Vegetation Dormancy in the Dynamics of Alpine Short-Lived Perennials: All in Good Time

The local population structures of two short-lived perennial species, Androsace albana and Eritrichium caucasicum, classified by ontogenetic stages were observed annually for 15 years (2009–2023) at permanent sites in the alpine belt of the North-West Caucasus. The uniquely long series of these data made it possible to discover the effects of vegetation dormancy in the life cycle of a short-lived species, which was fundamentally impossible with short series of about three to five years. Data of the “identified individuals” (A. albana) and “identified individuals from unknown parents” (E. caucasicum) types enable us to calibrate the corresponding matrix models of discrete-structured population dynamics and obtain the so-called annual population projection matrices (PPMs). The analysis of PPMs by mathematical means yields various quantitative characteristics of the monitored object, in particular, the viability measure of the local population. However, the revealed effects of vegetation dormancy make changes to the data series and raise the issue to revise the previous models and ensued characteristics. We show that including an additional state of death or vegetation dormancy into the life cycle, which is quite a logical move from the model’s viewpoint, does not make any sense in the task of assessing the population viability. When adjusted to fit the revised data, the calibration procedure, does naturally increase the previous estimates of the viability measure, thereby confirming an important role the vegetation dormancy plays as a mechanism to adapt the plant for stressful environment.

Our study is devoted to a subject popular in the field of matrix population models, namely, estimating the stochastic growth rate, λS, a quantitative measure of long-term population viability, for a discrete-stage-structured population monitored during many years. “Reproductive uncertainty” refers to a feature inherent in the data and life cycle graph (LCG) when the LCG has more than one reproductive stage, but when the progeny cannot be associated to a parent stage in a unique way. Reproductive uncertainty complicates the procedure of λS estimation following the defining of λS from the limit of a sequence consisting of population projection matrices (PPMs) chosen randomly from a given set of annual PPMs. To construct a Markov chain that governs the choice of PPMs for a local population of Eritrichium caucasicum, an short-lived perennial alpine plant species, we have found a local weather index that is correlated with the variations in the annual PPMs, and we considered its long time series as a realization of the Markov chain that was to be constructed. Reproductive uncertainty has required a proper modification of how to restore the transition matrix from a long realization of the chain, and the restored matrix has been governing random choice in several series of Monte Carlo simulations of long-enough sequences. The resulting ranges of λS estimates turn out to be more narrow than those obtained by the popular i.i.d. methods of random choice (independent and identically distributed matrices); hence, we receive a more accurate and reliable forecast of population viability.

In matrix population modeling the multi-year monitoring of a population structure results in a set of annual population projection matrices (PPMs), which gives rise to the stochastic growth rate λS, a quantitative measure of long-term population viability. This measure is usually found in the paradigm of population growth in a variable environment. The environment is represented by the set of PPMs, and λS ensues from a long sequence of PPMs chosen at random from the given set. because the known rules of random choice, such as the iid (independent and identically distributed) matrices, are generally artificial, the challenge is to find a more realistic rule. We achieve this with the a following a Markov chain that models, in a certain sense, the real variations in the environment. We develop a novel method to construct the ruling Markov chain from long-term weather data and to simulate, in a Monte Carlo mode, the long sequences of PPMs resulting in the estimates of λS. The stochastic nature of sequences causes the estimates to vary within some range, and we compare the range obtained by the “realistic choice” from 10 PPMs for a local population of a Red-Book species to those using the iid choice. As noted in the title of this paper, this realistic choice contracts the range of λS estimates, thus improving the estimation and confirming the Red-Book status of the species.

Adaptation on the ground and beneath: does the local population maximize its λ1?

Summary 1. Population projection matrices (PPMs) are probably the most commonly used empirical population models. To be useful for predictive or prospective analyses, PPM models should generally be irreducible (the associated life cycle graph contains the necessary transition rates to facilitate pathways from all stages to all other stages) and therefore ergodic (whatever initial stage structure is used in the population projection, it will always exhibit the same stable asymptotic growth rate). 2. Evaluation of 652 PPM models for 171 species from the literature suggests that 24·7% of PPM models are reducible (parameterized transition rates do not facilitate pathways from all stages to all other stages). Reducible models are sometimes ergodic but may be non‐ergodic (the model exhibits two or more stable asymptotic states with different asymptotic stable growth rates, which depend on the initial stage structure used in the population projection). In our sample of published PPMs, 15·6% are non‐ergodic. 3. This presents a problem: reducible–ergodic models often defy biological rationale in their description of the life cycle but may or may not prove problematic for analysis as they often behave similarly to irreducible models. Reducible–non‐ergodic models will usually defy biological rationale in their description of the both the life cycle and population dynamics, hence contravening most analytical methods. 4. We provide simple methods to evaluate reducibility and ergodicity of PPM models, present illustrative examples to elucidate the relationship between reducibility and ergodicity and provide empirical examples to evaluate the implications of these properties in PPM models. 5. As a prevailing tool for population ecologists, PPM models need to be as predictive as possible. However, there is a large incidence of reducibility in published PPMs, with significant implications for the predictive power of such models in many cases. We suggest that as a general rule, reducibility of PPM models should be avoided. However, we provide a guide to the pertinent analysis of reducible matrix models, largely based upon whether they are ergodic or not.

Eritrichium caucasicum is an alpine short-lived perennial species endemic for the Caucasus. The stage structure of a local population has been observed on permanent plots in the alpine belt of the Northwestern Caucasus annually for 13 years (2009–2021), accumulating data of the “identified individuals from unknown parents” type. The latter circumstance has predetermined what is called reproductive uncertainty in the terminology of matrix models for discrete-structured population dynamics and means that the annual recruitment rates inherent in the groups of generative plants and final flowering generative plants cannot be calibrated in a uniquely way. As a result, instead of the annual values of the asymptotic growth rate, the model gives only certain ranges of their values that vary from year to year, corresponding to the data. This introduces both technical difficulties and uncertainty in the viability forecast based on the asymptotic growth rates. A well-known alternative approach is to estimate the stochastic growth rate λS, but only artificial modes of randomness involved in the calculation of λS have been proposed in the literature. Our realistic model of randomness is related to variations in weather and microclimatic conditions of the habitat. It is reconstructed from a fairly long (60 years) time series of the weather indicator. Using this realistic model in Monte Carlo calculations of λS, we have obtained a more reliable and accurate estimate of the stochastic growth rate.

Quantifying the impact of anthropogenic development on local populations is important for conservation biology and wildlife management. However, these local populations are often subject to demographic stochasticity because of their small population size. Traditional modeling efforts such as population projection matrices do not consider this source of variation whereas individual‐based models, which include demographic stochasticity, are computationally intense and lack analytical tractability. One compromise between approaches is branching process models because they accommodate demographic stochasticity and are easily calculated. These models are known within some sub‐fields of probability and mathematical ecology but are not often applied in conservation biology and applied ecology. We applied branching process models to quantitatively compare and prioritize species locally vulnerable to the development of wind energy facilities. Specifically, we examined species vulnerability using branching process models for four representative species: A cave bat (a long‐lived, low fecundity species), a tree bat (short‐lived, moderate fecundity species), a grassland songbird (a short‐lived, high fecundity species), and an eagle (a long‐lived, slow maturation species). Wind turbine‐induced mortality has been observed for all of these species types, raising conservation concerns. We simulated different mortality rates from wind farms while calculating local extinction probabilities. The longer‐lived species types (e.g., cave bats and eagles) had much more pronounced transitions from low extinction risk to high extinction risk than short‐lived species types (e.g., tree bats and grassland songbirds). High‐offspring‐producing species types had a much greater variability in baseline risk of extinction than the lower‐offspring‐producing species types. Long‐lived species types may appear stable until a critical level of incidental mortality occurs. After this threshold, the risk of extirpation for a local population may rapidly increase with only minimal increases in wind mortality. Conservation biologists and wildlife managers may need to consider this mortality pattern when issuing take permits and developing monitoring protocols for wind facilities. We also describe how our branching process models may be generalized across a wider range of species for a larger assessment project and then describe how our methods may be applied to other stressors in addition to wind.

Editorial: Green or red: Challenges for fish and freshwater biodiversity conservation related to hydropower

The local population stage structures of the primrose Androsace albana and the Caucasian forget-me-not Eritrichium caucasicum were observed at permanent sites in the alpine belt of the North-West Caucasus annually for 14 years (2009–2022), accumulating data of the “identified individuals” type according to known ontogenetic scales. The data allow us to calibrate the corresponding matrix models of population dynamics, from which we can obtain various quantitative characteristics of the monitoring object, in particular, estimate the measure of viability. A well-known approach to predicting the viability of a local population is to estimate its stochastic growth rate (λS) under a certain scenario of random changes in environmental conditions from those observed during the monitoring period. However, only artificial randomness models involved in λS calculations are proposed in the literature. Our more realistic randomness model (RRM) is associated with variations in the weather and microclimatic conditions of the habitat. It is reconstructed from a sufficiently long (60 years) time series of the weather indicator, which has turned out to be species-specific in the model perennials. The use of RRM in λS calculations by the Monte Carlo method provides the more reliable and accurate estimates of stochastic population growth rates than those using the well-known technique with an artificial randomness model. The obtained λS estimates are compared between the two species, as well as between those for each of the species obtained from the monitoring data of different durations. The comparison allows us to draw the conclusion given in the paper title.

Background:The geographic range of the tick Amblyomma americanum, a vector of diseases of public health significance such as ehrlichiosis, has expanded from the southeast of the United States northward during the 20th century. Recently, populations of this tick have been reported to be present close to the Canadian border in Michigan and New York states, but established populations are not known in Canada. Previous research suggests that changing temperature patterns with climate change may influence tick life cycles and permit northward range expansion of ticks in the northern hemisphere.Objectives:We aimed to estimate minimal temperature conditions for survival of A. americanum populations at the northern edge of the tick’s range and to investigate the possibility of range expansion of A. americanum into northern U.S. states and southern Canada in the coming decades.Methods:A simulation model of the tick A. americanum was used, via simulations using climate data from meteorological stations in the United States and Canada, to estimate minimal temperature conditions for survival of A. americanum populations at the northern edge of the tick’s range.Results:The predicted geographic scope of temperature suitability [ annual cumulative degree days (DD) ] included most of the central and eastern U.S. states east of longitude 110°W, which is consistent with current surveillance data for the presence of the tick in this region, as well as parts of southern Quebec and Ontario in Canada. Regional climate model output raises the possibility of northward range expansion into all provinces of Canada from Alberta to Newfoundland and Labrador during the coming decades, with the greatest northward range expansion (up to by the year 2100) occurring under the greenhouse gas (GHG) emissions of Representative Concentration Pathway (RCP) 8.5. Predicted northward range expansion was reduced by approximately half under the reduced GHG emissions of RCP4.5.Discussion:Our results raise the possibility of range expansion of A. americanum into northern U.S. states and southern Canada in the coming decades, and conclude that surveillance for this tick, and the diseases it transmits, would be prudent. https://doi.org/10.1289/EHP5668

Selected by the tide: Studying the specificities of a traditional fishing method in mangroves

Averaging the population projection matrices: Heuristics against uncertainty and nonexistence

The timing of biological events (phenology) is an important aspect of both a species’ life cycle and how it interacts with other species and its environment. Patterns of phenological change have been given much scientific attention, particularly recently in relation to climate change. For pairs of interacting species, if their rates of phenological change differ, then this may lead to asynchrony between them and disruption of their ecological interactions. However it is often difficult to interpret differential rates of phenological change and to predict their ecological and evolutionary consequences. We review theoretical results regarding this topic, with special emphasis on those arising from life history theory, evolutionary game theory and population dynamic models. Much ecological research on phenological change builds upon the concept of match/mismatch, so we start by putting forward a simple but general model that captures essential elements of this concept. We then systematically compare the predictions of this baseline model with expectations from theory in which additional ecological mechanisms and features of species life cycles are taken into account. We discuss the ways in which the fitness consequences of interspecific phenological asynchrony may be weak, strong, or idiosyncratic. We discuss theory showing that synchrony is not necessarily an expected evolutionary outcome, and how population densities are not necessarily maximized by adaptation, and the implications of these findings. By bringing together theoretical developments regarding the eco‐evolutionary consequences of phenological asynchrony, we provide an overview of available alternative hypotheses for interpreting empirical patterns as well as the starting point for the next generation of theory in this field.

Improving our understanding of the ecological and demographic mechanisms that underlie changes in wild population productivity is critical to support ecosystem-based management. Yet, population productivity results from a combination of demographic rates (such as mortality, growth and recruitment) driven by intrinsic and extrinsic factors which interact through density-, size- or environment-dependent mechanisms. The interdependence between demographic rates, and intrinsic and extrinsic factors, has rarely been studied throughout the life cycle of a wild population. This is particularly needed for short life species, such as small pelagic fish, which are subject to inter-annual abrupt changes in abundances. Here, we developed an age-based life-cycle model to investigate the relative influence of intrinsic and extrinsic factors on successive transitions rates along the life cycle of a system composed of two small pelagic species of the Bay of Biscay, the European anchovy (Engraulis encrasicolus) and European sardine (Sardina pilchardus). Our model allows to disentangle the influence of density dependence, size dependence and environmental factors throughout the life cycle of short life species. Our results highlight that sardine demography is density-dependent, with significant effects of density on natural mortality at age 1 and age 3. In contrast, anchovy demography is both size- and density-dependent, with density-dependent effects on natural mortality at age 1, age 2 and on recruitment, and a size-dependent effect on age-1 mortality. Moreover, we found that natural mortalities of both anchovy and sardine were related to large-scale environmental indicators, such as the Atlantic Multidecadal Oscillation and the North Atlantic Oscillation. The originality of this approach lies in its ability to integrate the effects of size, density and environment on a multitude of demographic processes throughout the life cycle for wild species, and can be seen as a step towards supporting ecosystem-based management.

This study aims to determine how each element of intellectual capital affects earnings management based on the business life cycle. The data used for this study come from manufacturing companies listed on the IDX from 2020 to 2023. Overall, the research sample consists of 121 companies. The criteria for classifying a company's life cycle are based on the company's average sales growth. This study uses panel data regression analysis through the EViews 12 application. The results conclude that all intellectual capital components negatively influence earnings management, regardless of the company's life cycle. Meanwhile, HCE has a significant negative effect at the mature and decline stages but is insignificant during the growth stage. Additionally, SCE has a significant negative effect during the growth stage but an insignificant negative effect and an insignificant positive effect at the mature and decline stages. Furthermore, RCE shows a significant negative effect at the decline stage and an insignificant negative effect at the growth and mature stages. The last component, CEE, exhibits a significant negative effect at the mature stage but is insignificant during the growth and decline stages. Simultaneously, each intellectual capital component influences earnings management.