Seasonality, density dependence, and spatial population synchrony.

Studies of spatial population synchrony constitute a central approach for understanding the drivers of ecological dynamics. Recently, identifying the ecological impacts of climate change has emerged as a new important focus in population synchrony studies. However, while it is well known that climatic seasonality and sequential density dependence influences local population dynamics, the role of season-specific density dependence in shaping large-scale population synchrony has not received attention. Here, we present a widely applicable analytical protocol that allows us to account for both season and geographic context-specific density dependence to better elucidate the relative roles of deterministic and stochastic sources of population synchrony, including the renowned Moran effect. We exemplify our protocol by analyzing time series of seasonal (spring and fall) abundance estimates of cyclic rodent populations, revealing that season-specific density dependence is a major component of population synchrony. By accounting for deterministic sources of synchrony (in particular season-specific density dependence), we are able to identify stochastic components. These stochastic components include mild winter weather events, which are expected to increase in frequency under climate warming in boreal and Arctic ecosystems. Interestingly, these weather effects act both directly and delayed on the vole populations, thus enhancing the Moran effect. Our study demonstrates how different drivers of population synchrony, presently altered by climate warming, can be disentangled based on seasonally sampled population time-series data and adequate population models.

The impact of within-host coinfection interactions on between-host parasite transmission dynamics varies with spatial scale.

Within-host interactions among coinfecting parasites can have major consequences for individual infection risk and disease severity. However, the impact of these within-host interactions on between-host parasite transmission, and the spatial scales over which they occur, remain unknown. We developed and apply a novel spatially explicit analysis to parasite infection data from a wild wood mouse (Apodemus sylvaticus) population. We previously demonstrated a strong within-host negative interaction between two wood mouse gastrointestinal parasites, the nematode Heligmosomoides polygyrus and the coccidian Eimeria hungaryensis, using drug-treatment experiments. Here, we show this negative within-host interaction can significantly alter the between-host transmission dynamics of E. hungaryensis, but only within spatially restricted neighbourhoods around each host. However, for the closely related species E. apionodes, which experiments show does not interact strongly with H. polygyrus, we did not find any effect on transmission over any spatial scale. Our results demonstrate that the effects of within-host coinfection interactions can ripple out beyond each host to alter the transmission dynamics of the parasites, but only over local scales that likely reflect the spatial dimension of transmission. Hence there may be knock-on consequences of drug treatments impacting the transmission of non-target parasites, altering infection risks even for non-treated individuals in the wider neighbourhood.

Additive partitioning of multispecies distributional aggregation of local assemblages.

The distribution of species is not random in space. At the finest-resolution spatial scale, that is, field sampling locations, distributional aggregation level of different species would be determined by various factors, for example spatial autocorrelation or environmental filtering. However, few studies have quantitatively measured the importance of these factors. In this study, inspired by the statistical properties of a Markov transition model, we propose a novel additive framework to partition local multispecies distributional aggregation levels for sequential sampling-derived field biodiversity data. The framework partitions the spatial distributional aggregation of different species into two independent components: regional abundance variability and the local spatial inertia effect. Empirical studies from field amphibian surveys through line-transect sampling in southwestern China (Minya Konka) and central-southern Vietnam showed that local spatial inertia was always the dominant mechanism structuring the local occurrence and distributional aggregation of amphibians in the two regions with a latitudinal gradient from 1200 to nearly 4000 m. However, regional abundance variability is still nonnegligible in highly diverse tropical regions (i.e. Vietnam) where the altitude is not higher than 2000 m. In summary, we propose a novel framework that shows that the multispecies distributional aggregation level can be structured by two additive components. The two partitioned components could be theoretically independent. These findings are expected to deepen our understanding of the local community structure from the perspective of both spatial distribution and regional diversity patterns. The partitioning framework might have potential applications in field ecology and macroecology research.

Scaling of Activity Space in Marine Organisms across Latitudinal Gradients.

AbstractUnifying models have shown that the amount of space used by animals (e.g., activity space, home range) scales allometrically with body mass for terrestrial taxa; however, such relationships are far less clear for marine species. We compiled movement data from 1,596 individuals across 79 taxa collected using a continental passive acoustic telemetry network of acoustic receivers to assess allometric scaling of activity space. We found that ectothermic marine taxa do exhibit allometric scaling for activity space, with an overall scaling exponent of 0.64. However, body mass alone explained only 35% of the variation, with the remaining variation best explained by trophic position for teleosts and latitude for sharks, rays, and marine reptiles. Taxon-specific allometric relationships highlighted weaker scaling exponents among teleost fish species (0.07) than sharks (0.96), rays (0.55), and marine reptiles (0.57). The allometric scaling relationship and scaling exponents for the marine taxonomic groups examined were lower than those reported from studies that had collated both marine and terrestrial species data derived using various tracking methods. We propose that these disparities arise because previous work integrated summarized data across many studies that used differing methods for collecting and quantifying activity space, introducing considerable uncertainty into slope estimates. Our findings highlight the benefit of using large-scale, coordinated animal biotelemetry networks to address cross-taxa evolutionary and ecological questions.

Spatial metapopulation dynamics with local and global colonization.

We revisit a spatial metapopulation model on continuous space as a stochastic point pattern dynamics. In the model, local patches as points are distributed with a certain spatial configuration and status of each patch changes stochastically between occupied and empty: an occupied patch becomes empty by local extinction and an empty patch becomes occupied both by local and global colonization. We carry out simulation analysis and derive an analytical model in terms of singlet, pair and triplet probabilities that describe the stochastic dynamics. Using a simple closure that approximates triplet probabilities by singlet and pair probabilities, we show that equilibrium singlet and pair probabilities can be analytically derived. The derived equilibrium properties successfully describe simulation results under a certain condition where the range of local colonization and the proportion of global colonization play key roles. Our model is an extension of the classical non-spatial Levins model to a spatially explicit metapopulation model. We appeal the advantage of point pattern approach to study spatial dynamics in general ecology and call for the need to deepen our understanding of mathematical tools to explore point pattern dynamics.

Building use-inspired species distribution models: Using multiple data types to examine and improve model performance.

Species distribution models (SDMs) are becoming an important tool for marine conservation and management. Yet while there is an increasing diversity and volume of marine biodiversity data for training SDMs, little practical guidance is available on how to leverage distinct data types to build robust models. We explored the effect of different data types on the fit, performance and predictive ability of SDMs by comparing models trained with four data types for a heavily exploited pelagic fish, the blue shark (Prionace glauca), in the Northwest Atlantic: two fishery dependent (conventional mark-recapture tags, fisheries observer records) and two fishery independent (satellite-linked electronic tags, pop-up archival tags). We found that all four data types can result in robust models, but differences among spatial predictions highlighted the need to consider ecological realism in model selection and interpretation regardless of data type. Differences among models were primarily attributed to biases in how each data type, and the associated representation of absences, sampled the environment and summarized the resulting species distributions. Outputs from model ensembles and a model trained on all pooled data both proved effective for combining inferences across data types and provided more ecologically realistic predictions than individual models. Our results provide valuable guidance for practitioners developing SDMs. With increasing access to diverse data sources, future work should further develop truly integrative modeling approaches that can explicitly leverage the strengths of individual data types while statistically accounting for limitations, such as sampling biases.

Diet and Spatial Ecology Influence Red-Legged Partridge Exposure to Pesticides Used as Seed Treatment.

Seed treatment with pesticides is an extended agricultural practice with a high risk to granivorous birds that consume those seeds. To characterize that risk, it is necessary to understand the ecological factors that determine the exposure chances of birds to treated seeds. We investigated how pesticide uptake by red-legged partridges was related to cultivated plant ingestion and to the use of recently sown fields. We analyzed pesticide residues in 144 fecal samples from 32 flocks and determined the plant diet composition using DNA metabarcoding. Habitat use was studied through the monitoring of 15 GPS-tagged partridges. We confirmed, through the analysis of seeds, that >80% of cereal fields from the area had seeds treated with triazole fungicides. Tebuconazole was detected in 16.6% of partridges' feces. During the sowing season, cultivated plants accounted for half of the plant diet, but no association was found between cultivated plant consumption and pesticide intake. GPS tracking revealed that tebuconazole was detected in feces when partridges had recently used sown fields, whereas nonexposed partridges showed no overlap with recently sown areas. Our results highlight the need to incorporate field ecology into the characterization of pesticide exposure to improve the efficacy of environmental risk assessment.

Catchment-based sampling of river eDNA integrates terrestrial and aquatic biodiversity of alpine landscapes.

Monitoring of terrestrial and aquatic species assemblages at large spatial scales based on environmental DNA (eDNA) has the potential to enable evidence-based environmental policymaking. The spatial coverage of eDNA-based studies varies substantially, and the ability of eDNA metabarcoding to capture regional biodiversity remains to be assessed; thus, questions about best practices in the sampling design of entire landscapes remain open. We tested the extent to which eDNA sampling can capture the diversity of a region with highly heterogeneous habitat patches across a wide elevation gradient for five days through multiple hydrological catchments of the Swiss Alps. Using peristaltic pumps, we filtered 60 L of water at five sites per catchment for a total volume of 1800 L. Using an eDNA metabarcoding approach focusing on vertebrates and plants, we detected 86 vertebrate taxa spanning 41 families and 263 plant taxa spanning 79 families across ten catchments. For mammals, fishes, amphibians and plants, the detected taxa covered some of the most common species in the region according to long-term records while including a few more rare taxa. We found marked turnover among samples from distinct elevational classes indicating that the biological signal in alpine rivers remains relatively localised and is not aggregated downstream. Accordingly, species compositions differed between catchments and correlated with catchment-level forest and grassland cover. Biomonitoring schemes based on capturing eDNA across rivers within biologically integrated catchments may pave the way toward a spatially comprehensive estimation of biodiversity.

Effects of dispersal rates in a two-stage reaction-diffusion system.

It is well known that in reaction-diffusion models for a single unstructured population in a bounded, static, heterogeneous environment, slower diffusion is advantageous. That is not necessarily the case for stage structured populations. In (Cantrell et al. 2020), it was shown that in a stage structured model introduced by Brown and Lin (1980), there can be situations where faster diffusion is advantageous. In this paper we extend and refine the results of (Cantrell et al. 2020) on persistence to more general combinations of diffusion rates and to cases where either adults or juveniles do not move. We also obtain results on the asymptotic behavior of solutions as diffusion rates go to zero, and on competition between species that differ in their diffusion rates but are otherwise ecologically identical. We find that when the spatial distributions of favorable habitats for adults and juveniles are similar, slow diffusion is still generally advantageous, but if those distributions are different that may no longer be the case.

Block-pulse integrodifference equations.

We present a hybrid method for calculating the equilibrium population-distributions of integrodifference equations (IDEs) with strictly increasing growth, for populations that are confined to a finite habitat-patch. This method is based on approximating the growth function of the IDE with a piecewise-constant function, and we call the resulting model a block-pulse IDE. We explicitly write out analytic expressions for the iterates and equilibria of the block-pulse IDEs as sums of cumulative distribution functions. We characterize the dynamics of one-, two-, and three-step block-pulse IDEs, including formal stability analyses, and we explore the bifurcation structure of these models. These simple models display rich dynamics, with numerous fold bifurcations. We then use three-, five-, and ten-step block-pulse IDEs, with a numerical root finder, to approximate models with compensatory Beverton-Holt growth and depensatory, or Allee-effect, growth. Our method provides a good approximation for the equilibrium distributions for compensatory and depensatory growth and offers numerical and analytical advantages over the original growth models.

Resource allocation in a PDE ecosystem model.

The effects of habitat heterogeneity on a diffusing population are investigated here. We formulate a reaction-diffusion system of partial differential equations to analyze the effect of resource allocation in an ecosystem with resource having its own dynamics in space and time. We show a priori estimates to prove the existence of state solutions given a control. We formulate an optimal control problem of our ecosystem model such that the abundance of a single species is maximized while minimizing the cost of inflow resource allocation. In addition, we show the existence and uniqueness of the optimal control as well as the optimal control characterization. We also establish the existence of an optimal intermediate diffusion rate. Moreover, we illustrate several numerical simulations with Dirichlet and Neumann boundary conditions with the space domain in 1D and 2D.

Evolutionary origins for ecological patterns in space.

Historically, many biologists assumed that evolution and ecology acted independently because evolution occurred over distances too great to influence most ecological patterns. Today, evidence indicates that evolution can operate over a range of spatial scales, including fine spatial scales. Thus, evolutionary divergence across space might frequently interact with the mechanisms that also determine spatial ecological patterns. Here, we synthesize insights from 500 eco-evolutionary studies and develop a predictive framework that seeks to understand whether and when evolution amplifies, dampens, or creates ecological patterns. We demonstrate that local adaptation can alter everything from spatial variation in population abundances to ecosystem properties. We uncover 14 mechanisms that can mediate the outcome of evolution on spatial ecological patterns. Sometimes, evolution amplifies environmental variation, especially when selection enhances resource uptake or patch selection. The local evolution of foundation or keystone species can create ecological patterns where none existed originally. However, most often, we find that evolution dampens existing environmental gradients, because local adaptation evens out fitness across environments and thus counteracts the variation in associated ecological patterns. Consequently, evolution generally smooths out the underlying heterogeneity in nature, making the world appear less ragged than it would be in the absence of evolution. We end by highlighting the future research needed to inform a fully integrated and predictive biology that accounts for eco-evolutionary interactions in both space and time.

Reproductive ecology of muskellunge (Esox masquinongy), an introduced predator, in the lower Wolastoq/Saint John River, New Brunswick, Canada.

Introduced predators can have harmful top-down effects on their newly colonized system through competition with and direct predation on native species. Following an initial introduction of muskellunge in Lac Frontière, Québec in the 1970s at the headwaters of the Wolastoq/Saint John River, the species rapidly migrated downstream, expanding its range by ~500 km over ~20 years. Despite this expansive colonization and concern over possible threats to native species, little is known about the basic ecology of muskellunge in this system. The last downstream barrier is the hydroelectric facility, Mactaquac Generating Station (MGS), 150 km upstream of the sea. While there are no downstream fish passage facilities at MGS, adult muskellunge have been recorded downstream. In this study, muskellunge (n = 23) were surgically tagged with very-high-frequency (VHF) radio or combined acoustic radio telemetry (CART) tags and tracked over two spawning seasons. We sought to determine if there was a reproducing population downstream of MGS and tracked Tagged muskellunge over two spawning seasons. We tracked fish to locate and confirm spawning sites, and followed up with egg and/or juvenile sampling surveys. Tagged muskellunge (90%) moved upstream towards the MGS during the spawning period in each year (2016 and 2017), where they remained throughout the entire spawning period. No spawning or nursery sites were confirmed near MGS, but in 2016 three distinct spawning locations and six distinct nursery sites were confirmed 10-12 km downstream amongst a chain of flooded islands. In 2016, eggs, sac-fry and juveniles were collected and confirmed as muskellunge by genetic sequencing, providing the first empirical observation of successful spawning downstream of MGS.

Southward range extension and transboundary movements of reef manta rays Mobula alfredi along the east African coastline.

Opportunistic in-water and aerial surveys in South Africa and the analysis of contributed citizen science data have extended the known range of reef manta rays Mobula alfredi along the eastern coast of Africa by 140 km (to Mdumbi Beach) and verified the first transboundary movements for the species. Additionally, six new long-range dispersal records have provided evidence of connectivity with the M. alfredi population off the Inhambane coastline of Mozambique. Five of these records captured one-way movements, the longest of which was an individual that travelled 505 km from Praia do Tofo to Sodwana Bay with 301 days between sightings. A single individual made a return trip between Závora, Mozambique and Sodwana Bay, South Africa (a total distance of ~870 km). These findings support the Convention on Migratory Species listing for the species, suggesting regional transboundary management units are warranted for this wide-ranging elasmobranch.

Spatial Ecology of Siberian Forest Reindeer (Rangifer tarandus valentinae Flerov, 1932) of Kuznetsk Alatau.

For the first time, information is provided on the size of the home range of Siberian forest reindeer (Rangifer tarandus valentinae) of the Altai-Sayan population of Kuznetsk Alatau, the minimum distance that the deer passes per day, their changes, and changes of habitats during the annual cycle. Possible reasons for the cyclical use of the habitat by forest reindeer are discussed.

Inferring spatially varying animal movement characteristics using a hierarchical continuous-time velocity model.

Understanding the spatial dynamics of animal movement is an essential component of maintaining ecological connectivity, conserving key habitats, and mitigating the impacts of anthropogenic disturbance. Altered movement and migratory patterns are often an early warning sign of the effects of environmental disturbance, and a precursor to population declines. Here, we present a hierarchical Bayesian framework based on Gaussian processes for analysing the spatial characteristics of animal movement. At the heart of our approach is a novel covariance kernel that links the spatially varying parameters of a continuous-time velocity model with GPS locations from multiple individuals. We demonstrate the effectiveness of our framework by first applying it to a synthetic data set and then by analysing telemetry data from the Serengeti wildebeest migration. Through application of our approach, we are able to identify the key pathways of the wildebeest migration as well as revealing the impacts of environmental features on movement behaviour.

Measuring habitat complexity and spatial heterogeneity in ecology.

Habitat complexity has been considered a key driver of biodiversity and other ecological phenomena for nearly a century. However, there is still no consensus over the definition of complexity or how to measure it. Up-to-date and clear guidance on measuring complexity is urgently needed, particularly given the rise of remote sensing and advent of technologies that allow environments to be scanned at unprecedented spatial extents and resolutions. Here we review how complexity is measured in ecology. We provide a framework for metrics of habitat complexity, and for the related concept of spatial heterogeneity. We focus on the two most commonly used complexity metrics in ecology: fractal dimension and rugosity. We discuss the pros and cons of these metrics using practical examples from our own empirical data and from simulations. Fractal dimension is particularly widely used, and we provide a critical examination of it drawing on research from other scientific fields. We also discuss informational metrics of complexity and their potential benefits. We chart a path forward for research on measuring habitat complexity by presenting, as a guide, sets of essential and desirable criteria that a metric of complexity should possess. Lastly, we discuss the applied significance of our review.

Epiphytic fungal communities vary by substrate type and at submetre spatial scales.

Fungal species have numerous important environmental functions. Where these functions occur will depend on how fungi are spatially distributed, but the spatial structures of fungal communities are largely unknown, especially in understudied hyperdiverse tropical tree canopy systems. Here we explore fungal communities in a Costa Rican tropical rainforest canopy, with a focus on local-scale spatial structure and substrate specificity of fungi. Samples of ~1 cm3 were collected from 135 points along five adjacent tree branches, with intersample distances from 1 to 800 cm, and dissected into four substrates: outer host tree bark, inner bark, dead bryophytes and living bryophytes. We sequenced the ITS2 region to characterize total fungal communities. Fungal community composition and diversity varied among substrate types, even when multiple substrates were in direct contact. Fungi were most diverse in living bryophytes, with 39% of all operational taxonomic units (OTUs) found exclusively in this substrate, and the least diverse in inner bark. Fungal communities had significant positive spatial autocorrelation and distance decay of similarity only at distances less than 1 m. Similarity among samples declined by half in less than 10 cm, and even at these short distances, similarities were low with few OTUs shared among samples. These results indicate that community turnover is high and occurs at very small spatial scales, with any two locations sharing very few fungi in common. High heterogeneity of fungal communities in space and among substrates may have implications for the distributions, population dynamics and diversity of other tree canopy organisms, including epiphytic plants.

A millennium of increasing diversity of ecosystems until the mid-20th century.

Land-use change is widely regarded as a simplifying and homogenising force in nature. In contrast, analysing global land-use reconstructions from the 10th to 20th centuries, we found progressive increases in the number, evenness, and diversity of ecosystems (including human-modified land-use types) present across most of the Earth's land surface. Ecosystem diversity increased more rapidly after ~1700 CE, then slowed or slightly declined (depending on the metric) following the mid-20th century acceleration of human impacts. The results also reveal increasing spatial differentiation, rather than homogenisation, in both the presence-absence and area-coverage of different ecosystem types at sub-global scales-at least, prior to the mid-20th century. Nonetheless, geographic homogenization was revealed for a subset of analyses at a global scale, reflecting the now-global presence of certain human-modified ecosystem types. Our results suggest that, while human land-use changes have caused declines in relatively undisturbed or "primary" ecosystem types, they have also driven increases in ecosystem diversity over the last millennium.

Topological early warning signals: Quantifying varying routes to extinction in a spatially distributed population model.

Understanding and predicting critical transitions in spatially explicit ecological systems is particularly challenging due to their complex spatial and temporal dynamics and high dimensionality. Here, we explore changes in population distribution patterns during a critical transition (an extinction event) using computational topology. Computational topology allows us to quantify certain features of a population distribution pattern, such as the level of fragmentation. We create population distribution patterns via a simple coupled patch model with Ricker map growth and nearest neighbors dispersal on a two dimensional lattice. We observe two dominant paths to extinction within the explored parameter space that depend critically on the dispersal rate d and the rate of parameter drift, Δϵ. These paths to extinction are easily topologically distinguishable, so categorization can be automated. We use this population model as a theoretical proof-of-concept for the methodology, and argue that computational topology is a powerful tool for analyzing dynamical changes in systems with noisy data that are coarsely resolved in space and/or time. In addition, computational topology can provide early warning signals for chaotic dynamical systems where traditional statistical early warning signals would fail. For these reasons, we envision this work as a helpful addition to the critical transitions prediction toolbox.