An integrated risk assessment framework using information theory-based coupling methods for basin-scale water quality management: A case study in the Danjiangkou Reservoir Basin, China.

As the second largest reservoir in China, the Danjiangkou Reservoir (DJKR) serves as the water source of the Middle Route of the South-to-North Water Diversion Project of China (MRSNWDPC), i.e., the currently longest (1273 km) inter-basin water diversion project in the world, for more than eight years. The water quality status of the DJKR basin has been receiving worldwide attention because it is related to the health and safety of >100 million people and the integrity of an ecosystem covering >92,500 km2. In this study, basin-scale water quality sampling campaigns were conducted monthly at 47 monitoring sites in river systems of the DJKRB from the year 2020 to 2022, covering nine water quality indicators, i.e., water temperature (WT), pH, dissolved oxygen (DO), permanganate index (CODMn), five-day biochemical oxygen demand (BOD5), ammonia nitrogen (NH3-N), total phosphorus (TP), total nitrogen (TN), and fluoride (F-). The water quality index (WQI) and multivariate statistical techniques were introduced to comprehensively evaluate water quality status and understand the corresponding driving factors of water quality variations. An integrated risk assessment framework simultaneously considered intra and inter-regional factors using information theory-based and the SPA (Set-Pair Analysis) methods were proposed for basin-scale water quality management. The results showed that the water quality of the DJKR and its tributaries stably maintained a "good" status, with all the average WQIs >60 of river systems during the monitoring period. The spatial variations of all WQIs in the basin showed significantly different (Kruskal-Wallis tests, P < 0.01), while no seasonal differences were found. The increase in built-up land use and agricultural water consumption revealed the highest contributions (Mantel's r > 0.5, P < 0.05) to the rise of nutrient loadings of all river systems, showing the intensive anthropogenic activities can eclipse the power of natural processes on water quality variations to some extent. The risks of specific sub-basins that may cause water quality degradation on the MRSNWDPC were effectively quantified and identified into five classifications based on transfer entropy and the SPA methods. This study provides an informative risk assessment framework that was relatively easy to be applied by professionals and non-experts for basin-scale water quality management, thus providing a valuable and reliable reference for the administrative department to conduct effective pollution control in the future.

An information theory approach to biocultural complexity.

The study of biocultural diversity requires the use of appropriate concepts and analytical tools. Particularly, there is a need of indices capable to show the degree of stratification in the set of interactions among cultures and groups of plants and animals in a given region. Here, we present a mathematical approach based on the mutual Shannon information theory to study the relationships among cultural and biological groups. Biocultural complexity was described in terms of effective biocultural units, a new concept defined in this work. From the mathematical formulation of biocultural complexity, formulas were derived to measure the specificity of biological groups and the specialization of cultures, based on the association of human societies with plant or animal groups. To exemplify the concepts and tools, two data sets were analyzed; 1) a set that included artificial data in order to demonstrate the use of the formulas and calculate the indices, and 2) a set that included published data on the use of 18 mushroom species by people in five villages of eastern India. Analysis of the first data set revealed a clear case of biocultural complexity, whereas that of the second set showed that the villages and the use of biological resources composed a single biocultural unit. Overall, hypothesis testing of the association among cultures and biological species was consistent with the information that was provided by the new indices.

Frequency-specific brain dynamics related to prediction during language comprehension.

The brain's remarkable capacity to process spoken language virtually in real time requires fast and efficient information processing machinery. In this study, we investigated how frequency-specific brain dynamics relate to models of probabilistic language prediction during auditory narrative comprehension. We recorded MEG activity while participants were listening to auditory stories in Dutch. Using trigram statistical language models, we estimated for every word in a story its conditional probability of occurrence. On the basis of word probabilities, we computed how unexpected the current word is given its context (word perplexity) and how (un)predictable the current linguistic context is (word entropy). We then evaluated whether source-reconstructed MEG oscillations at different frequency bands are modulated as a function of these language processing metrics. We show that theta-band source dynamics are increased in high relative to low entropy states, likely reflecting lexical computations. Beta-band dynamics are increased in situations of low word entropy and perplexity possibly reflecting maintenance of ongoing cognitive context. These findings lend support to the idea that the brain engages in the active generation and evaluation of predicted language based on the statistical properties of the input signal.

Information theory to tachycardia therapy: electrogram entropy predicts diastolic microstructure of reentrant ventricular tachycardia.

There is no known strategy to differentiate which multicomponent electrograms in sinus rhythm maintain reentrant ventricular tachycardia (VT). Low entropy in the voltage breakdown of a multicomponent electrogram can localize conditions suitable for reentry but has not been validated against the classic VT activation mapping. We examined whether low entropy in a late and diversely activated ventricular scar region characterizes and differentiates the diastolic path of VT and represents protected tissue channels devoid of side branches. Intraoperative bipolar electrogram (BiEGM) activation and entropy maps were obtained during sinus rhythm in 17 patients with ischemic cardiomyopathy and compared with diastolic activation paths of VT (total of 39 VTs). Mathematical modeling of a zigzag main channel with side branches was also used to further validate structural representation of low entropy in the ventricular scar. A median of one region per patient (range: 1-2 regions) was identified in sinus rhythm, in which BiEGM with the latest mean activation time and adjacent minimum entropy were assembled together in a high-activation dispersion region. These regions accurately recognized diastolic paths of 34 VTs, often to multiple inducible VTs within a single individual arrhythmogenic region. In mathematical modeling, side branching from the main channel had a strong influence on the BiEGM composition along the main channel. The BiEGM obtained from a long unbranched channel had the lowest entropy compared with those with multiple side branches. In conclusion, among a population of multicomponent sinus electrograms, those that demonstrate low entropy and are delayed colocalize to critical long-protected channels of VT. This information is pertinent for planning VT ablation in sinus rhythm. NEW & NOTEWORTHY Entropy is a measure to quantify breakdown in information. Electrograms from a protected tissue channel can only possess a few states in their voltage and thus less information. In contrast, current-load interactions from side branches in unprotected channels introduce a number of dissimilar voltage deflections and thus high information. We compare here a mapping approach based on entropy against a rigorous reference standard of activation mapping during VT and entropy was assessed in sinus rhythm.

EEG Classification with a Sequential Decision-Making Method in Motor Imagery BCI.

To develop subject-specific classifier to recognize mental states fast and reliably is an important issue in brain-computer interfaces (BCI), particularly in practical real-time applications such as wheelchair or neuroprosthetic control. In this paper, a sequential decision-making strategy is explored in conjunction with an optimal wavelet analysis for EEG classification. The subject-specific wavelet parameters based on a grid-search method were first developed to determine evidence accumulative curve for the sequential classifier. Then we proposed a new method to set the two constrained thresholds in the sequential probability ratio test (SPRT) based on the cumulative curve and a desired expected stopping time. As a result, it balanced the decision time of each class, and we term it balanced threshold SPRT (BTSPRT). The properties of the method were illustrated on 14 subjects' recordings from offline and online tests. Results showed the average maximum accuracy of the proposed method to be 83.4% and the average decision time of 2.77[Formula: see text]s, when compared with 79.2% accuracy and a decision time of 3.01[Formula: see text]s for the sequential Bayesian (SB) method. The BTSPRT method not only improves the classification accuracy and decision speed comparing with the other nonsequential or SB methods, but also provides an explicit relationship between stopping time, thresholds and error, which is important for balancing the speed-accuracy tradeoff. These results suggest that BTSPRT would be useful in explicitly adjusting the tradeoff between rapid decision-making and error-free device control.

Aging, frailty and complex networks.

When people age their mortality rate increases exponentially, following Gompertz's law. Even so, individuals do not die from old age. Instead, they accumulate age-related illnesses and conditions and so become increasingly vulnerable to death from various external and internal stressors. As a measure of such vulnerability, frailty can be quantified using the frailty index (FI). Larger values of the FI are strongly associated with mortality and other adverse health outcomes. This association, and the insensitivity of the FI to the particular health variables that are included in its construction, makes it a powerful, convenient, and increasingly popular integrative health measure. Still, little is known about why the FI works so well. Our group has recently developed a theoretical network model of health deficits to better understand how changes in health are captured by the FI. In our model, health-related variables are represented by the nodes of a complex network. The network has a scale-free shape or "topology": a few nodes have many connections with other nodes, whereas most nodes have few connections. These nodes can be in two states, either damaged or undamaged. Transitions between damaged and non-damaged states are governed by the stochastic environment of individual nodes. Changes in the degree of damage of connected nodes change the local environment and make further damage more likely. Our model shows how age-dependent acceleration of the FI and of mortality emerges, even without specifying an age-damage relationship or any other time-dependent parameter. We have also used our model to assess how informative individual deficits are with respect to mortality. We find that the information is larger for nodes that are well connected than for nodes that are not. The model supports the idea that aging occurs as an emergent phenomenon, and not as a result of age-specific programming. Instead, aging reflects how damage propagates through a complex network of interconnected elements.

Information Processing in the Mental Workspace Is Fundamentally Distributed.

The brain is a complex, interconnected information processing network. In humans, this network supports a mental workspace that enables high-level abilities such as scientific and artistic creativity. Do the component processes underlying these abilities occur in discrete anatomical modules, or are they distributed widely throughout the brain? How does the flow of information within this network support specific cognitive functions? Current approaches have limited ability to answer such questions. Here, we report novel multivariate methods to analyze information flow within the mental workspace during visual imagery manipulation. We find that mental imagery entails distributed information flow and shared representations throughout the cortex. These findings challenge existing, anatomically modular models of the neural basis of higher-order mental functions, suggesting that such processes may occur at least in part at a fundamentally distributed level of organization. The novel methods we report may be useful in studying other similarly complex, high-level informational processes.

Information theoretic evaluation of a noiseband-based cochlear implant simulator.

Noise-band vocoders are often used to simulate the signal processing algorithms used in cochlear implants (CIs), producing acoustic stimuli that may be presented to normal hearing (NH) subjects. Such evaluations may obviate the heterogeneity of CI user populations, achieving greater experimental control than when testing on CI subjects. However, it remains an open question whether advancements in algorithms developed on NH subjects using a simulator will necessarily improve performance in CI users. This study assessed the similarity in vowel identification of CI subjects and NH subjects using an 8-channel noise-band vocoder simulator configured to match input and output frequencies or to mimic output after a basalward shift of input frequencies. Under each stimulus condition, NH subjects performed the task both with and without feedback/training. Similarity of NH subjects to CI users was evaluated using correct identification rates and information theoretic approaches. Feedback/training produced higher rates of correct identification, as expected, but also resulted in error patterns that were closer to those of the CI users. Further evaluation remains necessary to determine how patterns of confusion at the token level are affected by the various parameters in CI simulators, providing insight into how a true CI simulation may be developed to facilitate more rapid prototyping and testing of novel CI signal processing and electrical stimulation strategies.

A Neural Substrate for Rapid Timbre Recognition? Neural and Behavioral Discrimination of Very Brief Acoustic Vowels.

The timbre of a sound plays an important role in our ability to discriminate between behaviorally relevant auditory categories, such as different vowels in speech. Here, we investigated, in the primary auditory cortex (A1) of anesthetized guinea pigs, the neural representation of vowels with impoverished timbre cues. Five different vowels were presented with durations ranging from 2 to 128 ms. A psychophysical experiment involving human listeners showed that identification performance was near ceiling for the longer durations and degraded close to chance level for the shortest durations. This was likely due to spectral splatter, which reduced the contrast between the spectral profiles of the vowels at short durations. Effects of vowel duration on cortical responses were well predicted by the linear frequency responses of A1 neurons. Using mutual information, we found that auditory cortical neurons in the guinea pig could be used to reliably identify several vowels for all durations. Information carried by each cortical site was low on average, but the population code was accurate even for durations where human behavioral performance was poor. These results suggest that a place population code is available at the level of A1 to encode spectral profile cues for even very short sounds.

Serum trace element differences between Schizophrenia patients and controls in the Han Chinese population.

Little is known about the trace element profile differences between Schizophrenia patients and healthy controls; previous studies about the association of certain elements with Schizophrenia have obtained conflicting results. To identify these differences in the Han Chinese population, inductively coupled plasma-mass spectrometry was used to quantify the levels of 35 elements in the sera of 111 Schizophrenia patients and 110 healthy participants, which consisted of a training (61/61 for cases/controls included) and a test group including remaining participants. An orthogonal projection to latent structures model was constructed from the training group (R(2)Y = 0.465, Q(2)cum = 0.343) had a sensitivity of 76.0% and a specificity of 71.4% in the test group. Single element analysis indicated that the concentrations of cesium, zinc, and selenium were significantly reduced in patients with Schizophrenia in both the training and test groups. The meta-analysis including 522 cases and 360 controls supported that Zinc was significantly associated with Schizophrenia (standardized mean difference [SMD], -0.81; 95% confidence intervals [CI], -1.46 to -0.16, P = 0.01) in the random-effect model. Information theory analysis indicated that Zinc could play roles independently in Schizophrenia. These results suggest clear element profile differences between patients with Schizophrenia and healthy controls, and reduced Zn level is confirmed in the Schizophrenia patients.

Position-specific prediction of methylation sites from sequence conservation based on information theory.

Protein methylation plays vital roles in many biological processes and has been implicated in various human diseases. To fully understand the mechanisms underlying methylation for use in drug design and work in methylation-related diseases, an initial but crucial step is to identify methylation sites. The use of high-throughput bioinformatics methods has become imperative to predict methylation sites. In this study, we developed a novel method that is based only on sequence conservation to predict protein methylation sites. Conservation difference profiles between methylated and non-methylated peptides were constructed by the information entropy (IE) in a wider neighbor interval around the methylation sites that fully incorporated all of the environmental information. Then, the distinctive neighbor residues were identified by the importance scores of information gain (IG). The most representative model was constructed by support vector machine (SVM) for Arginine and Lysine methylation, respectively. This model yielded a promising result on both the benchmark dataset and independent test set. The model was used to screen the entire human proteome, and many unknown substrates were identified. These results indicate that our method can serve as a useful supplement to elucidate the mechanism of protein methylation and facilitate hypothesis-driven experimental design and validation.

Time-frequency analysis of resting state and evoked EEG data recorded at higher magnetic fields up to 9.4 T.

BACKGROUND: Combining both high temporal and spatial resolution by means of simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) is of relevance to neuroscientists. This combination, however, leads to a distortion of the EEG signal by the so-called cardio-ballistic artefacts. The aim of the present study was developing an approach to restore meaningful physiological EEG data from recordings at different magnetic fields. NEW METHODS: The distortions introduced by the magnetic field were corrected using a combination of concepts from independent component analysis (ICA) and mutual information (MI). Thus, the components were classified as either related to the cardio-ballistic artefacts or to the signals of interest. EEG data from two experimental paradigms recorded at different magnetic field strengths up to 9.4 T were analyzed: (i) spontaneous activity using an eyes-open/eyes-closed alternation, and (ii) responses to auditory stimuli, i.e. auditory evoked potentials. RESULTS: Even at ultra-high magnetic fields up to 9.4 T the proposed artefact rejection approach restored the physiological time-frequency information contained in the signal of interest and the data were suitable for subsequent analyses. COMPARISON WITH EXISTING METHODS: Blind source separation (BSS) has been used to retrieve information from EEG data recorded inside the MR scanner in previous studies. After applying the presented method on EEG data recorded at 4 T, 7 T, and 9.4 T, we could retrieve more information than from data cleaned with the BSS method. CONCLUSIONS: The present work demonstrates that EEG data recorded at ultra-high magnetic fields can be used for studying neuroscientific research question related to oscillatory activity.

Visual attention mitigates information loss in small- and large-scale neural codes.

The visual system transforms complex inputs into robust and parsimonious neural codes that efficiently guide behavior. Because neural communication is stochastic, the amount of encoded visual information necessarily decreases with each synapse. This constraint requires that sensory signals are processed in a manner that protects information about relevant stimuli from degradation. Such selective processing--or selective attention--is implemented via several mechanisms, including neural gain and changes in tuning properties. However, examining each of these effects in isolation obscures their joint impact on the fidelity of stimulus feature representations by large-scale population codes. Instead, large-scale activity patterns can be used to reconstruct representations of relevant and irrelevant stimuli, thereby providing a holistic understanding about how neuron-level modulations collectively impact stimulus encoding.

Corticothalamic Synaptic Noise as a Mechanism for Selective Attention in Thalamic Neurons.

A reason why the thalamus is more than a passive gateway for sensory signals is that two-third of the synapses of thalamocortical neurons are directly or indirectly related to the activity of corticothalamic axons. While the responses of thalamocortical neurons evoked by sensory stimuli are well characterized, with ON- and OFF-center receptive field structures, the prevalence of synaptic noise resulting from neocortical feedback in intracellularly recorded thalamocortical neurons in vivo has attracted little attention. However, in vitro and modeling experiments point to its critical role for the integration of sensory signals. Here we combine our recent findings in a unified framework suggesting the hypothesis that corticothalamic synaptic activity is adapted to modulate the transfer efficiency of thalamocortical neurons during selective attention at three different levels: First, on ionic channels by interacting with intrinsic membrane properties, second at the neuron level by impacting on the input-output gain, and third even more effectively at the cell assembly level by boosting the information transfer of sensory features encoded in thalamic subnetworks. This top-down population control is achieved by tuning the correlations in subthreshold membrane potential fluctuations and is adapted to modulate the transfer of sensory features encoded by assemblies of thalamocortical relay neurons. We thus propose that cortically-controlled (de-)correlation of subthreshold noise is an efficient and swift dynamic mechanism for selective attention in the thalamus.

Information-bearing acoustic change outperforms duration in predicting intelligibility of full-spectrum and noise-vocoded sentences.

Recent research has demonstrated a strong relationship between information-bearing acoustic changes in the speech signal and speech intelligibility. The availability of information-bearing acoustic changes reliably predicts intelligibility of full-spectrum [Stilp and Kluender (2010). Proc. Natl. Acad. Sci. U.S.A. 107(27), 12387-12392] and noise-vocoded sentences amid noise interruption [Stilp et al. (2013). J. Acoust. Soc. Am. 133(2), EL136-EL141]. However, other research reports that proportion of signal duration preserved also predicts intelligibility of noise-interrupted speech. These factors have only ever been investigated independently, obscuring whether one better explains speech perception. The present experiments manipulated both factors to answer this question. A broad range of sentence durations (160-480 ms) containing high or low information-bearing acoustic changes were replaced by speech-shaped noise in noise-vocoded (Experiment 1) and full-spectrum sentences (Experiment 2). Sentence intelligibility worsened with increasing noise replacement, but in both experiments, information-bearing acoustic change was a statistically superior predictor of performance. Perception relied more heavily on information-bearing acoustic changes in poorer listening conditions (in spectrally degraded sentences and amid increasing noise replacement). Highly linear relationships between measures of information and performance suggest that exploiting information-bearing acoustic change is a shared principle underlying perception of acoustically rich and degraded speech. Results demonstrate the explanatory power of information-theoretic approaches for speech perception.

Synchrony: a spiking-based mechanism for processing sensory stimuli.

Synchronous behaviour of neurons is both beneficial and detrimental to the neural code. On the one extreme, synchronous firing activity is well known to be a symptom of epileptic seizures, whilst on the other synchrony provides a mechanism for coordinating brain activity. This paper briefly reviews some current thinking with regard to synchrony, and outlines some experiments with LIF neurons that harness near-synchronous states for processing biologically-realistic sensory stimuli. Inspired by the topology of neurons in the cochlear nucleus, laterally connected leaky integrate and fire neurons, operating in near-synchronous states, are investigated for their ability to reduce noisy spikes and increase spectral contrast of auditory stimuli. Two connectivity parameters, referred to as connection length and neighbourhood radius, are introduced to configure lateral inhibitory connectivity to generate this neural behaviour. Information-theoretic principles are then employed to quantify the information retained by the coding, and then this is compared to the information retained by the various output topologies.

High capacity optical channels for bioinformation transfer: acupuncture meridians.

Mammalian bodies are hierarchical systems whose internal cooperation and coherent activity require high capacity information transfer between the central control unit--the brain--and the periphery--the organs. A communication system capable of meeting information capacity requirements should be based on transmission of electromagnetic signals. Structures that fulfill requirements for such information transfer have not yet been analyzed. Acupuncture meridians have been demonstrated experimentally in some animals. They might represent systems of information transfer between the brain and the peripheral organs. The ducts of the meridians may correspond to optical fibers operating from the far infrared to the visible wavelength region. The main features of a model of the duct as an optical fiber are delineated and its properties outlined. However, to analyze essentials of the transmission capabilities, the whole meridian structure should be mapped and a more comprehensive set of physical parameters measured. In particular, experimental data concerning morphological arrangements of ordered water in the ducts and corpuscles, and a complete content of the biological particles in the flowing water and its permittivity are missing.

Summarization of clinical information: a conceptual model.

BACKGROUND: To provide high-quality and safe care, clinicians must be able to optimally collect, distill, and interpret patient information. Despite advances in text summarization, only limited research exists on clinical summarization, the complex and heterogeneous process of gathering, organizing and presenting patient data in various forms. OBJECTIVE: To develop a conceptual model for describing and understanding clinical summarization in both computer-independent and computer-supported clinical tasks. DESIGN: Based on extensive literature review and clinical input, we developed a conceptual model of clinical summarization to lay the foundation for future research on clinician workflow and automated summarization using electronic health records (EHRs). RESULTS: Our model identifies five distinct stages of clinical summarization: (1) Aggregation, (2) Organization, (3) Reduction and/or Transformation, (4) Interpretation and (5) Synthesis (AORTIS). The AORTIS model describes the creation of complex, task-specific clinical summaries and provides a framework for clinical workflow analysis and directed research on test results review, clinical documentation and medical decision-making. We describe a hypothetical case study to illustrate the application of this model in the primary care setting. CONCLUSION: Both practicing physicians and clinical informaticians need a structured method of developing, studying and evaluating clinical summaries in support of a wide range of clinical tasks. Our proposed model of clinical summarization provides a potential pathway to advance knowledge in this area and highlights directions for further research.

[Mathematical analysis of characteristics of glucocorticoid-induced yang deficiency or yin deficiency syndrome in animal models based on information entropy theory].

OBJECTIVE: Based on information entropy theory, this study analyzes the experimental indicators of glucocorticoid-induced traditional Chinese medicine yang deficiency or yin deficiency syndrome in animal models, thus laying the foundation of defining and interpreting the model state. METHODS: Data of biochemical indicators from 24 groups of animal models with different modeling conditions (type of modeling agent, dosage of modeling agent and modeling time) were collected. Information gain values for three study objects (type of modeling agent, dosage of modeling agent and modeling time) were calculated respectively after standardization, and then characteristics of yang deficiency or yin deficiency syndrome models were interpreted with these values and ranking map. RESULTS: Greatest information gain values of most indicators were got when the study object was the type of modeling agent, which is the most important factor in the differentiation of model state. With this study object, triglyceride (TG), total cholesterol (TC), total protein (TP), albumin (ALB), alanine aminotransferase (ALT), aspartate aminotransferase (AST), and corticosteroid (CS) got larger information gain values than testosterone (T), lactic dehydrogenase (LDH), and cyclic adenosine monophosphate (cAMP). This indicated that the former seven indicators may be significantly different between the two animal models induced by hydrocortisone and dexamethasone, respectively. In the study of the modeling time, AST may be affected more than others. In the study of the dosage of modeling agent, TC and ALB may be affected more in the model of hydrocortisone and no indicators were significantly affected by the dosage of modeling agent in the dexamethasone model. CONCLUSION: Mathematical method based on information entropy theory allows researchers to analyze experimental data in several experiments at the same time and to interpret the trend and stability of indicators. Based on this study and by analyzing the impacts of different modeling conditions on the indicators, elucidations of the characteristics of glucocorticoid-induced yang deficiency or yin deficiency syndrome in animal models and some biochemical indicators for model comparison are in need.

Information theory and the neuropeptidergic regulation of seasonal reproduction in mammals and birds.

Seasonal breeding in the temperate zone is a dramatic example of a naturally occurring change in physiology and behaviour. Cues that predict periods of environmental amelioration favourable for breeding must be processed by the brain so that the appropriate responses in reproductive physiology can be implemented. The neural integration of several environmental cues converges on discrete hypothalamic neurons in order to regulate reproductive physiology. Gonadotrophin-releasing hormone-1 (GnRH1) and Kisspeptin (Kiss1) neurons in avian and mammalian species, respectively, show marked variation in expression that is positively associated with breeding state. We applied the constancy/contingency model of predictability to investigate how GnRH1 and Kiss1 integrate different environmental cues to regulate reproduction. We show that variation in GnRH1 from a highly seasonal avian species exhibits a predictive change that is primarily based on contingency information. Opportunistic species have low measures of predictability and exhibit a greater contribution of constancy information that is sex-dependent. In hamsters, Kiss1 exhibited a predictive change in expression that was predominantly contingency information and is anatomically localized. The model applied here provides a framework for studies geared towards determining the impact of variation in climate patterns to reproductive success in vertebrate species.