Abstract Aim Seedling recruitment is a vital process for forest regeneration and is influenced by various factors such as stand composition, climate, and soil disturbance. We conducted a long-term field experiment (18?years) to study the effects of these factors and their interactions on seedling recruitment. Location Our study focused on five main species in boreal mixed woods of eastern Canada: trembling aspen (Populus tremuloides), paper birch (Betula papyrifera), white spruce (Picea glauca), balsam fir (Abies balsamea), and white cedar (Thuja occidentalis). Methods Sixteen 1-m2 seedling monitoring subplots were set up in each of seven stands originating from different wildfires (fire years ranging from 1760 to 1944), with a soil scarification treatment applied to every other subplot. Annual new seedling counts were related to growing-season climate (mean temperature, growing degree days and drought code), scarification, and stand effects via a Bayesian generalized linear mixed model. Results Soil scarification had a large positive effect on seedling recruitment for three species (aspen, birch and spruce). As expected, high mean temperatures during the seed production period (two years prior to seedling emergence) increased seedling recruitment for all species but aspen. Contrary to other studies, we did not find a positive effect of dry conditions during the seed production period. Furthermore, high values of growing degree days suppressed conifer seedling recruitment. Except for white cedar, basal area was weakly correlated with seedling abundance, suggesting a small number of reproductive individuals is sufficient to saturate seedling recruitment. Conclusion Our findings underscore the importance of considering multiple factors, such as soil disturbance, climate, and stand composition, as well as their effects on different life stages when developing effective forest management strategies to promote regeneration in boreal mixed-wood ecosystems.

Climate, soil, and competition factors jointly drive tree growth variability at local and regional scale. However, the comprehensive interaction of these factors and their combined effects on tree responses within their environment remains poorly explored in current research. Using a detailed forest inventory dataset in Québec, we examined tree growth of balsam fir (Abies balsamea Mill), black spruce (Picea mariana Mill), red maple (Acer rubrum L.), sugar maple (Acer saccharum Marshall), white birch (Betula papyrifera Marshall), and yellow birch (Betula alleghaniensis Britton), as a function of competition for light and space with neighboring trees, climate and soil-related variables. Interactions between all these variables were considered in a Bayesian model predicting tree growth. The amount of light received by trees was the main variables explaining tree growth except for coniferous tree species which was influenced mostly by climate variables. Among the studied species, only red maple and white birch exhibited increased growth under warmer conditions. Intraspecific competition had strong species-specific impacts, varying from negative effects for balsam fir, to positive for red maple and yellow birch. Interactions between climate, soil, and competition played a crucial role in shaping growth patterns, especially for sugar maple, and black spruce that strongly responded to a combination of climate and competition factors. In general, tree growth also increased with the soil cation-exchange capacity (CEC), especially when higher CEC is coupled with higher temperatures and precipitation, except for black spruce. While anticipated climate conditions in Quebec, even under the most optimistic scenarios, will have a strong negative impact on the tree growth of most tree species, management can mitigate this impact by promoting tree diversity with more complex stand structures.

Current ecological models predict profound climate change-related effects on the natural disturbance regimes of forests. Spruce budworm (Choristoneura fumiferana) (SBW) is the principal insect defoliator in eastern North America, and SBW outbreaks have a major impact on the structure and function of the Canadian boreal forest, as defoliation leads to decreased tree growth, increased mortality, and lower forest productivity. SBW outbreaks have become more severe over the last century with the changing climate; however, little is known about how climate fluctuations affect the growth of SBW host species during the outbreak period. Here we evaluate how climate and outbreak severity combined to affect black spruce (Picea mariana) growth during the SBW outbreak that occurred between 1968–1988 and 2006–2017. We compiled dendrochronological series (2271 trees), outbreak severity (estimated by observed aerial defoliation), and climate data for 164 sites in Québec, Canada. We used a linear mixed effect model to determine the impacts of climatic parameters, cumulative defoliation (of the previous five years), and their coupled effect on basal area growth. At maximum outbreak severity, basal area growth of black spruce was reduced by 14%–18% over five years. This outbreak growth response was affected by climate: warmer previous summer minimum temperatures and a higher previous summer climate moisture index further decreased growth by 11% and 4%, respectively. In contrast, a preceding year's warmer spring minimum temperatures (9%) and summer maximum temperatures (7%) attenuated the negative SBW effect. This study adds knowledge to our landscape-level understanding of combined insect–climate effects and helps predictions of future SBW-related damage to forest stands to bolster sustainable forest management. We also recommend that projections of boreal forest ecosystems include several classes of SBW defoliation and multiple climatic scenarios in future simulations.

The boreal forest holds the world's largest soil carbon (C) reservoir. A large portion of it is contained in a thick organic layer originating from the slow decay of bryophytes. Because a thick organic layer slows down tree growth, reduces forest productivity, and thereby reduces the potential wood supply, silvicultural treatments that aim to maintain or restore forest productivity after harvesting often involve mechanical site preparation. However, while these treatments can increase growth and C storage in trees, they can also lead to accelerated decomposition of the soil organic matter, reducing C storage. In this study, we assessed the nine-years effect of two silvicultural treatments on soil C dynamics in forested peatlands of northwestern Quebec, compared to unharvested controls: (1) cut with protection of regeneration and soils (CPRS; low soil disturbance, also called careful logging around advanced growth (CLAAG)), (2) CPRS followed by mechanical site preparation (CPRS + MSP, plowing; severe soil disturbance). The mass loss rate of three bryophytes (Pleurozium schreberi, Sphagnum capillifolium, and Sphagnum fuscum) was measured over two growing seasons together with soil organic carbon (SOC) stocks. We also studied the different effects of temperature, water table level, depth, and type of soil layer on mosses decomposition.We observed a significant influence of silvicultural treatments, bryophyte species, and soil layer type (fibric, mesic, humic and mineral) on bryophyte mass loss, which was higher in the CPRS + MSP treatment (21.6 ± 0.13 % standard error) than in control sites (9.5 ± 0.21 %); CPRS alone resulted in an intermediate mass loss of 11.6 ± 0.23 %, for Sphagnum mosses. Bryophyte mass loss was significantly higher in fibric than humic layer. SOC stocks in the uppermost organic soil layer (fibric) were lower in the CPRS + MSP group than in the control group, while the CPRS group was intermediate; however, differences were not statistically significant for the other soil layer and for total SOC. We conclude that while CPRS + MSP accelerates Sphagnum moss decomposition in the topsoil layer, it has limited impact on total soil C stocks that are detectable with stock change methods.

With climate change, climatic optima are shifting poleward more rapidly than tree migration processes, resulting in a mismatch between species distributions and bioclimatic envelopes. Temperate hardwood tree species may take advantage of the release of climate constraints and forest management to migrate into the boreal forest. Here, we use the SORTIE-ND forest simulation model to determine the potential for the persistence of three temperate species (sugar maple, red maple and yellow birch) when introduced at seedling stage in typical balsam fir–paper birch (BF–PB) bioclimatic domain stands of eastern Canada, quantifying the consequences on the native species composition. SORTIE-ND is a spatially explicit, individual-based forest stand model that simulates tree growth, regeneration and mortality. We performed a novel parameterization of the SORTIE-ND tree growth equation allowing for the inclusion of climate modifiers on tree growth. After validating our model with data from permanent forest inventory plots, we modeled the dynamics of unharvested stands at different successional stages, as well as post-harvest stands, after the addition of sugar maple, red maple and yellow birch seedlings at different densities. Our results show that current BF–PB domain climate conditions do not limit growth and survival of temperate species in boreal stands. Of the temperate species introduced, sugar maple had the lowest ability to grow and survive by the end of the simulation. Species assemblages of host stands were impacted by the presence of temperate species when the addition of seedlings was above 5000 temperate seedlings per hectare at the beginning of the simulation. For stands that were recently clear cut, temperate seedlings were unable to grow due to intense competition from aspen regeneration. Our results suggest that both current climate and competitive interactions between temperate species and boreal species should not impede the ability of temperate species to grow and survive in the BF–PB domain.

We present a broad class of semi-parametric models for time series of random sums of positive variables. Our methodology allows the number of terms inside the sum to be time-varying and is therefore well suited to many examples encountered in the natural sciences. We study the stability properties of the models and provide a valid statistical inference procedure to estimate the model parameters. It is shown that the proposed quasi-maximum likelihood estimator is consistent and asymptotically Gaussian distributed. This work is complemented by simulation results and applied to time series representing growth rates of white spruce (Picea glauca) trees from a few dozen sites in Québec (Canada). This time series spans 41 years, including one major spruce budworm (Choristoneura fumiferana) outbreak between 1968 and 1991. We found significant growth reductions related to budworm-induced defoliation up to two years post-outbreak. Our results also revealed the positive effects of maximum summer temperature, precipitation, and the climate moisture index on white spruce growth. We also identified the negative effects of the climate moisture index in the spring and the maximum temperature of the previous summer. However, the model’s performance on this data set was not improved when the interactions between climate and defoliation on growth were considered. This study represents a major advance in our understanding of budworm–climate–tree interactions and provides a useful tool to project the combined effects of climate and insect defoliation on tree growth in a context of greater frequency and severity of outbreaks coupled with the anticipated increases in temperature.

Aim

Detection of rare species is limited by their intrinsic nature and by the constraints associated with traditional field surveys. Remote sensing (RS) provides a powerful alternative to traditional detection methods through the increasing availability of RS products. Here, we assess the capacity of RS at high and medium resolution to detect rare plants with direct and indirect approaches, and how the performance of RS can be influenced by the characteristics of species.

Methods

An extensive literature review was conducted to synthesize the use of RS to detect or predict rare plant occurrence at high and medium resolution (<30 m and 30–300 m, respectively). The concept of “rarity” was based on Rabinowitz's rare species classification. The literature review was performed in Scopus for the period 1990–2020.

Results

While direct detection is often limited, it is possible with high and very high spatial resolution data for rare plants with distinctive traits. RS is also able to capture biophysical conditions driving rare plant distributions, which can indirectly provide accurate predictions for them. Both approaches have the potential to discover new populations of rare plants. RS can also feed SAMs of rare plants, which combined with SDMs can provide a valuable approach for rare plant detection. While direct detection is limited by the space occupied by a species within its habitat and its morphological, phenological and physiological characteristics, the predictive performance of RS-based SDMs (indirect detection) can be influenced by habitat size, habitat specificity and phenological features of rare plants. Similarly, model predictive performance can be influenced by the rarity form of the target species according to the rarity classification criteria.

Main conclusions.

With this synthesis, the strong potential of RS for the purposes of detection and prediction of rare plant has been highlighted, with practical applications for conservation and management.

A combination of wildfires and defoliating insect outbreaks play an important role in the natural successional dynamics of North American boreal mixedwood forests, which, in the long term, change the post‐disturbance composition and structure of forest stands. After stand‐replacing disturbances (mainly wildfires), early successional hardwoods typically dominate the affected areas. Provided enough time following disturbances, the increasing recruitment of mid‐ to late‐successional softwoods as well as the mortality of hardwoods gradually change forest composition from hardwoods to admixtures of hardwood‐conifer species and conifer‐dominated stands in mid and late successional stages, respectively. Such mixedwoods are abundant across the southern Canadian boreal forest. In boreal Canada, mixedwoods are the most structurally heterogeneous forest ecosystems, are highly productive, and form an important source of timber supply. Here we present the EASTERN BOREAL MIXEDWOODS CANADA data set, which documents the changes in composition and structure of stands originating from eight different wildfires representing a chronosequence of 249 yr since fire in eastern Canada. This data set has been used in several different projects to study and model the influence of natural (e.g., insect outbreaks) and anthropogenic disturbances (e.g., harvesting) on the dynamics of post‐fire stands. The data set covers a high range of variability in stand composition and structure, explained by species establishment, dominance, and mixture. It thus constitutes a useful source of information to trace the dynamics of the main boreal tree species of eastern North America, from their establishment to their replacement at different spatial scales (e.g., from stand to landscape level). Please cite this data paper when the data are used in publications. We also request that researchers and teachers inform us of how they are using the data. We are open to collaborate in developing or co‐authoring relevant research projects based on this data set.

Citizen science (CS) currently refers to the participation of non-scientist volunteers in any discipline of conventional scientific research. Over the last two decades, nature-based CS has flourished due to innovative technology, novel devices, and widespread digital platforms used to collect and classify species occurrence data. For scientists, CS offers a low-cost approach of collecting species occurrence information at large spatial scales that otherwise would be prohibitively expensive. We examined the trends and gaps linked to the use of CS as a source of data for species distribution models (SDMs), in order to propose guidelines and highlight solutions. We conducted a quantitative literature review of 207 peer-reviewed articles to measure how the representation of different taxa, regions, and data types have changed in SDM publications since the 2010s. Our review shows that the number of papers using CS for SDMs has increased at approximately double the rate of the overall number of SDM papers. However, disparities in taxonomic and geographic coverage remain in studies using CS. Western Europe and North America were the regions with the most coverage (73%). Papers on birds (49%) and mammals (19.3%) outnumbered other taxa. Among invertebrates, flying insects including Lepidoptera, Odonata and Hymenoptera received the most attention. Discrepancies between research interest and availability of data were as especially important for amphibians, reptiles and fishes. Compared to studies on animal taxa, papers on plants using CS data remain rare. Although the aims and scope of papers are diverse, species conservation remained the central theme of SDM using CS data. We present examples of the use of CS and highlight recommendations to motivate further research, such as combining multiple data sources and promoting local and traditional knowledge. We hope our findings will strengthen citizen-researchers partnerships to better inform SDMs, especially for less-studied taxa and regions. Researchers stand to benefit from the large quantity of data available from CS sources to improve global predictions of species distributions.

The impact of traditional even-aged forest management on landscape age structure, tree composition, and connectivity has been well documented. Very little, however, is known about the impact on stand structural diversity. This study aims to compare the structural and abiotic characteristics of forest stands disturbed by clearcut logging and by stand-replacing fire in Quebec’s boreal landscapes. We hypothesized that unlike fire, logging specifically targeted stands having a higher economic value, i.e., merchantable volume, leaving altered forest characteristics on post-harvested landscapes. We compared two aerial forest surveys of a 2200 km2 study area, one survey completed before any logging activity (preindustrial survey; 1980s), and the second survey collected >10 years after logging activity (modern survey; 2000s). Forest stands at the time of the preindustrial survey were primary forests. We identified stands as either burned, logged, or left aside after forest management of the area (remaining stands) between the two surveys and compared their structural and abiotic characteristics using logistic regression. The structural and abiotic characteristics of burned and logged stands differed significantly. Relative to the burned stands, logged stands were older, denser, and marked by poorer drainage and a higher proportion of black spruce; therefore post-harvest and post-burn landscapes differed in terms of their structural diversities. Traditional even-aged forest management has significantly altered the boreal forest landscape by targeting specific stands having higher economic value and leaving behind stands of lower economic value. Remaining high economic stands should be protected, and a more balanced approach to harvesting must be used in the context of ecosystem-based management.

Tree neighbourhood modelling has significantly contributed to our understanding of the mechanisms structuring communities. Investigations into the impact of neighbouring crowding on tree performance have generally been conducted at local scales, missing important regional-scale context such as the suitability of the climate for each species. Favourable climates may enhance tree performance, but this may come at the cost of increased neighbourhood crowding and competition negatively impacting survival and growth. Through the synthesis of continental-scale forest inventory and trait datasets from the northeast USA and Puerto Rico we present an analytical approach that elucidates the important interactions between local competitive and regional climatic contexts. Our results show strong asymmetries in competitive interactions and significant niche differences that are dependent on habitat suitability. The strong interaction between local neighbourhood and regional climate highlights the need for models that consider the interaction between these two processes that have been previously ignored.

Biophysical conditions, including climate, environmental stress, and habitat availability, are key drivers of many ecological processes (e.g., community assembly and productivity) and associated ecosystem services (e.g., carbon sequestration and fishery production). Furthermore, anthropogenic impacts such as coastal development and fishing can have drastic effects on the structure and function of marine ecosystems. Scientists need to account for environmental variation and human impacts to accurately model, manage, and conserve marine ecosystems. Although there are many types of environmental data available from global remote sensing and open-source data products, some are inaccessible to potential end-users because they exist as global layers in high temporal and spatial resolutions which require considerable computational power to process. Additionally, coastal locations often suffer from missing data or data quality issues which limit the utility of some global marine products for coastal sites. Herein we present the Marine Socio-Environmental Covariates dataset for the global oceans, which consists of environmental and anthropogenic variables summarized in ecologically relevant ways. The dataset includes four sets of environmental variables related to biophysical conditions (net primary productivity models corrected for shallow-water reflectance, wave energy including sheltered-coastline corrections) and landscape context (coral reef and land cover within varying radii). We also present two sets of anthropogenic variables, human population density (within varying radii) and distance to large population center, which can serve as indicators of local human impacts. We have paired global, summarized layers available for download with an online data querying platform that allows users to extract data for specific point locations with finer control of summary statistics. In creating these global layers and online platform, we hope to make the data accessible to a wide array of end-users with the goal of advancing marine ecosystem studies.

Although amphibians typically exhibit high site fidelity and low dispersal, they do undertake rare, long-distance movements. The factors influencing these events remain poorly understood, partly because amphibian spring movements tend to radiate from breeding sites and the animals are often difficult to locate at other times of the year. In this study, we investigate whether these movement patterns can be reproduced by a parsimonious model where foraging steps follow a heavy-tailed, Lévy alpha-stable distribution and individuals may either return to a previous refuge site or establish a new one. We consider three versions of the return behaviour: (1) a distance-independent probability of return to any previous refuge; (2) constant probability of return to the nearest refuge; or (3) a distance-dependent probability of return to each refuge. Using approximate Bayesian computation, we fit each version of the model to radiotracking data from a population of Fowler’s Toads, which inhabits a linear sand dune habitat on the north shore of Lake Erie in Ontario, Canada. Only the model with distance-independent, random returns provides a good fit of the inter-refuge distance distribution and the number of refuges visited per toad. Our results suggest that while toads occasionally forage over long distances, the establishment of new refuges is not driven by the minimization of energy expenditure.

While a growing proportion of global food consumption is obtained through international trade, there is an ongoing debate on whether this increased reliance on trade benefits or hinders food security, and specifically, the ability of global food systems to absorb shocks due to local or regional losses of production. This paper introduces a model that simulates the short-term response to a food supply shock originating in a single country, which is partly absorbed through decreases in domestic reserves and consumption, and partly transmitted through the adjustment of trade flows. By applying the model to publicly-available data for the cereals commodity group over a 17 year period, we find that differential outcomes of supply shocks simulated through this time period are driven not only by the intensification of trade, but as importantly by changes in the distribution of reserves. Our analysis also identifies countries where trade dependency may accentuate the risk of food shortages from foreign production shocks; such risk could be reduced by increasing domestic reserves or importing food from a diversity of suppliers that possess their own reserves. This simulation-based model provides a framework to study the short-term, nonlinear and out-of-equilibrium response of trade networks to supply shocks, and could be applied to specific scenarios of environmental or economic perturbations.

The composition of social bees’ corbicular pollen loads contains information about both the bees’ foraging behavior and the surrounding floral landscape. There have been, however, few attempts to integrate pollen composition and floral landscape to test hypotheses about foraging behavior. Here, we present an individual-based model that generates the species composition of pollen loads given a foraging model and a spatial distribution of floral resources. We apply this model to an existing dataset of inflorescence counts and bumble bee pollen loads sampled at different field sites in California. For two out of three sites, a foraging model consisting in correlated random walks with constant preferences for each plant species provides a plausible fit for the observed distribution of pollen load content. Pollen load compositions at the third site could be explained by an extension of the model, where different preferences apply to the choice of an initial foraging patch and subsequent foraging steps. Since this model describes the expected level of pollen load differentiation due solely to the spatial clustering of conspecific plants, it provides a null hypothesis against which more complex descriptions of behavior (e.g. flower constancy) can be tested.

Ferrihydrite is a key reactive nanoparticle in the Earth surface environment that regulates nutrient and metal cycling in marine, lacustrine, and groundwater settings; yet its genesis is unknown and there is no mechanistic explanation of its nanostructural nature. We develop a model, based on established aqueous precipitation theory, for rough-surface phases growing by accretion of monomers under fast diffusion conditions. The model is entirely defined in terms of a minimal set of necessary microscopic (molecular-scale) characteristic parameters. It is applied to ferrihydrite where the needed microscopic parameters are constrained by some of the mineral’s known physico-chemical properties. Our model qualitatively reproduces the main nanostructural properties and known precipitation kinetics of ferrihydrite, predicting that ferrihydrite will be nanometric in size, will have a narrow particle size distribution (standard deviation width equal to a fraction of the average particle size), and will typically precipitate from supersaturated conditions within times ranging from a fraction of a second to several days, under normal environmental-proxy conditions. The model also suggests that different metastable structures of ferrihydrite having similar rough surfaces would kinetically compete during nucleation and growth to coexist in the final product, as observed by Janney et al. (2000a, 2001).