, and rates of C uptake by plants (i.e., assimilation rates) vary and are determined by their physiological traits (stomatal density, leaf area, leaf turnover rate, photosynthesis type etc.).  This leads to differences in residence times for C in the biological component of the C cycle, and differences in the chemical makeup of plant biomass.  At the ecosystem-scale, this residence time is a function of the transfer of C from primary producers to higher trophic levels, which mainly happens through herbivory, or the consumption of some form of plant biomass.  Plant material quality is often characterized by its nutrient content or the C to N ratio, with material that has more N content being considered higher-quality.  Higher quality plant materials are preferentially selected by both herbivores and decomposers, influencing C and nutrient cycling rates. ♠

​Above: an excerpt from Edward De Bono's book: 'Practical Thinking'  (1971), where he argues that the principle objective of a good scientist is to be wrong.  That is to say, that scientists try to prove that their ideas hold water by constantly trying to disprove them; or in essence asking a "yes" question and concluding an answer of "no".  In Practical Thinking, De Bono advocates for a third answer to the usual yes/no question.  A usual "yes" or "no" can be circumvented with a Bonian "po". 
​
A "po" in short means possibly.  It can be the launching point for scientific inquiry.  

Thinking pioneer, psychologist, physician, and all-around intellectual, Edward De Bono pioneered a new school of thought that incorporates six thinking caps. ​Of his six caps science uses the discernment cap, defined as: 

• logic applied to identifying reasons to be cautious and conservative. Practical and realistic thinking primarily relying on logical reasoning.

Science, as a discipline, relies on using the scientific method.  
Observation -> Hypothesis -> Experimental design -> Experiment -> Analysis of results -> Careful thought and interpretation of results -> Generation of scientific knowledge and understanding (not guaranteed).  Implicit in a good experiment, is the isolation of variables of interest. 

Ecology is a science were the isolation of experimental variables is unusually difficult. Natural experiments tend to include many confounding factors.  For example, when trying to quantify the successional responses of 0.1 Ha forest patches to a human-induced experimental hurricane ("The Canopy Trimming Experiment") in Puerto Rico, forest recovery to the experimental trimming was confounded by differences in community composition among replicates.  (Zimmerman et al. (2014), Forest Ecology and Management 322 64-74).  

In a classic note that has now been blown-up to epic proportions, Dan Janzen published arguably the shortest academic note ever.  Two measly words.  Yes? No. The dichotomy of these two simple words embodies the essence of scientific process and ensuing debate.  As illustrated by De Bono, a good scientists ask questions, with the sole motivation of proving them wrong. 

What if we don't know?  We can simply say "po".  And the voice of reason speaks; Leave it to the British to pick it apart; Thank you C.H. Sterling!  A Bonian "po" allows for the scientific method to decide, as if to say, "well, possibly", "I simply do not know", or "Why don't we find out".  

Modern day scientists have immense pressure to deliver concrete, yes/no answers to weighty questions that oftentimes impact policy, decisions and livelihoods.  Usually, the answers is more nuanced than a simple, yes/no, therefore I advocate for the revival of the "it depends" or Bonian "po". ♠