Allometry of fine roots in forest ecosystems

Theoretical predictions regarding fine root production are needed in many ecosystem models but are lacking. Here, we expand the classic pipe model to fine roots and predict isometric scaling relationships between leaf and fine root biomass and among all major biomass production components of individual trees. We also predict that fine root production scales more slowly against increases in leaf production across global forest ecosystems at the stand level. Using meta‐analysis, we show fine root biomass scales isometrically against leaf biomass both at the individual tree and stand level. However, despite isometric scaling between stem and coarse root production, fine root production scales against leaf production with a slope of about 0.8 at the stand level, which probably results from more rapid increase of turnover rate in leaves than in fine roots. These analyses help to improve our understandings of allometric theory and controls of belowground C processes.     

The limits to population density in birds and mammals

We address two fundamental ecological questions: what are the limits to animal population density and what determines those limits? We develop simple alternative models to predict population limits in relation to body mass. A model assuming that within‐species area use increases with the square of daily travel distance broadly predicts the scaling of empirical extremes of minimum density across birds and mammals. Consistent with model predictions, the estimated density range for a given mass, ‘population scope’, is greater for birds than for mammals. However, unlike mammals and carnivorous birds, expected broad relationships between body mass and density extremes are not supported by data on herbivorous and omnivorous birds. Our results suggest that simple constraints on mobility and energy use/supply are major determinants of the scaling of density limits, but further understanding of interactions between dietary constraints and density limits are needed to predict future wildlife population responses to anthropogenic threats.     

Ecological application of wavelet analysis in the scaling of spatial distribution patterns of Ceratoides ewersmanniana

Ecological experiments are usually conducted on small scales, but the ecological and environmental issues are usually on large scales. Hence, there is a clear need of scaling. Namely, when we deal with patterns and processes on larger scales, a special connection needs to be established on the small scales that we are familiar with. Here we presented a wavelet analysis method that could build relationships between spatial distribution patterns on different scales. With this method, we also studied how spatial heterogeneity and distribution patterns changed with the scale. We investigated the distribution and the habitat of C. ewersmanniana in two plots (200 m × 200 m; the distance between these two plots is 15 km) at Mosuowan desert. The results demonstrated that spatial heterogeneity and distribution patterns were incorporated into larger scales when the wavelet scale varied from one (5 m) to four (20 m). However, if the wavelet scale was above five (25 m), the spatial distribution patterns varied placidly, the oscillation frequency of landforms stabilized at 110 m, and the dynamic quantity period of C. ewersmanniana stabilized at 115–125 m. We also identified signal mutation points with wavelet analysis and verified the heterogeneity degree of local space with position variance. We found that position variance decomposed the distribution patterns on large scales into small sampling plots, and the position with the largest variance also had the strongest heterogeneity. In a word, the wavelet analysis method could scale-up spatial distribution patterns and habitat heterogeneity. With this method and other methods derived from this one, such as wavelet scale, wavelet variance, position variance and extremely direct-viewing graphs, wavelet analysis could be widely applied in solving the scaling problem in ecological and environmental studies.     

Effects of metabolic rate on protein evolution

Since the modern evolutionary synthesis was first proposed early in the twentieth century, attention has focused on assessing the relative contribution of mutation versus natural selection on protein evolution. Here we test a model that yields general quantitative predictions on rates of protein evolution by combining principles of individual energetics with Kimura's neutral theory. The model successfully predicts much of the heterogeneity in rates of protein evolution for diverse eukaryotes (i.e. fishes, amphibians, reptiles, birds, mammals) from different thermal environments. Data also show that the ratio of non-synonymous to synonymous nucleotide substitution is independent of body size, and thus presumably of effective population size. These findings indicate that rates of protein evolution are largely controlled by mutation rates, which in turn are strongly influenced by individual metabolic rate.     

Long bone scaling in Captorhinidae: do limb bones scale according to elastic similarity in sprawling basal amniotes?

Captorhinids are a speciose clade of sauropsids that are crucial to understand several aspects of basal amniote general biology. Members of the Captorhinidae explored different diets and, amongst basal amniotes, were one of the first groups to demonstrate high‐fibre herbivory. Several papers have been published on the cranial anatomy of captorhinids, but there are relatively few studies which focus on the post‐cranium, especially on the appendicular skeleton and long bones. This contribution presents the first quantitative long bone scaling in Captorhinidae performed through morphometric analyses. From classical biomechanical research, it is well‐established that to accommodate an increase in size, gravity will result in elastic deformation of long bones. This outcome is especially significant in terrestrial tetrapods with a sprawling limb posture such as captorhinids, where great torsional stresses are applied to long bones, both during locomotion and in the resting phase. In this paper, we test whether the consistent evolutionary size increase in captorhinids led to major re‐patterning in long bone structure as theoretically expected, based on the theory of elastic similarity. Morphometric analysis shows that, apart from a small positive allometry in the humerus, captorhinid long bones scale geometrically as body size increases. Thus, the predicted elastic similarity to maintain similar levels in peak stress with an increase in dimensions does not seem not to apply to long bone evolution in captorhinids. We propose that, as already observed experimentally in larger‐bodied varanid lizards, large captorhinids could also mitigate size‐related increases in stress by reducing femur rotation and increasing the percentage of the stride cycle during which the right hindfoot was on the ground (i.e. the duty factor). In this way, large captorhinids could avoid reaching peak stress thresholds by sacrificing speed during locomotion and without a substantial long bone re‐patterning or postural change.     

Scale‐dependent spatial patterns in benthic communities around a tropical island seascape

Understanding and predicting patterns of spatial organization across ecological communities is central to the field of landscape ecology, and a similar line of inquiry has begun to evolve sub‐tidally among seascape ecologists. Much of our current understanding of the processes driving marine community patterns, particularly in the tropics, has come from small‐scale, spatially‐discrete data that are often not representative of the broader seascape. Here we expand the spatial extent of seascape ecology studies and combine spatially‐expansive in situ digital imagery, oceanographic measurements, spatial statistics, and predictive modeling to test whether predictable patterns emerge between coral reef benthic competitors across scales in response to intra‐island gradients in physical drivers. We do this around the entire circumference of a remote, uninhabited island in the central Pacific (Jarvis Island) that lacks the confounding effects of direct human impacts. We show, for the first time, that competing benthic groups demonstrate predictable scaling patterns of organization, with positive autocorrelation in the cover of each group at scales < ~1 km. Moreover, we show how gradients in subsurface temperature and surface wave power drive spatially‐abrupt transition points in group dominance, explaining 48–84% of the overall variation in benthic cover around the island. Along the western coast, we documented ten times more sub‐surface cooling‐hours than any other part of the coastline, with events typically resulting in a drop of 1–4°C over a period of < 5 h. These high frequency temperature fluctuations are indicative of upwelling induced by internal waves and here result in localized nitrogen enrichment (NO₂ + NO₃) that promotes hard coral dominance around 44% of the island's perimeter. Our findings show that, in the absence of confounding direct human impacts, the spatial organization of coral reef benthic competitors are predictable and somewhat bounded across the seascape by concurrent gradients in physical drivers.     

Modelling time‐varying low‐temperature‐induced mortality rates for pupae of Tuta absoluta (Gelechiidae,Lepidoptera)

In a series of laboratory experiments, acclimated pupae of Tuta absoluta were exposed to various constant low temperatures in order to estimate their maximum survival times (Kaplan–Meier, Lt_99.99). A Weibull function was fitted to the data points, describing maximum survival time as a function of temperature. In another experiment at ?6°C, the progress of mortality increasing with exposure time was identified. These values were fitted by a sigmoidal function converging asymptotically to 100% mortality for very long exposure times. Analysing mortality data from the maximum survival experiment by a generalized linear model showed a significant common slope parameter (p < .001) that reveals parallelism of the survival curves at each temperature if a log time axis is used. These curves appear stretched (time scaled) if plotted with a nonlogarithmic time axis. By combining these mathematical relations, it was possible to calculate a species‐specific ‘mortality surface’ which exhibits mortalities, depending on temperature and duration of exposure. In order to accumulate hourly mortalities for courses of varying temperatures, an algorithm was developed which yields mortality values from that surface taking into account the attained mortality level. In validation experiments, recorded mortalities were compared against modelled mortalities. Prediction of mortality was partially supported by the model, but pupae experiencing intensely fluctuating temperatures showed decreased mortality, probably caused by rapid cold hardening during exposure. Despite this observation, mortality data converged to distinct levels very close to 100% depending on the intensity of temperature fluctuations that were characteristic for different types of experiments. The highest mortality limit occurred at intensely fluctuating temperatures in laboratory experiments. This constituted a benchmark that was not reached under various field conditions. Thus, it was possible to identify temperature limits for the extinction of field populations of Tuta absoluta pupae.     

Modelling Plant Responses to Elevated CO2: How Important is Leaf Area Index?

BACKGROUND AND AIMS: The problem of increasing CO(2) concentration [CO(2)] and associated climate change has generated much interest in modelling effects of [CO(2)] on plants. While variation in growth and productivity is closely related to the amount of intercepted radiation, largely determined by leaf area index (LAI), effects of elevated [CO(2)] on growth are primarily via stimulation of leaf photosynthesis. Variability in LAI depends on climatic and growing conditions including [CO(2)] concentration and can be high, as is known for agricultural crops which are specifically emphasized in this report. However, modelling photosynthesis has received much attention and photosynthesis is often represented inadequately detailed in plant productivity models. Less emphasis has been placed on the modelling of leaf area dynamics, and relationships between plant growth, elevated [CO(2)] and LAI are not well understood. This Botanical Briefing aims at clarifying the relative importance of LAI for canopy assimilation and growth in biomass under conditions of rising [CO(2)] and discusses related implications for process-based modelling. MODEL: A simulation exercise performed for a wheat crop demonstrates recent experimental findings about canopy assimilation as affected by LAI and elevation of [CO(2)]. While canopy assimilation largely increases with LAI below canopy light saturation, effects on canopy assimilation of [CO(2)] elevation are less pronounced and tend to decline as LAI increases. Results from selected model-testing studies indicate that simulation of LAI is often critical and forms an important source of uncertainty in plant productivity models, particularly under conditions of limited resource supply. CONCLUSIONS: Progress in estimating plant growth and productivity under rising [CO(2)] is unlikely to be achieved without improving the modelling of LAI. This will depend on better understanding of the processes of substrate allocation, leaf area development and senescence, and the role of LAI in controlling plant adaptation to environmental changes.     

Scaling law in sizes of protein sequence families: from super-families to orphan genes

It has been observed that the size of protein sequence families is unevenly distributed, with few super families with a large number of members and many "orphan" proteins that do not belong to any family. Here it is shown that the distribution of sizes of protein families in different databases and classifications (Protomap, Prodom, Cog) follows a power-law behavior with similar scaling exponents, which is characteristic of self-organizing systems. Since large databases are used in this study, a more detailed analysis of the data than in previous studies was possible. Hence, it is shown that the size distribution is governed by two exponents, different for the super families and the orphan proteins. A simple model of protein evolution is proposed, in which proteins are dynamically generated and clustered into families. The model yields a scaling behavior very similar to the distribution observed in the actual sequence databases, including the two distinct regimes for the large and small families, and thus suggests that the existence of "super families" of proteins and "orphan" proteins are two manifestations of the same evolutionary process.     

Exploring the nonlinear geometry of protein homology

The explosion of biological data resulting from genomic and proteomic research has created a pressing need for data analysis techniques that work effectively on a large scale. An area of particular interest is the organization and visualization of large families of protein sequences. An increasingly popular approach is to embed the sequences into a low-dimensional Euclidean space in a way that preserves some predefined measure of sequence similarity. This method has been shown to produce maps that exhibit global order and continuity and reveal important evolutionary, structural, and functional relationships between the embedded proteins. However, protein sequences are related by evolutionary pathways that exhibit highly nonlinear geometry, which is invisible to classical embedding procedures such as multidimensional scaling (MDS) and nonlinear mapping (NLM). Here, we describe the use of stochastic proximity embedding (SPE) for producing Euclidean maps that preserve the intrinsic dimensionality and metric structure of the data. SPE extends previous approaches in two important ways: (1) It preserves only local relationships between closely related sequences, thus allowing the map to unfold and reveal its intrinsic dimension, and (2) it scales linearly with the number of sequences and therefore can be applied to very large protein families. The merits of the algorithm are illustrated using examples from the protein kinase and nuclear hormone receptor superfamilies.