Predicting impacts of climate change on biodiversity: a role for semi‐mechanistic community‐level modelling

Aim Robust and reliable predictions of the effects of climate change on biodiversity are required in formulating conservation and management strategies that best retain biodiversity into the future. Significant challenges in modelling climate change impacts arise from limitations in our current knowledge of biodiversity. Community‐level modelling can complement species‐level approaches in overcoming these limitations and predicting climate change impacts on biodiversity as a whole. However, the community‐level approaches applied to date have been largely correlative, ignoring the key processes that influence change in biodiversity over space and time. Here, we suggest that the development of new ‘semi‐mechanistic’ community‐level models would substantially increase our capacity to predict climate change impacts on biodiversity. Location Global. Methods Drawing on an expansive review of biodiversity modelling approaches and recent advances in semi‐mechanistic modelling at the species level, we outline the main elements of a new semi‐mechanistic community‐level modelling approach. Results Our quantitative review revealed a sharp divide between mechanistic and non‐mechanistic biodiversity modelling approaches, with very few semi‐mechanistic models developed to date. Main conclusions We suggest that the conceptual framework presented here for combining mechanistic and non‐mechanistic community‐level approaches offers a promising means of incorporating key processes into predictions of climate change impacts on biodiversity whilst working within the limits of our current knowledge.     

Causation at different levels: tracking the commitments of mechanistic explanations

This paper tracks the commitments of mechanistic explanations focusing on the relation between activities at different levels. It is pointed out that the mechanistic approach is inherently committed to identifying causal connections at higher levels with causal connections at lower levels. For the mechanistic approach to succeed a mechanism as a whole must do the very same thing what its parts organised in a particular way do. The mechanistic approach must also utilise bridge principles connecting different causal terms of different theoretical vocabularies in order to make the identities of causal connections transparent. These general commitments get confronted with two claims made by certain proponents of the mechanistic approach: William Bechtel often argues that within the mechanistic framework it is possible to balance between reducing higher levels and maintaining their autonomy at the same time, whereas, in a recent paper, Craver and Bechtel argue that the mechanistic approach is able to make downward causation intelligible. The paper concludes that the mechanistic approach imbued with identity statements is no better candidate for anchoring higher levels to lower ones while maintaining their autonomy at the same time than standard reductive accounts are, and that what mechanistic explanations are able to do at best is showing that downward causation does not exist.     

The range implications of lizard traits in changing environments

Aim Most predictions of species ranges are based on correlating current species localities to environmental conditions. These correlative models do not explicitly include a species' biology. In contrast, some mechanistic models link traits to energetics and population dynamics to predict species distributions. These models enable one to ask whether considering a species' biology is important for predicting its range. I implement mechanistic models to investigate how a species' morphology, physiology and life history influence its range. Location North America. Methods I compare the mechanistic model predictions with those of correlative models for eight species of North American lizards in both current environments and following a uniform 3 °C temperature warming. I then examine the implications of superimposing habitat and elevation requirements on constraints associated with environmental tolerances. Results In the mechanistic model, species with a narrower thermal range for activity are both predicted and observed to have more restricted distributions. Incorporating constraints on habitat and elevation further restricts species distributions beyond areas that are thermally suitable. While correlative models generally outperform mechanistic models at predicting current distributions, the performance of mechanistic models improves when incorporating additional factors. In response to a 3 °C temperature warming, the northward range shifts predicted by the mechanistic model vary between species according to trait differences and are of a greater extent than those predicted by correlative models. Main conclusions These findings highlight the importance of species traits for understanding the dynamics of species ranges in changing environments. The analysis demonstrates that mechanistic models may provide an important complement to correlative models for predicting range dynamics, which may underpredict climate‐induced range shifts.     

Mechanists Must be Holists Too! Perspectives from Circadian Biology

The pursuit of mechanistic explanations in biology has produced a great deal of knowledge about the parts, operations, and organization of mechanisms taken to be responsible for biological phenomena. Holist critics have often raised important criticisms of proposed mechanistic explanations, but until recently holists have not had alternative research strategies through which to advance explanations. This paper argues both that the results of mechanistic strategies has forced mechanists to confront ways in which whole systems affect their components and that new representational and modeling strategies are providing tools for understanding these effects of whole systems upon components. Drawing from research on the mechanism responsible for circadian rhythms in mammals, I develop two examples in which mechanistic analysis is being integrated into a more holist perspective: research revealing intercellular integration of circadian mechanisms with those involved in cell metabolism and research revealing that stable?rhythms are dependent on how individual cells in the suprachiasmatic nucleus synchronize with each other to generate regular rhythms. Tools such as network diagramming and computational modeling are providing means to integrate mechanistic models into accounts of whole systems.     

Hybrid neural network modeling of a full-scale industrial wastewater treatment process

In recent years, hybrid neural network approaches, which combine mechanistic and neural network models, have received considerable attention. These approaches are potentially very efficient for obtaining more accurate predictions of process dynamics by combining mechanistic and neural network models in such a way that the neural network model properly accounts for unknown and nonlinear parts of the mechanistic model. In this work, a full-scale coke-plant wastewater treatment process was chosen as a model system. Initially, a process data analysis was performed on the actual operational data by using principal component analysis. Next, a simplified mechanistic model and a neural network model were developed based on the specific process knowledge and the operational data of the coke-plant wastewater treatment process, respectively. Finally, the neural network was incorporated into the mechanistic model in both parallel and serial configurations. Simulation results showed that the parallel hybrid modeling approach achieved much more accurate predictions with good extrapolation properties as compared with the other modeling approaches even in the case of process upset caused by, for example, shock loading of toxic compounds. These results indicate that the parallel hybrid neural modeling approach is a useful tool for accurate and cost-effective modeling of biochemical processes, in the absence of other reasonably accurate process models.     

Mechanistic reasoning and informed consent

Evidence‐based medicine (EBM) proponents have argued that mechanistic evidence concerning medical treatments should be considered secondary to evidence derived from randomized controlled trials (RCTs). One common criticism of RCTs is that they often do not yield results that are generalizable to clinical practice, and that for clinical practice application, mechanistic evidence is needed. However, proponents of EBM have argued that mechanistic reasoning is often unreliable and thus not very useful. Here we suggest an important role of mechanistic explanation that has been left out of this discussion entirely, namely, its importance in a patient’s decision of whether or not to take certain drugs. We argue that in certain cases, knowing how a treatment works is just as important for the patient as knowing whether it does. In this paper, we explore how and why giving patients mechanistic information can be an important factor in obtaining informed consent for medical treatment, focusing on the example case of hormonal contraceptives.     

Not all drift feeders are trout: a short review of fitness-based habitat selection models for fishes

Currently, there are few mechanistic fitness-based habitat selection models for stream fishes and most models used by management agencies focus on physical habitat alone. In this review, I describe the historical development and the status of mechanistic, fitness-based, habitat selection models for both water column (i.e., drift-feeding) and benthic stream fishes focusing on North America. Although the majority of drift feeders are not salmonids, most mechanistic habitat selection models have been developed and tested only in this group of fishes, likely due to their substantial economic importance. I review the fitness-based microhabitat selection model of Grossman et al. (Ecol Fresh Fish 11:2–10, 2002), which has been tested in both a salmonid and multiple cyprinid species. The model predicts optimal focal point velocities for drift feeders based on prey capture success–velocity relationships and does not include physiological costs, which are logistically difficult to quantify. In addition, I discuss mechanistic, fitness-based models used to predict microhabitat (i.e., patch) selection in benthic fishes. For both basic scientific and management/conservation perspectives, it is important to quantify habitat choice in fishes using mechanistic, fitness-based criteria.     

Carl F. Craver, Explaining what? Review of explaining the brain: mechanisms and the mosaic unity of neuroscience

Carl Craver’s recent book offers an account of the explanatory and theoretical structure of neuroscience. It depicts it as centered around the idea of achieving mechanistic understanding, i.e., obtaining knowledge of how a set of underlying components interacts to produce a given function of the brain. Its core account of mechanistic explanation and relevance is causal-manipulationist in spirit, and offers substantial insight into casual explanation in brain science and the associated notion of levels of explanation. However, the focus on mechanistic explanation leaves some open questions regarding the role of computation and cognition.     

The causal structure of mechanisms

Recently, a number of philosophers of science have claimed that much explanation in the sciences, especially in the biomedical and social sciences, is mechanistic explanation. I argue the account of mechanistic explanation provided in this tradition has not been entirely satisfactory, as it has neglected to describe in complete detail the crucial causal structure of mechanistic explanation. I show how the interventionist approach to causation, especially within a structural equations framework, provides a simple and elegant account of the causal structure of mechanisms. This account explains the many useful insights of traditional accounts of mechanism, such as Carl Craver’s account in his book Explaining the Brain (2007), but also helps to correct the omissions of such accounts. One of these omissions is the failure to provide an explicit formulation of a modularity constraint that plays a significant role in mechanistic explanation. One virtue of the interventionist/structural equations framework is that it allows for a simple formulation of a modularity constraint on mechanistic explanation. I illustrate the role of this constraint in the last section of the paper, which describes the form that mechanistic explanation takes in the computational, information-processing paradigm of cognitive psychology.     

Quantitative Comparison of Catalytic Mechanisms and Overall Reactions in Convergently Evolved Enzymes: Implications for Classification of Enzyme Function

Functionally analogous enzymes are those that catalyze similar reactions on similar substrates but do not share common ancestry, providing a window on the different structural strategies nature has used to evolve required catalysts. Identification and use of this information to improve reaction classification and computational annotation of enzymes newly discovered in the genome projects would benefit from systematic determination of reaction similarities. Here, we quantified similarity in bond changes for overall reactions and catalytic mechanisms for 95 pairs of functionally analogous enzymes (non-homologous enzymes with identical first three numbers of their EC codes) from the MACiE database. Similarity of overall reactions was computed by comparing the sets of bond changes in the transformations from substrates to products. For similarity of mechanisms, sets of bond changes occurring in each mechanistic step were compared; these similarities were then used to guide global and local alignments of mechanistic steps. Using this metric, only 44% of pairs of functionally analogous enzymes in the dataset had significantly similar overall reactions. For these enzymes, convergence to the same mechanism occurred in 33% of cases, with most pairs having at least one identical mechanistic step. Using our metric, overall reaction similarity serves as an upper bound for mechanistic similarity in functional analogs. For example, the four carbon-oxygen lyases acting on phosphates (EC 4.2.3) show neither significant overall reaction similarity nor significant mechanistic similarity. By contrast, the three carboxylic-ester hydrolases (EC 3.1.1) catalyze overall reactions with identical bond changes and have converged to almost identical mechanisms. The large proportion of enzyme pairs that do not show significant overall reaction similarity (56%) suggests that at least for the functionally analogous enzymes studied here, more stringent criteria could be used to refine definitions of EC sub-subclasses for improved discrimination in their classification of enzyme reactions. The results also indicate that mechanistic convergence of reaction steps is widespread, suggesting that quantitative measurement of mechanistic similarity can inform approaches for functional annotation.     

A top-down approach to mechanistic biological modeling: application to the single-chain antibody folding pathway

A top-down approach to mechanistic modeling of biological systems is presented and exemplified with the development of a hypothesis-driven mathematical model for single-chain antibody fragment (scFv) folding in Saccharomyces cerevisiae by mediators BiP and PDI. In this approach, model development starts with construction of the most basic mathematical model—typically consisting of predetermined or newly-elucidated biological behavior motifs—capable of reproducing desired biological behaviors. From this point, mechanistic detail is added incrementally and systematically, and the effects of each addition are evaluated. This approach follows the typical progression of experimental data availability in that higher-order, lumped measurements are often more prevalent initially than specific, mechanistic ones. It also necessarily provides the modeler with insight into the structural requirements and performance capabilities of the resulting detailed mechanistic model, which facilitates further analysis. The top-down approach to mechanistic modeling identified three such requirements and a branched dependency-degradation competition motif critical for the scFv folding model to reproduce experimentally observed scFv folding dependencies on BiP and PDI and increased production when both species are overexpressed and promoted straightforward prediction of parameter dependencies. It also prescribed modification of the guiding hypothesis to capture BiP and PDI synergy.     

Breaking out from a restricted range: Alternative habitat models to assess population perspectives

Habitat conservation for restricted-range species should also consider adjacent areas, but the analytical approaches for such assessments (particularly for a future perspective) are constrained by currently observed habitat relationships. We used two conceptually different habitat modelling approaches for analysing habitat distribution for the isolated Estonian population of a species of European conservation concern, the Siberian flying squirrel (Pteromys volans (Linnaeus, 1758)). We expected that the correlative (statistical) approaches based on current location data will increasingly deviate along with the distance from the current range, compared with a mechanistic approach based on limiting factors for the species. For conservation planning, we also investigated how the current protected area network covers quality habitats around the current range. We constructed three alternative correlative models (MaxEnt; Random forest; Generalized Boosted Regression) utilizing remote-sensing (Sentinel-2; LiDAR) and forest inventory data for 1299 occurrences in the currently occupied ca. 1400 km² range. A mechanistic model was constructed as a decision tree that distinguished 11 quality classes of forest land based on the ecological prioritization of limiting factors: site type; forest cover; abundance of key tree species; stand age; patch size; and layer structure. Supporting our expectation, an overall good accordance of habitat predictions of all the correlative models and the mechanistic model (at 30 × 30 m pixel size) declined with the distance from the current range. The MaxEnt model most closely followed the full range of habitat quality classes of the mechanistic model, while the other correlative models emphasized the highest habitat-quality class. Within the current range, both MaxEnt and the mechanistic model similarly revealed habitat quality differences between occupied and unoccupied species protection areas. Delineation of habitat aggregations all over the country based on the mechanistic model revealed habitat loss both within and adjacent to the current range, which sets limits to local population recovery. For analysing wider options, we recommend complementing statistical spatial modelling of current conditions with ecologically sound mechanistic approaches. Based on our specific case, we outline how such model predictions can be assessed for management planning beyond current range.     

Mechanistic considerations for general acid-base catalysis by RNA: revisiting the mechanism of the hairpin ribozyme

Several small ribozymes carry out self-cleavage at a specific phosphodiester bond to yield 2',3'-cyclic phosphate and 5'-hydroxyl termini. Prior mechanistic and structural studies on the HDV ribozymes led to the proposal that the pK(a) of C75 is shifted toward neutrality, making it an effective general acid. Recent mechanistic studies on the hairpin ribozyme have led to models in which protonation of G8 is required for phosphodiester cleavage, either for general acid catalysis or for electrostatic stabilization. Inspection of recent crystal structures of the hairpin ribozyme, including a complex with a vanadate transition state mimic, suggests an alternative model involving general acid-base catalysis with G8 serving as the general base and A38 as the general acid. This model is consistent with the literature on the hairpin ribozyme, including pH-rate profiles of wild-type and mutant ribozymes and solvent isotope effects. General mechanistic considerations for RNA catalysis suggest that the penalty for having general acids and bases with pK(a)s removed from neutrality is not as severe as expected. These considerations suggest that general acid-base catalysis may be a common mechanistic strategy of RNA enzymes.     

Revisiting heme mechanisms. A perspective on the mechanisms of nitric oxide synthase (NOS), Heme oxygenase (HO), and cytochrome P450s (CYP450s)

Despite the essential biological importance of reactions that involve heme, mechanisms of heme reactions in enzymes like nitric oxide synthase (NOS), heme oxygenase (HO), and cytochrome P450s (CYP450s) are still not well-understood. This Perspective on NOS, HO, and CYP450 mechanisms is written from the point of view of the heme chemistry. Steps in the classical heme catalytic cycle are discussed based on the specific environment within each of these enzymes. Elucidation of the mechanisms of NOS inactivation by some substrate analogues provides important mechanistic clues to the NOS catalytic mechanism. On the basis of mechanistic studies of NOS inactivation by amidine analogues of l-arginine and other previous mechanistic results, a new mechanism for NOS-catalyzed l-arginine NG-hydroxylation (the first half of the catalytic reaction) is proposed in this Perspective. The key step in the second half of the NOS catalytic reaction, the internal electron transfer between the substrate and heme, is discussed on the basis of mechanistic results of NOS inactivation by NG-allyl-l-arginine and the structures of the substrate intermediates. Elucidation of the mechanism of NOS inactivation by amidines, which leads to heme degradation, also provides important mechanistic implications for heme oxygenase-catalyzed heme catabolism. Focusing on the meso-hydroxylation step during inactivation of NOS by amidines as well as the HO-catalyzed reaction, the essential nature of the heme-oxygen species responsible for porphyrin meso-hydroxylation is discussed. Finally, on the basis of the proposed heme degradation mechanism during NOS inactivation and the HO-catalyzed reaction, the mechanism for the formation of the monooxygenated heme species in P450-catalyzed reactions is discussed.     

Parallel hybrid modeling methods for a full-scale cokes wastewater treatment plant

Parallel hybrid modeling methods are applied to a full-scale cokes wastewater treatment plant. Within the hybrid model structure, a mechanistic model specifies the basic dynamics of the relevant process and a non-parametric model compensates for the inaccuracy of the mechanistic model. First, a simplified mechanistic model is developed based on Activated Sludge Model No. 1 and the specific process knowledge of the cokes wastewater treatment process. Then, the mechanistic model is combined with five different non-parametric models--feedforward back-propagation neural network, radial basis function network, linear partial least squares (PLS), quadratic PLS and neural network PLS (NNPLS)--in parallel configuration. These models are identified with the same data obtained from the plant operation to predict dynamic behavior of the process. The performance of each parallel hybrid model is compared based on their ease of model building, prediction accuracy and interpretability. For this application, the parallel hybrid model with NNPLS as non-parametric model gives better performance than other parallel hybrid models. In addition, the NNPLS model is used to analyze the behavior of the operation data in the reduced space and allows for fault detection and isolation.     

Mechanisms,resources, and background conditions

Distinguishing mechanistic components from mere causally relevant background conditions remains a difficulty for mechanistic accounts of explanation. By distinguishing resources from mechanical parts, I argue that we can more effectively draw this boundary. Further, the distinction makes obvious that there are distinctive resource explanations which are not captured by a traditional part-based mechanistic account. While this suggests a straightforward extension of the mechanistic model, I argue that incorporating resources and resource explanations requires moving beyond the purely local account of levels that some mechanists advocate.     

Integrating mechanistic and correlative niche models to unravel range‐limiting processes in a temperate amphibian

Insights into the causal mechanisms that limit species distributions are likely to improve our ability to anticipate species range shifts in response to climate change. For species with complex life histories, a mechanistic understanding of how climate affects different lifecycle stages may be crucial for making accurate forecasts. Here, we use mechanistic niche modeling (NicheMapR) to derive “proximate” (mechanistic) variables for tadpole, juvenile, and adult Rana temporaria. We modeled the hydroperiod, and maximum and minimum temperatures of shallow (30 cm) ponds, as well as activity windows for juveniles and adults. We then used those (“proximate”) variables in correlative ecological niche models (Maxent) to assess their role in limiting the species’ current distribution, and to investigate the potential effects of climate change on R. temporaria across Europe. We further compared the results with a model based on commonly used macroclimatic (“distal”) layers (i.e., bioclimatic layers from WorldClim). The maximum temperature of the warmest month (a macroclimatic variable) and maximum pond temperatures (a mechanistic variable) were the most important range‐limiting factors, and maximum temperature thresholds were consistent with the observed upper thermal limit of R. temporaria tadpoles. We found that range shift forecasts in central Europe are far more pessimistic when using distal macroclimatic variables, compared to projections based on proximate mechanistic variables. However, both approaches predicted extensive decreases in climatic suitability in southern Europe, which harbors a significant fraction of the species’ genetic diversity. We show how mechanistic modeling provides ways to depict gridded layers that directly reflect the microenvironments experienced by organisms at continental scales, and to reconstruct those predictors without extrapolation under novel future conditions. Furthermore, incorporating those predictors in correlative ecological niche models can help shed light on range‐limiting processes, and can have substantial impacts on predictions of climate‐induced range shifts.     

In vitro and in vivo single-molecule fluorescence imaging of ribosome-catalyzed protein synthesis

Combined with the availability of highly purified, fluorescently labeled in vitro translation systems, the advent of single-molecule fluorescence imaging has ushered in a new era in high-resolution mechanistic studies of ribosome-catalyzed protein synthesis, or translation. Together with ensemble biochemical investigations of translation and structural studies of functional ribosomal complexes, in vitro single-molecule fluorescence imaging of protein synthesis is providing unique mechanistic insight into this fundamental biological process. More recently, rapidly evolving breakthroughs in fluorescence-based molecular imaging in live cells with sub-diffraction-limit spatial resolution and ever-increasing temporal resolution provide great promise for conducting mechanistic studies of translation and its regulation in living cells. Here we review the remarkable recent progress that has been made in these fields, highlight important mechanistic insights that have been gleaned from these studies thus far, and discuss what we envision lies ahead as these approaches continue to evolve and expand to address increasingly complex mechanistic and regulatory aspects of translation.     

Chromatographic parameter determination for complex biological feedstocks

The application of mechanistic models for chromatography requires accurate model parameters. Especially for complex feedstocks such as a clarified cell harvest, this can still be an obstacle limiting the use of mechanistic models. Another commonly encountered obstacle is a limited amount of sample material and time to determine all needed parameters. Therefore, this study aimed at implementing an approach on a robotic liquid handling system that starts directly with a complex feedstock containing a monoclonal antibody. The approach was tested by comparing independent experimental data sets with predictions generated by the mechanistic model using all parameters determined in this study. An excellent agreement between prediction and experimental data was found verifying the approach. Thus, it can be concluded that RoboColumns with a bed volume of 200 μL can well be used to determine isotherm parameters for predictions of larger scale columns. Overall, this approach offers a new way to determine crucial model input parameters for mechanistic modelling of chromatography for complex biological feedstocks. © 2018 The Authors Biotechnology Progress published by Wiley Periodicals, Inc. on behalf of American Institute of Chemical Engineers Biotechnol. Prog., 34:1006–1018, 2018