Modeling population dynamics of invasive pines to optimize their control in native grasslands of Argentina

The spread of invasive alien species over natural environments has become one of the most serious threats to biodiversity and the functioning of ecosystems worldwide. Understanding the population attributes that allow a given species to become invasive is crucial for improving prevention and control interventions. Pampas grasslands are particularly sensitive to the invasion of exotic woody plants. In particular, the Ventania Mountains undergo the advance of alien woody plants; among which the Aleppo pine (Pinus halepensis) stands out due to the extension of the area it covers and the magnitude of the ecological changes associated to its presence. Using a model that describes the population dynamics of the species in the area, we evaluated the expected behavior of the population under different environmental conditions and different management scenarios. When the effect of stochastic fires was simulated, the growth rate was greater than 1 for all the frequencies considered, peaking under fires every nine years, on average. When evaluating the effect of periodic mechanical control of the adult population, the reduction in growth rate was insufficient, except for cutting intensities that significantly exceeded the current operational capacity of the area. Under prescribed fire scenarios, on the other hand, burning frequencies greater than seven years resulted in population reductions. The results highlight the importance of fire in regulating the population of P. halepensis in the Ventania Mountains, with contrasting effects depending on the frequency with which it occurs, which allows considering it as an effective environmental management option for the control of the species.

Difficulties in summing log-normal distributions for abundance and potential solutions

The log-normal distribution, often used to model animal abundance and its uncertainty, is central to ecological modeling and conservation but its statistical properties are less intuitive than those of the normal distribution. The right skew of the log-normal distribution can be considerable for highly uncertain estimates and the median is often chosen as a point estimate. However, the use of the median can become complicated when summing across populations since the median of the sum of log-normal distributions is not the sum of the constituent medians. Such estimates become sensitive to the spatial or taxonomic scale over which abundance is being summarized and the naive estimate (the median of the distribution representing the sum across populations) can become grossly inflated. Here we review the statistical issues involved and some alternative formulations that might be considered by ecologists interested in modeling abundance. Using a recent estimate of global avian abundance as a case study (Callaghan et al. 2021), we investigate the properties of several alternative methods of summing across species' abundance, including the sorted summing used in the original study (Callaghan et al. 2021) and the use of shifted log-normal distributions, truncated normal distributions, and rectified normal distributions. The appropriate method of summing across distributions was intimately tied to the use of the mean or median as the measure of central tendency used as the point estimate. Use of the shifted log-normal distribution, however, generated scale-consistent estimates for global abundance across a spectrum of contexts. Our paper highlights how seemingly inconsequential decisions regarding the estimation of abundance yield radically different estimates of global abundance and its uncertainty, with conservation consequences that are underappreciated and require careful consideration.

Defining biologically relevant and hierarchically nested population units to inform wildlife management

Wildlife populations are increasingly affected by natural and anthropogenic changes that negatively alter biotic and abiotic processes at multiple spatiotemporal scales and therefore require increased wildlife management and conservation efforts. However, wildlife management boundaries frequently lack biological context and mechanisms to assess demographic data across the multiple spatiotemporal scales influencing populations. To address these limitations, we developed a novel approach to define biologically relevant subpopulations of hierarchically nested population levels that could facilitate managing and conserving wildlife populations and habitats. Our approach relied on the Spatial "K"luster Analysis by Tree Edge Removal clustering algorithm, which we applied in an agglomerative manner (bottom-to-top). We modified the clustering algorithm using a workflow and population structure tiers from least-cost paths, which captured biological inferences of habitat conditions (functional connectivity), dispersal capabilities (potential connectivity), genetic information, and functional processes affecting movements. The approach uniquely included context of habitat resources (biotic and abiotic) summarized at multiple spatial scales surrounding locations with breeding site fidelity and constraint-based rules (number of sites grouped and population structure tiers). We applied our approach to greater sage-grouse (Centrocercus urophasianus), a species of conservation concern, across their range within the western United States. This case study produced 13 hierarchically nested population levels (akin to cluster levels, each representing a collection of subpopulations of an increasing number of breeding sites). These closely approximated population closure at finer ecological scales (smaller subpopulation extents with fewer breeding sites; cluster levels ≥2), where >92% of individual sage-grouse's time occurred within their home cluster. With available population monitoring data, our approaches can support the investigation of factors affecting population dynamics at multiple scales and assist managers with making informed, targeted, and cost-effective decisions within an adaptive management framework. Importantly, our approach provides the flexibility of including species-relevant context, thereby supporting other wildlife characterized by site fidelity.

The Roles of the Moss Layer in Mediating Tree Seedling Environmental Stress, Mercury Exposure, and Regeneration in High-Elevation Conifer Forests

The persistence of future forests depends on the success of tree seedlings which are experiencing increasing physiological stress from changing climate and air pollution. Although the moss layer can serve as an important substrate for tree seedlings, its potential for reducing environmental stress and enhancing the establishment of seedlings remains poorly understood. We tested if the moss layer decreased environmental stress and increased the abundance of balsam fir seedlings dominant in high-elevation forests of northeastern United States that are sensitive to changing climate and mercury deposition. We surveyed balsam fir seedling density by substrate (moss, litter, other) on 120 quadrats (1 × 1 m) in two contrasting canopy environments (in gaps and under canopies), measured seedling stress, and quantified mercury content in seedlings and substrates. We observed that, in both canopy environments, tree seedlings established on moss exhibited (i) increased density, (ii) decreased physiological stress, and (iii) higher potential to recruit into larger size classes, compared to seedlings established in litter. Regardless of canopy environment, seedling foliar mercury levels did not correspond to substrate mercury despite large differences in substrate mercury concentrations (relative to moss, litter concentrations were ~ 4-times greater and soil concentrations were ~ 6-times greater), likely reflecting the dominance of foliar over root uptake of mercury. Because the moss layer appeared to mitigate seedling drought stress, and to increase seedling establishment and recruitment compared to other substrates, these microsite effects should be considered in models predicting forest regeneration and dynamics under increased drought stress associated with the ongoing climate warming.

Modeling abundance, distribution, movement and space use with camera and telemetry data

Studies of animal abundance and distribution are often conducted independently of research on movement, despite the important links between processes. Movement can cause rapid changes in spatial variation in density, and movement influences detection probability and therefore estimates of abundance from inferential methods such as spatial capture-recapture (SCR). Technological developments including camera traps and GPS telemetry have opened new opportunities for studying animal demography and movement, yet statistical models for these two data types have largely developed along parallel tracks. We present a hierarchical model in which both datasets are conditioned on a movement process for a clearly defined population. We fitted the model to data from 60 camera traps and 23,572 GPS telemetry locations collected on 17 male white-tailed deer in the Big Cypress National Preserve, Florida, USA during July 2015. Telemetry data were collected on a 3-4 h acquisition schedule, and we modeled the movement paths of all individuals in the region with a Ornstein-Uhlenbeck process that included individual-specific random effects. Two of the 17 deer with GPS collars were detected on cameras. An additional 20 male deer without collars were detected on cameras and individually identified based on their unique antler characteristics. Abundance was 126 (95% CI: 88-177) in the 228 km² region, only slightly higher than estimated using a standard SCR model: 119 (84-168). The standard SCR model, however, was unable to describe individual heterogeneity in movement rates and space use as revealed by the joint model. Joint modeling allowed the telemetry data to inform the movement model and the SCR encounter model, while leveraging information in the camera data to inform abundance, distribution and movement. Unlike most existing methods for population-level inference on movement, the joint SCR-movement model can yield unbiased inferences even if non-uniform sampling is used to deploy transmitters. Potential extensions of the model include the addition of resource selection parameters, and relaxation of the closure assumption when interest lies in survival and recruitment. These developments would contribute to the emerging holistic framework for the study of animal ecology, one that uses modern technology and spatio-temporal statistics to learn about interactions between behavior and demography.

The macroecology of plant populations from local to global scales

Population ecologists develop theoretical and pragmatic knowledge of how and why populations change or remain stable, how life histories evolve and devise management strategies for populations of concern. However, forecasting the effects of global change or recommending management strategies is often urgent, requiring ecologists to work without detailed local evidence while using data and models from outside the focal location or species. Here we explore how the comparative ecology of populations, population macroecology, can be used to develop generalisations within and between species across different scales, using available demographic, environmental, life history, occurrence and trait data. We outline the strengths and weaknesses of using broad climatic variables and suitability inferred from probability of occupancy models to represent environmental variation in comparative analyses. We evaluate the contributions of traits, environment and their interaction as drivers of life history strategy. We propose that insights from life history theory, together with the adaptive capacity of populations and individuals, can inform on 'persist in place' vs 'shift in space' responses to changing conditions. As demographic data accumulate at landscape and regional scales for single species, and throughout plant phylogenies, we will have new opportunities for testing macroecological generalities within and across species.

Estimation of breeding probability can make monitoring data more revealing: a case study of amphibians

Monitoring programs serve to detect trends in the distribution and abundance of species. To do so, monitoring programs often use static state variables. Dynamic state variables that describe population dynamics might be more valuable because they allow for a mechanistic understanding of the processes that lead to population trends. We fit multistate occupancy models to data from a country-wide multispecies amphibian occupancy monitoring program and estimated occupancy and breeding probabilities. If breeding probabilities are determinants of occupancy dynamics, then they may serve in monitoring programs as state variables that describe dynamic processes. The results showed that breeding probabilities were low and that a large proportion of the populations had to be considered to be non-breeding populations (i.e., populations where adults are present but no breeding occurs). For some species, the majority of populations were non-breeding populations. We found that non-breeding populations have lower persistence probabilities than populations where breeding occurs. Breeding probabilities may thus explain trends in occupancy but they might also explain other ecological phenomena, such as the success of invasive species, which had high breeding probabilities. Signs of breeding, i.e., the presence of eggs and larvae, were often hard to detect. Importantly, non-breeding populations also had low detection probabilities, perhaps because they had lower abundances. We suggest that monitoring programs should invest more in the detection of life history stages indicative of breeding, and also into the detection of non-breeding populations. We conclude that breeding probability should be used as a state variable in monitoring programs because it can lead to deeper insights into the processes driving occupancy dynamics.

Genetic diversity in a long-lived mammal is explained by the past's demographic shadow and current connectivity

Within-species genetic diversity is crucial for the persistence and integrity of populations and ecosystems. Conservation actions require an understanding of factors influencing genetic diversity, especially in the context of global change. Both population size and connectivity are factors greatly influencing genetic diversity; the relative importance of these factors can, however, change through time. Hence, quantifying the degree to which population size or genetic connectivity are shaping genetic diversity, and at which ecological time scale (past or present), is challenging, yet essential for the development of efficient conservation strategies. In this study, we estimated the genetic diversity of 42 colonies of Rhinolophus hipposideros, a long-lived mammal vulnerable to global change, sampling locations spanning its continental northern range. Here, we present an integrative approach that disentangles and quantifies the contribution of different connectivity measures in addition to contemporary colony size and historic bottlenecks in shaping genetic diversity. In our study, the best model explained 64% of the variation in genetic diversity. It included historic bottlenecks, contemporary colony size, connectivity and a negative interaction between the latter two. Contemporary connectivity explained most genetic diversity when considering a 65 km radius around the focal colonies, emphasizing the large geographic scale at which the positive impact of connectivity on genetic diversity is most profound and hence, the minimum scale at which conservation should be planned. Our results highlight that the relative importance of the two main factors shaping genetic diversity varies through time, emphasizing the relevance of disentangling them to ensure appropriate conservation strategies.

Fine-scale spatial variation in fitness is comparable to disturbance-induced fluctuations in a fire-adapted species

The spatial scale at which demographic performance (e.g., net reproductive output) varies can profoundly influence landscape-level population growth and persistence, and many demographically pertinent processes such as species interactions and resource acquisition vary at fine scales. We compared the magnitude of demographic variation associated with fine-scale heterogeneity (<10 m), with variation due to larger-scale (>1 ha) fluctuations associated with fire disturbance. We used a spatially explicit model within an IPM modeling framework to evaluate the demographic importance of fine-scale variation. We used a measure of expected lifetime fruit production, E_F , that is assumed to be proportional to lifetime fitness. Demographic differences and their effects on E_F were assessed in a population of the herbaceous perennial Hypericum cumulicola (~2,600 individuals), within a patch of Florida rosemary scrub (400 × 80 m). We compared demographic variation over fine spatial scales to demographic variation between years across 6 yr after a fire. Values of E_F changed by orders of magnitude over <10 m. This variation in fitness over fine spatial scales (<10 m) is commensurate to postfire changes in fitness for this fire-adapted perennial. A life table response experiment indicated that fine-scale spatial variation in vital rates, especially survival, explains as much change in E_F as demographic changes caused by time-since-fire, a key driver in this system. Our findings show that environmental changes over a few tens of meters can have ecologically meaningful implications for population growth and extinction.

What processes must we understand to forecast regional-scale population dynamics?

An urgent challenge facing biologists is predicting the regional-scale population dynamics of species facing environmental change. Biologists suggest that we must move beyond predictions based on phenomenological models and instead base predictions on underlying processes. For example, population biologists, evolutionary biologists, community ecologists and ecophysiologists all argue that the respective processes they study are essential. Must our models include processes from all of these fields? We argue that answering this critical question is ultimately an empirical exercise requiring a substantial amount of data that have not been integrated for any system to date. To motivate and facilitate the necessary data collection and integration, we first review the potential importance of each mechanism for skilful prediction. We then develop a conceptual framework based on reaction norms, and propose a hierarchical Bayesian statistical framework to integrate processes affecting reaction norms at different scales. The ambitious research programme we advocate is rapidly becoming feasible due to novel collaborations, datasets and analytical tools.

The demographic contributions of connectivity versus local dynamics to population growth of an endangered bird

Conservation and management increasingly focus on connectivity, because connectivity driven by variation in immigration rates across landscapes is thought to be crucial for maintaining local population and metapopulation persistence. Yet, efforts to quantify the relative role of immigration on population growth across the entire range of species and over time have been lacking. We assessed whether immigration limited local and range-wide population growth of the endangered snail kite Rostrhamus sociabilis in Florida, USA, over 18 years using multi-state, reverse-time modelling that accounts for imperfect detection of individuals and unobservable states. Demographic contributions of immigration varied depending on the dynamics and geographic position of the local populations, were scale-dependent and changed over time. By comparing the relative contributions of immigration versus local demography for periods of significant change in local abundance, we found empirical evidence for a disproportionately large role of immigration in facilitating population growth of a centrally located population-a connectivity 'hub'. The importance of connectivity changed depending of the spatial scale considered, such that immigration was a more important driver of population growth at small versus large spatial scales. Furthermore, the contribution of immigration was much greater during time periods when local population size was small, emphasizing abundance-dependent rescue effects. Our findings suggest that efforts aimed at improving local breeding habitat will likely be most effective at increasing snail kite population growth. More broadly, our results provide much needed information on the role of connectivity for population growth, suggesting that connectivity conservation may have the greatest benefits when efforts focus on centrally located habitat patches and small populations. Furthermore, our results highlight that connectivity is highly dynamic over time and that interpreting the effects of connectivity at local scales may not transfer to region-wide dynamics.

Soil Microbiomes Underlie Population Persistence of an Endangered Plant Species

Microbiomes can dramatically alter individual plant performance, yet how these effects influence higher-order processes is not well resolved. In particular, little is known about how microbiome effects on individual plants alter plant population dynamics, a question critical to imperiled species conservation. Here we integrate bioassays, multidecadal demographic data, and integral projection modeling to determine how the presence of the natural soil microbiome underlies plant population dynamics. Simulations indicated that the presence of soil microbiomes boosted population growth rates (λ) of the endangered Hypericum cumulicola by 13% on average, the difference between population growth versus decline in 76% of patches. The greatest benefit (47% increase in λ) occurred in low-nutrient, high-elevation habitats, suggesting that the soil microbiome may help expand H. cumulicola's distribution to include these stressful habitats. Our results demonstrate that soil microbiomes can significantly affect plant population growth and persistence and support the incorporation of soil microbiomes into conservation planning.

Range expansion is associated with increased survival and fecundity in a long-lived bat species

The speed and dynamics of range expansions shape species distributions and community composition. Despite the critical impact of population growth rates for range expansion, they are neglected in existing empirical studies, which focus on the investigation of selected life-history traits. Here, we present an approach based on non-invasive genetic capture-mark-recapture data for the estimation of adult survival, fecundity and juvenile survival, which determine population growth. We demonstrate the reliability of our method with simulated data, and use it to investigate life-history changes associated with range expansion in 35 colonies of the bat species Rhinolophus hipposideros. Comparing the demographic parameters inferred for 19 of those colonies which belong to an expanding population with those inferred for the remaining 16 colonies from a non-expanding population reveals that range expansion is associated with higher net reproduction. Juvenile survival was the main driver of the observed reproduction increase in this long-lived bat species with low per capita annual reproductive output. The higher average growth rate in the expanding population was not associated with a trade-off between increased reproduction and survival, suggesting that the observed increase in reproduction stems from a higher resource acquisition in the expanding population. Environmental conditions in the novel habitat hence seem to have an important influence on range expansion dynamics, and warrant further investigation for the management of range expansion in both native and invasive species.

Landscape heterogeneity is key to forecasting outcomes of plant reintroduction

Conservation and restoration projects often involve starting new populations by introducing individuals into portions of their native or projected range. Such efforts can help meet many related goals, including habitat creation, ecosystem service provisioning, assisted migration, and the reintroduction of imperiled species following local extirpation. The outcomes of reintroduction efforts, however, are highly variable, with results ranging from local extinction to dramatic population growth; reasons for this variation remain unclear. Here, we ask whether population growth following plant reintroductions is governed by variation at two scales: the scale of individual habitat patches to which individuals are reintroduced, and larger among-landscape scales in which similar patches may be situated in landscapes that differ in matrix type, soil conditions, and other factors. Quantifying demographic variation at these two scales will help prioritize locations for introduction and, once introductions take place, forecast population growth. This work took place within a large-scale habitat fragmentation experiment, where individuals of two perennial forb species were reintroduced into eight replicate ~50-ha landscapes, each containing a set of five ~1-ha patches that varied in their degree of isolation (connected by habitat corridors or unconnected) and edge-to-area ratio. Using data on individual growth, survival, reproductive output, and recruitment collected one to two years after reintroduction, we developed models to forecast population growth, then compared forecasts to observed population sizes, three and six years later. Both the type of patch (connected and unconnected) and identity of the landscape to which individuals were reintroduced had effects on forecasted population growth rates, but only variation associated with landscape identity was an accurate predictor of subsequently observed population growth rates. Models that did not include landscape identity had minimal forecasting ability, revealing the key importance of variation at this scale for accurate prediction. Of the five demographic rates used to model population dynamics, seed production was the most important source of forecast error in population growth rates. Our results point to the importance of accounting for landscape-scale variation in demographic models and demonstrate how such models might assist with prioritizing particular landscapes for species reintroduction projects.

Incorporating local adaptation into forecasts of species' distribution and abundance under climate change

Populations of many species are genetically adapted to local historical climate conditions. Yet most forecasts of species' distributions under climate change have ignored local adaptation (LA), which may paint a false picture of how species will respond across their geographic ranges. We review recent studies that have incorporated intraspecific variation, a potential proxy for LA, into distribution forecasts, assess their strengths and weaknesses, and make recommendations for how to improve forecasts in the face of LA. The three methods used so far (species distribution models, response functions, and mechanistic models) reflect a trade-off between data availability and the ability to rigorously demonstrate LA to climate. We identify key considerations for incorporating LA into distribution forecasts that are currently missing from many published studies, including testing the spatial scale and pattern of LA, the confounding effects of LA to nonclimatic or biotic drivers, and the need to incorporate empirically based dispersal or gene flow processes. We suggest approaches to better evaluate these aspects of LA and their effects on species-level forecasts. In particular, we highlight demographic and dynamic evolutionary models as promising approaches to better integrate LA into forecasts, and emphasize the importance of independent model validation. Finally, we urge closer examination of how LA will alter the responses of central vs. marginal populations to allow stronger generalizations about changes in distribution and abundance in the face of LA.

Annual Abundance and Population Structure of Two Dung Beetle Species in a Human-Modified Landscape

Population studies are essential for understanding different aspects of species' biology, estimating extinction probability, and determining evolutionary and life history. Using the mark-recapture method, we studied the abundance and population structure of dung beetle species (Deltochilum mexicanum and Dichotomius satanas) over one year in a human-modified landscape in Mexico. We captured 1960 individuals with a net recapture rate of 11%. Deltochilum mexicanum had a higher rate of recapture (14%) than Dichotomius satanas (5%). Annual variation in abundance was similar for both species, with maximum abundance occurring in summer and a marked reduction during winter. Deltochilum mexicanum was dominant inside the forest, and its abundance was influenced by vegetation cover, temperature, and humidity. Dichotomius satanas was more frequent outside the forest, and none of the considered environmental variables affected its abundance. The adult sex ratio of Deltochilum mexicanum was female-biased, whereas that of Dichotomius satanas was male-biased. The maximum estimated population size was similar for both species, but Deltochilum mexicanum had a higher number of new individuals and survival rate. Since species with different biological attributes presented a similar pattern of abundance and population structure, we conclude that environmental conditions are the main regulator of dung beetle populations in the human-modified landscape.

Scale-dependent portfolio effects explain growth inflation and volatility reduction in landscape demography

Population demography is central to fundamental ecology and for predicting range shifts, decline of threatened species, and spread of invasive organisms. There is a mismatch between most demographic work, carried out on few populations and at local scales, and the need to predict dynamics at landscape and regional scales. Inspired by concepts from landscape ecology and Markowitz's portfolio theory, we develop a landscape portfolio platform to quantify and predict the behavior of multiple populations, scaling up the expectation and variance of the dynamics of an ensemble of populations. We illustrate this framework using a 35-y time series on gypsy moth populations. We demonstrate the demography accumulation curve in which the collective growth of the ensemble depends on the number of local populations included, highlighting a minimum but adequate number of populations for both regional-scale persistence and cross-scale inference. The attainable set of landscape portfolios further suggests tools for regional population management for both threatened and invasive species.

Asynchrony among local communities stabilises ecosystem function of metacommunities

Temporal stability of ecosystem functioning increases the predictability and reliability of ecosystem services, and understanding the drivers of stability across spatial scales is important for land management and policy decisions. We used species‐level abundance data from 62 plant communities across five continents to assess mechanisms of temporal stability across spatial scales. We assessed how asynchrony (i.e. different units responding dissimilarly through time) of species and local communities stabilised metacommunity ecosystem function. Asynchrony of species increased stability of local communities, and asynchrony among local communities enhanced metacommunity stability by a wide range of magnitudes (1–315%); this range was positively correlated with the size of the metacommunity. Additionally, asynchronous responses among local communities were linked with species’ populations fluctuating asynchronously across space, perhaps stemming from physical and/or competitive differences among local communities. Accordingly, we suggest spatial heterogeneity should be a major focus for maintaining the stability of ecosystem services at larger spatial scales.