Biology subjects

Kratina, P.

Publications and source records attributed to Kratina, P..

5 recordsLinked to original sources

bioRxiv2024

Body size is a better predictor of intra- than interspecific variation of animal stoichiometry across realms

Animal stoichiometry affects fundamental processes ranging from organismal physiology to global element cycles. However, it is unknown whether animal stoichiometry follows predictable scaling relationships with body mass and whether adaptation to life on land or water constrains patterns of elemental allocation. To test both interspecific and intraspecific body-size scaling relationships of the nitrogen (N), phosphorus (P), and N:P content of animals, we used a subset of the StoichLife database encompassing 9,933 individual animals (vertebrates and invertebrates) belonging to 1,543 species spanning 10 orders of magnitude of body size from terrestrial, freshwater, and marine realms. Across species, body mass did not explain much variation in %N and %P composition, although the %P of invertebrates decreased with size. The effects of body size on species elemental content were small in comparison to the effects of taxonomy. Body size was a better predictor of intraspecific than interspecific elemental patterns. Between 42 to 45% in intraspecific stoichiometric variation was explained by body size for 27% of vertebrate species and 35% of invertebrate species. Further, differences between organisms inhabiting aquatic and terrestrial realms were observed only in invertebrate interspecific %N, suggesting that the realm does not play an important role in determining elemental allocation of animals. Based on our analysis of the most comprehensive animal stoichiometry database, we conclude that (i) both body size and realm are relatively weak predictors of animal stoichiometry across taxa, and (ii) body size is a good predictor of intraspecific variation in animal elemental content, which is consistent with tissue-scaling relationships that hold broadly across large groups of animals. This research reveals a lack of general scaling patterns in the elemental content across animals and instead points to a large variation in scaling relationships within and among lineages.

ecology↗

bioRxiv2022

Logging alters tropical forest structure, while conversion reduces biodiversity and functioning

The impacts of degradation and deforestation on tropical forests are poorly understood, particularly at landscape scales. We present an extensive ecosystem analysis of the impacts of logging and conversion of tropical forest to oil palm from a large-scale study in Borneo, synthesizing responses from 82 variables categorized into four ecological levels spanning a broad suite of ecosystem properties: 1) structure and environment, 2) species traits, 3) biodiversity, and 4) ecosystem functions. Responses were highly heterogeneous and often complex and non-linear. Variables that were directly impacted by the physical process of timber extraction, such as soil structure, were sensitive to even moderate amounts of logging, whereas measures of biodiversity and ecosystem functioning were generally resilient to logging but more affected by conversion to oil palm plantation. One-Sentence SummaryLogging tropical forest mostly impacts structure while biodiversity and functions are more vulnerable to habitat conversion.

ecology↗

bioRxiv2022

Dispersal provides trophic-level dependent insurance against a heatwave in freshwater ecosystems

Climate change-related heatwaves are major recent threats to biodiversity and ecosystem functioning. However, our current understanding of the mechanisms governing community resilience (resistance and recovery) to extreme temperature events is still rudimentary. The spatial insurance hypothesis postulates that diverse regional species pools can buffer ecosystem functioning against local disturbances through immigration of better adapted taxa. However, experimental evidence for such predictions from multi-trophic communities and pulse-type disturbances, like heatwaves, are largely missing. We performed an experimental mesocosm study with alpine lake plankton to test whether a dispersal event from natural lakes prior to a simulated heatwave could increase resistance and recovery of local communities. As the buffering effect of dispersal may differ among trophic groups, we independently manipulated dispersal of organisms from lower (microorganisms) and higher (zooplankton) trophic levels. The experimental heatwave suppressed total community biomass by having a strong negative effect on zooplankton biomass, probably due to a heat-induced increase in metabolic costs that in turn caused mortality. Heating thus resulted in weaker top-down control and a subsequent shift to bottom-heavy food webs. While zooplankton dispersal did not alleviate the negative heatwave effects on zooplankton biomass, dispersal of microorganism enhanced biomass recovery at the level of phytoplankton, thereby providing evidence for spatial insurance. The different response of trophic groups may be related to the timing of dispersal, which happened under strongly monopolized resource conditions by zooplankton, creating limited opportunity for competitors to establish. At the same time, the heatwave released phytoplankton from grazing pressure and increased nutrient recycling, which may have facilitated the establishment of new phytoplankton taxa. Our findings clearly show that even a short heatwave can strongly alter energy flow in aquatic ecosystems. Although dispersal can enhance community resilience, the strength of its buffering effects depends on the trophic level.

ecology↗

bioRxiv2019

The evolution of competitive ability for essential resources

Competition for limiting resources is among the most fundamental ecological interactions and has long been considered a key driver of species coexistence and biodiversity. Species minimum resource requirements, their R*s, are key traits that link individual physiological demands to the outcome of competition. However, a major question remains unanswered - to what extent are species competitive traits able to evolve in response to resource limitation? To address this knowledge gap, we performed an evolution experiment in which we exposed Chlamydomonas reinhardtii for approximately 285 generations to seven environments in chemostats which differed in resource supply ratios (including nitrogen, phosphorus and light limitation) and salt stress. We then grew the ancestors and descendants in common garden and quantified their competitive abilities for essential resources. We investigated constraints on trait evolution by testing whether changes in resource requirements for different resources were correlated. Competitive abilities for phosphorus improved in all populations, while competitive abilities for nitrogen and light increased in some populations and decreased in others. In contrast to the common assumption that there are trade-offs between competitive abilities for different resources, we found that improvements in competitive ability for a resource came at no detectable cost. Instead, improvements in competitive ability for multiple resources were either positively correlated or not significantly correlated. Using resource competition theory, we then demonstrated that rapid adaptation in competitive traits altered the predicted outcomes of competition. These results highlight the need to incorporate contemporary evolutionary change into predictions of competitive community dynamics over environmental gradients.

evolutionary biology↗

bioRxiv2018

The intrinsic predictability of ecological time series and its potential to guide forecasting

Successfully predicting the future states of systems that are complex, stochastic and potentially chaotic is a major challenge. Model forecasting error (FE) is the usual measure of success; however model predictions provide no insights into the potential for improvement. In short, the realized predictability of a specific model is uninformative about whether the system is inherently predictable or whether the chosen model is a poor match for the system and our observations thereof. Ideally, model proficiency would be judged with respect to the systems intrinsic predictability - the highest achievable predictability given the degree to which system dynamics are the result of deterministic v. stochastic processes. Intrinsic predictability may be quantified with permutation entropy (PE), a model-free, information-theoretic measure of the complexity of a time series. By means of simulations we show that a correlation exists between estimated PE and FE and show how stochasticity, process error, and chaotic dynamics affect the relationship. This relationship is verified for a dataset of 461 empirical ecological time series. We show how deviations from the expected PE-FE relationship are related to covariates of data quality and the nonlinearity of ecological dynamics.\n\nThese results demonstrate a theoretically-grounded basis for a model-free evaluation of a systems intrinsic predictability. Identifying the gap between the intrinsic and realized predictability of time series will enable researchers to understand whether forecasting proficiency is limited by the quality and quantity of their data or the ability of the chosen forecasting model to explain the data. Intrinsic predictability also provides a model-free baseline of forecasting proficiency against which modeling efforts can be evaluated.\n\nGlossaryActive information: The amount of information that is available to forecasting models (redundant information minus lost information; Fig. 1).\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=45 SRC=\"FIGDIR/small/350017_fig1a.gif\" ALT=\"Figure 1A\">\nView larger version (9K):\norg.highwire.dtl.DTLVardef@934d9eorg.highwire.dtl.DTLVardef@ccdc10org.highwire.dtl.DTLVardef@1839ed9org.highwire.dtl.DTLVardef@31bd70_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFigure 1A.C_FLOATNO The total information content of an observation of a system at a given state in time, St, is depicted by filled circles with past states (St-1 and St-2) represented by shades of grey, i) lack of overlap between past and present states illustrating a case where no information is transmitted from past states (i.e. a purely stochastic system), with low redundancy and high Shannon entropy rate, ii) intermediate overlap indicating a case when some information is transferred from past to present (i.e. a deterministic system strongly driven by stochastic forcing), with intermediate redundancy and Shannon entropy rate, iii) large overlap indicating a case when the current state is mostly determined by the previous state (i.e. a highly deterministic system), with high redundancy and low Shannon entropy rate. Note that both the redundancy and Shannon entropy rate of a system are intrinsic properties of the system and will only change if the system itself changes.\n\nC_FIG Forecasting error (FE): A measure of the discrepancy between a models forecasts and the observed dynamics of a system. Common measures of forecast error are root mean squared error and mean absolute error.\n\nEntropy: Measures the average amount of information in the outcome of a stochastic process.\n\nInformation: Any entity that provides answers and resolves uncertainty about a process. When information is calculated using logarithms to the base two (i.e. information in bits), it is the minimum number of yes/no questions required, on average, to determine the identity of the symbol (Jost 2006). The information in an observation consists of information inherited from the past (redundant information), and of new information.\n\nIntrinsic predictability: the maximum achievable predictability of a system (Beckage et al. 2011).\n\nLost information: The part of the redundant information lost due to measurement or sampling error, or transformations of the data (Fig. 1).\n\nNew information, Shannon entropy rate: The Shannon entropy rate quantifies the average amount of information per observation in a time series that is unrelated to the past, i.e., the new information (Fig. 1).\n\nNonlinearity: When the deterministic processes governing system dynamics depend on the state of the system.\n\nPermutation entropy (PE): permutation entropy is a measure of the complexity of a time series (Bandt & Pompe, 2002) that is negatively correlated with a systems predictability (Garland et al. 2015). Permutation entropy quantifies the combined new and lost information. PE is scaled to range between a minimum of 0 and a maximum of 1.\n\nRealized predictability: the achieved predictability of a system from a given forecasting model.\n\nRedundant information: The information inherited from the past, and thus the maximum amount of information available for use in forecasting (Fig. 1).\n\nSymbols, words, permutations: symbols are simply the smallest unit in a formal language such as the letters in the English alphabet i.e., {\"A\", \"B\",..., \"Z\"}. In information theory the alphabet is more abstract, such as elements in the set {\"up\", \"down\"} or {\"1\", \"2\", \"3\"}. Words, of length m refer to concatenations of the symbols (e.g., up-down-down) in a set. Permutations are the possible orderings of symbols in a set. In this manuscript, the words are the permutations that arise from the numerical ordering of m data points in a time series.\n\nWeighted permutation entropy (WPE): a modification of permutation entropy (Fadlallah et al., 2013) that distinguishes between small-scale, noise-driven variation and large-scale, system-driven variation by considering the magnitudes of changes in addition to the rank-order patterns of PE.

ecology↗