Large old trees increase growth under shifting climatic constraints: Aligning tree longevity and individual growth dynamics in primary mountain spruce forests

In a world of accelerating changes in environmental conditions driving tree growth, tradeoffs between tree growth rate and longevity could curtail the abundance of large old trees (LOTs), with potentially dire consequences for biodiversity and carbon storage. However, the influence of tree-level tradeoffs on forest structure at landscape scales will also depend on disturbances, which shape tree size and age distribution, and on whether LOTs can benefit from improved growing conditions due to climate warming. We analyzed temporal and spatial variation in radial growth patterns from ~5000 Norway spruce (Picea abies [L.] H. Karst) live and dead trees from the Western Carpathian primary spruce forest stands. We applied mixed-linear modeling to quantify the importance of LOT growth histories and stand dynamics (i.e., competition and disturbance factors) on lifespan. Finally, we assessed regional synchronization in radial growth variability over the 20th century, and modeled the effects of stand dynamics and climate on LOTs recent growth trends. Tree age varied considerably among forest stands, implying an important role of disturbance as an age constraint. Slow juvenile growth and longer period of suppressed growth prolonged tree lifespan, while increasing disturbance severity and shorter time since last disturbance decreased it. The highest age was not achieved only by trees with continuous slow growth, but those with slow juvenile growth followed by subsequent growth releases. Growth trend analysis demonstrated an increase in absolute growth rates in response to climate warming, with late summer temperatures driving the recent growth trend. Contrary to our expectation that LOTs would eventually exhibit declining growth rates, the oldest LOTs (>400 years) continuously increase growth throughout their lives, indicating a high phenotypic plasticity of LOTs for increasing biomass, and a strong carbon sink role of primary spruce forests under rising temperatures, intensifying droughts, and increasing bark beetle outbreaks.     

Human-modified landscapes driving the global primate extinction crisis

The world's primates have been severely impacted in diverse and profound ways by anthropogenic pressures. Here, we evaluate the impact of various infrastructures and human-modified landscapes on spatial patterns of primate species richness, at both global and regional scales. We overlaid the International Union for the Conservation of Nature (IUCN) range maps of 520 primate species and applied a global 100 km² grid. We used structural equation modeling and simultaneous autoregressive models to evaluate direct and indirect effects of six human-altered landscapes variables (i.e., human footprint [HFP], croplands [CROP], road density [ROAD], pasture lands [PAST], protected areas [PAs], and Indigenous Peoples' lands [IPLs]) on global primate species richness, threatened and non-threatened species, as well as on species with decreasing and non-decreasing populations. Two-thirds of all primate species are classified as threatened (i.e., Critically Endangered, Endangered, and Vulnerable), with ~86% experiencing population declines, and ~84% impacted by domestic or international trade. We found that the expansion of PAST, HFP, CROP, and road infrastructure had the most direct negative effects on primate richness. In contrast, forested habitat within IPLs and PAs was positively associated in safeguarding primate species diversity globally, with an even stronger effect at the regional level. Our results show that IPLs and PAs play a critical role in primate species conservation, helping to prevent their extinction; in contrast, HFP growth and expansion has a dramatically negative effect on primate species worldwide. Our findings support predictions that the continued negative impact of anthropogenic pressures on natural habitats may lead to a significant decline in global primate species richness, and likely, species extirpations. We advocate for stronger national and international policy frameworks promoting alternative/sustainable livelihoods and reducing persistent anthropogenic pressures to help mitigate the extinction risk of the world's primate species.     

Change in climatically suitable breeding distributions reduces hybridization potential between Vermivora warblers

Aim

Climate change is affecting the distribution of species and subsequent biotic interactions, including hybridization potential. The imperiled Golden-winged Warbler (GWWA) competes and hybridizes with the Blue-winged Warbler (BWWA), which may threaten the persistence of GWWA due to introgression. We examined how climate change is likely to alter the breeding distributions and potential for hybridization between GWWA and BWWA.

Location

North America.

Methods

We used GWWA and BWWA occurrence data to model climatically suitable conditions under historical and future climate scenarios. Models were parameterized with 13 bioclimatic variables and 3 topographic variables. Using ensemble modeling, we estimated historical and modern distributions, as well as a projected distribution under six future climate scenarios. We quantified breeding distribution area, the position of and amount of overlap between GWWA and BWWA distributions under each climate scenario. We summarized the top explanatory variables in our model to predict environmental parameters of the distributions under future climate scenarios relative to historical climate.

Results

GWWA and BWWA distributions are projected to substantially change under future climate scenarios. GWWA are projected to undergo the greatest change; the area of climatically suitable breeding season conditions is expected to shift north to northwest; and range contraction is predicted in five out of six future climate scenarios. Climatically suitable conditions for BWWA decreased in four of the six future climate scenarios, while the distribution is projected to shift east. A reduction in overlapping distributions for GWWA and BWWA is projected under all six future climate scenarios.

Main Conclusions

Climate change is expected to substantially alter the area of climatically suitable conditions for GWWA and BWWA, with the southern portion of the current breeding ranges likely to become climatically unsuitable. However, interactions between BWWA and GWWA are expected to decline with the decrease in overlapping habitat, which may reduce the risk of genetic introgression.     

Development and use of probability models: The industry perspective

Summary In the processed meat industry, food safety and microbiological shelf life issues lend themselves to the use of probability modeling. Our research concentrated on predicting the effectiveness of sodium lactate as an antibotulinal agent in vacuum packaged, uncured and cured turkey breast model systems. In uncured turkey breast containing 1.4% NaCl, 0.3% Na phosphate, and 0–3% Na lactate, the antibotulinal effect of sodium lactate can be predicted using the following model: Days to toxicity = 3.13+0.39(Na lactate)². Using cured turkey breast with 0.3% Na phosphate, 0.2% sucrose, 0–3% Na lactate, the time to toxicity can be predicted from the following model: Days to toxicity = 1.69+4.88(NaCl)–11.16(Na lactate)+7.23(Na lactate)². Probability models have also been developed to predict the refrigerated shelf life of specific processed meat products. The usefulness of the predictive modeling for food safety and quality in the food industry will also be discussed.This paper was presented at The International Conference on the Application of Predictive Microbiology and Computer Modeling Techniques to the Food Industry, April 12–15 1992, Hyatt Regency Hotel, Tampa, FL, USA.     

The greenhouse effect: the impacts of carbon dioxide (CO2), ultraviolet-B (UV-B) radiation and ozone (O3) on vegetation (crops)

Man's influence on the greenhouse effect, the heating of the atmosphere due to increasing concentrations of tropospheric trace gases, is of much international concern. Among the climatic variables, elevated levels of carbon dioxide (CO₂), ultraviolet-B (UV-B) radiation and ozone (O₃) are known to have a direct effect on vegetation. Our current knowledge of these effects is mainly based on studies involving single stress mode. Thus, the joint effects of CO₂, UV-B and O₃ on vegetation are poorly understood. Nevertheless, based on the literature analysis of plant response to individual stress factors, it can be concluded that sorghum, pea, bean, potato, oat, lettuce, cucumber, rice and tomato are among the crop species potentially sensitive to the joint effects of the aforementioned three variables. Similar information for tree species is essentially lacking.At least with some climatic variables such as O₃, present modeling efforts of cause-effect relationships have proven to be controversial. While at a regional geographic scale ambient CO₂ concentrations appear to be relatively homogeneous, ambient concentrations of O₃ exhibit significant temporal and spatial variability. Because of the protective action of O₃ against UV-B, similar but inverse temporal and spatial variability is expected in the surface levels of UV-B. Thus, future experimental designs should consider these exposure dynamics and modeling cuase-effect relationships should be directed to stochastic processes.     

A data-driven framework to model the organism–environment system

Organisms modify their development and function in response to the environment. At the same time, the environment is modified by the activities of the organism. Despite the ubiquity of such dynamical interactions in nature, it remains challenging to develop models that accurately represent them, and that can be fitted using data. These features are desirable when modeling phenomena such as phenotypic plasticity, to generate quantitative predictions of how the system will respond to environmental signals of different magnitude or at different times, for example, during ontogeny. Here, we explain a modeling framework that represents the organism and environment as a single coupled dynamical system in terms of inputs and outputs. Inputs are external signals, and outputs are measurements of the system in time. The framework uses time-series data of inputs and outputs to fit a nonlinear black-box model that allows to predict how the system will respond to novel input signals. The framework has three key properties: it captures the dynamical nature of the organism–environment system, it can be fitted with data, and it can be applied without detailed knowledge of the system. We study phenotypic plasticity using in silico experiments and demonstrate that the framework predicts the response to novel environmental signals. The framework allows us to model plasticity as a dynamical property that changes in time during ontogeny, reflecting the well-known fact that organisms are more or less plastic at different developmental stages.     

Comparison of conformational characteristics in structurally similar protein pairs.

Although it is known that three-dimensional structure is well conserved during the evolutionary development of proteins, there have been few studies that consider other parameters apart from divergence of the main-chain coordinates. In this study, we align the structures of 90 pairs of homologous proteins having sequence identities ranging from 5 to 100%. Their structures are compared as a function of sequence identity, including not only consideration of C alpha coordinates but also accessibility, Ooi numbers, secondary structure, and side-chain angles. We discuss how these properties change as the sequences become less similar. This will be of practical use in homology modeling, especially for modeling very distantly related or analogous proteins. We also consider how the average size and number of insertions and deletions vary as sequences diverge. This study presents further quantitative evidence that structure is remarkably well conserved in detail, as well as at the topological level, even when the sequences do not show similarity that is significant statistically.     

An automated method for modeling proteins on known templates using distance geometry.

We present an automated method incorporated into a software package, FOLDER, to fold a protein sequence on a given three-dimensional (3D) template. Starting with the sequence alignment of a family of homologous proteins, tertiary structures are modeled using the known 3D structure of one member of the family as a template. Homologous interatomic distances from the template are used as constraints. For nonhomologous regions in the model protein, the lower and the upper bounds for the interatomic distances are imposed by steric constraints and the globular dimensions of the template, respectively. Distance geometry is used to embed an ensemble of structures consistent with these distance bounds. Structures are selected from this ensemble based on minimal distance error criteria, after a penalty function optimization step. These structures are then refined using energy optimization methods. The method is tested by simulating the alpha-chain of horse hemoglobin using the alpha-chain of human hemoglobin as the template and by comparing the generated models with the crystal structure of the alpha-chain of horse hemoglobin. We also test the packing efficiency of this method by reconstructing the atomic positions of the interior side chains beyond C beta atoms of a protein domain from a known 3D structure. In both test cases, models retain the template constraints and any additionally imposed constraints while the packing of the interior residues is optimized with no short contacts or bond deformations. To demonstrate the use of this method in simulating structures of proteins with nonhomologous disulfides, we construct a model of murine interleukin (IL)-4 using the NMR structure of human IL-4 as the template. The resulting geometry of the nonhomologous disulfide in the model structure for murine IL-4 is consistent with standard disulfide geometry.     

Structural analysis based on state-space modeling.

A new method has been developed to compute the probability that each amino acid in a protein sequence is in a particular secondary structural element. Each of these probabilities is computed using the entire sequence and a set of predefined structural class models. This set of structural classes is patterned after Jane Richardson''s taxonomy for the domains of globular proteins. For each structural class considered, a mathematical model is constructed to represent constraints on the pattern of secondary structural elements characteristic of that class. These are stochastic models having discrete state spaces (referred to as hidden Markov models by researchers in signal processing and automatic speech recognition). Each model is a mathematical generator of amino acid sequences; the sequence under consideration is modeled as having been generated by one model in the set of candidates. The probability that each model generated the given sequence is computed using a filtering algorithm. The protein is then classified as belonging to the structural class having the most probable model. The secondary structure of the sequence is then analyzed using a "smoothing" algorithm that is optimal for that structural class model. For each residue position in the sequence, the smoother computes the probability that the residue is contained within each of the defined secondary structural elements of the model. This method has two important advantages: (1) the probability of each residue being in each of the modeled secondary structural elements is computed using the totality of the amino acid sequence, and (2) these probabilities are consistent with prior knowledge of realizable domain folds as encoded in each model. As an example of the method''s utility, we present its application to flavodoxin, a prototypical alpha/beta protein having a central beta-sheet, and to thioredoxin, which belongs to a similar structural class but shares no significant sequence similarity.     

Modeling of protein loops by simulated annealing.

A method is presented to model loops of protein to be used in homology modeling of proteins. This method employs the ESAP program of Higo et al. (Higo, J., Collura, V., & Garnier, J., 1992, Biopolymers 32, 33-43) and is based on a fast Monte Carlo simulation and a simulated annealing algorithm. The method is tested on different loops or peptide segments from immunoglobulin, bovine pancreatic trypsin inhibitor, and bovine trypsin. The predicted structure is obtained from the ensemble average of the coordinates of the Monte Carlo simulation at 300 K, which exhibits the lowest internal energy. The starting conformation of the loop prior to modeling is chosen to be completely extended, and a closing harmonic potential is applied to N, CA, C, and O atoms of the terminal residues. A rigid geometry potential of Robson and Platt (1986, J. Mol. Biol. 188, 259-281) with a united atom representation is used. This we demonstrate to yield a loop structure with good hydrogen bonding and torsion angles in the allowed regions of the Ramachandran map. The average accuracy of the modeling evaluated on the eight modeled loops is 1 A root mean square deviation (rmsd) for the backbone atoms and 2.3 A rmsd for all heavy atoms.