Sparse large trees in secondary and planted forests highlight the need to improve forest conservation and management

Large trees are essential for carbon storage and biodiversity conservation. While an increasing number of studies have focused on large trees in primary forests, little is known about them in secondary and planted forests. We surveyed 86,936 trees in secondary forests and 91,294 trees in planted forests in Zhejiang, China, to investigate the distribution patterns and determinants of large trees in these forests. We found a mean density of large trees (DBH ≥ 30 cm) of 15 ± 13 stems ha-1 in secondary forests and 11 ± 9 stems ha-1 in planted forests. Moreover, the mean density of trees with DBH ≥ 60 cm was 0.36 stems ha-1, indicating that large trees are particularly rare in secondary and planted forests. These large trees were primarily occurred in secondary forests that living in high-elevation area with less human exploitation and colder and wetter climates, and in planted forests with higher species richness and lower tree density. In addition, the density of large trees in these forests significantly increased with tree species richness and decreased with increasing tree density. These results indicate that the sparse large trees were the legacy of historical human activities in the studied area, but currently, the development of large trees is still limited by the improper forest structure characterized by low species diversity and high tree density. To better conserve large trees, there is an urgent need for enhanced conservation policies for secondary forests, such as establishing forest parks for forests with large trees, and implementing near-natural forest management practices for planted forests, which include planting mixed native tree species and maintaining moderate tree density.

Biodiversity survey and estimation for line-transect sampling

Conducting biodiversity surveys using a fully randomised design can be difficult due to budgetary constraints (e.g., the cost of labour), site accessibility, and other constraints. To this end, ecologists usually select representative line transects or quadrats from a studied area to collect individuals of a given species and use this information to estimate the levels of biodiversity over an entire region. However, commonly used biodiversity estimators such as Rao's quadratic diversity index (and especially the Gini-Simpson index) were developed based on the assumption of independent sampling of individuals. Therefore, their performance can be compromised or even misleading when applied to species abundance datasets that are collected from non-independent sampling. In this study, we utilise a Markov chain model and derive an associated parameter estimator to account for non-independence in sequential sampling. Empirical tests on two forest plots in tropical (Barro Colorado, Island of Panama) and subtropical (Heishiding Nature Reserve of Guangdong, China) regions and the continental-scale spatial distribution of Acacia species in Australia showed that our estimators performed reasonably well. The estimated parameter measuring the degree of non-independence of subsequent sampling showed that a non-independent effect is very likely to occur when using line transects to sample organisms in subtropical regions at both local and regional spatial scales. In summary, based on a first-order Markov sampling model and using Rao's quadratic diversity index as an example, our study provides an improvement in diversity estimation while simultaneously accounting for the non-independence of sampling in field biodiversity surveys. Our study presents one possible solution for addressing the non-independent sampling of individuals in biodiversity surveys.

Selfing rate variation within species is unrelated to life-history traits or geographic range position

PREMISE

In plants, populations and species vary widely along the continuum from outcrossing to selfing. Life-history traits and ecological circumstances influence among-species variation in selfing rates, but their general role in explaining intraspecific variation is unknown. Using a database of plant species, we test whether life-history traits, geographic range position, or abundance predict selfing rate variation among populations.

METHODS

We identified species where selfing rates were estimated in at least three populations at known locations. Two key life-history traits (generation time and growth form) were used to predict within-species selfing rate variation. Populations sampled within a species' native range were assessed for proximity to the nearest edge and abundance. Finally, we conducted linear and segmented regressions to determine functional relationships between selfing rate and geographic range position within species.

RESULTS

Selfing rates for woody species varied less than for herbs, which is explained by the lower average selfing rate of woody species. Relationships between selfing and peripherality or abundance significantly varied among species in their direction and magnitude. However, there was no general pattern of increased selfing toward range edges. A power analysis shows that tests of this hypothesis require studying many (i.e., 40+) populations.

CONCLUSIONS

Intraspecific variation in plant mating systems is often substantial yet remains difficult to explain. Beyond sampling more populations, future tests of biogeographic hypotheses will benefit from phylogeographic information concerning specific range edges, the study of traits influencing mating system (e.g., herkogamy), and measures of abundance at local scales (e.g., population density).

Distance-based methods for estimating density of nonrandomly distributed populations

Population density is the most basic ecological parameter for understanding population dynamics and biological conservation. Distance-based methods (or plotless methods) are considered as a more efficient but less robust approach than quadrat-based counting methods in estimating plant population density. The low robustness of distance-based methods mainly arises from the oversimplistic assumption of completely spatially random (CSR) distribution of a population in the conventional distance-based methods for estimating density of non-CSR populations in natural communities. In this study we derived two methods to improve on density estimation for plant populations of non-CSR distribution. The first method modified an existing composite estimator to correct for the long-recognized bias associated with that estimator. The second method was derived from the negative binomial distribution (NBD) that directly deals with aggregation in the distribution of a species. The performance of these estimators was tested and compared against various distance-based estimators by both simulation and empirical data of three large-scale stem-mapped forests. Results showed that the NBD point-to-tree distance estimator has the best and most consistent performance across populations with vastly different spatial distributions. This estimator offers a simple, efficient and robust method for estimating density for empirical populations of plant species.

Seasonal productivity drives aggregations of killer whales and other cetaceans over submarine canyons of the Bremer Sub-Basin, south-western Australia

Cetaceans are iconic predators that serve as important indicators of marine ecosystem health. The Bremer Sub-Basin, south-western Australia, supports a diverse cetacean community including the largest documented aggregation of killer whales (Orcinus orca) in Australian waters. Knowledge of cetacean distributions is critical for managing the area’s thriving ecotourism industry, yet is largely sporadic. Here we combined aerial with opportunistic ship-borne surveys during 2015–2017 to describe the occurrence of multiple cetacean species on a regional scale. We used generalised estimating equations to model variation in killer whale relative density as a function of both static and dynamic covariates, including seabed depth, slope, and chlorophyll a concentration, while accounting for autocorrelation. Encountered cetacean groups included: killer (n=177), sperm (n=69), long-finned pilot (n=29), false killer (n=2), and strap-toothed beaked (n=1) whales, as well as bottlenose (n=12) and common (n=5) dolphins. Killer whale numbers peaked in areas of low temperatures and high primary productivity, likely due to seasonal upwelling of nutrient-rich waters supporting high prey biomass. The best predictive model highlighted potential killer whale ‘hotspots’ in the Henry, Hood, Pallinup and Bremer Canyons. This study demonstrates the value of abundance data from platforms of opportunity for marine planning and wildlife management in the open ocean.

Support for a relationship between demography and modeled habitat suitability is scale dependent for the purple martin Progne subis

Species distribution models (SDMs) estimate habitat suitability for species in geographic space. They are extensively used in conservation under the assumption that there is a positive relationship between habitat suitability and species success and stability. Given the difficulties in obtaining demographic data across a species' range, this assumption is rarely tested. Here we provide a range-wide test of this relationship for the eastern subspecies of purple martin Progne subis subis. We build a well-supported SDM for the breeding range of the purple martin, and pair it with an unparalleled demographic dataset of nest success and local and regional abundance data for the species to test the proposed link between habitat suitability and fecundity and demography. We find a positive relationship between regional abundance and habitat suitability but no relationship between local abundance or fecundity and habitat suitability. Our data suggest that local success is driven largely by biotic and stochastic factors and raise the possibility that purple martins are experiencing a time lag in their distribution. More broadly our results call for caution in how we interpret SDMs and do not support the assumption that areas of high habitat suitability are the best areas for species persistence.

Conservation and production responses vary by disturbance intensity in a long-term forest management experiment

Reductions in management intensity are often proposed to support a broader range of beneficial ecosystem responses than traditional management approaches. However, few studies evaluate ecosystem responses across approaches. Also, managers lack information about how species traits mediate responses across management approaches, a potentially substantial source of spatial and temporal variation in population and community responses that if ignored may hinder effectiveness of management programs. We used data collected over eight years from a manipulative experiment to test how four forest management strategies influenced avian community composition and wood production. After harvesting, we evaluated responses to three levels of plant cover suppression (Light, Moderate, and Intensive herbicide applications) in relation to a control without herbicide. We predicted the Moderate and Intensive treatments would exert strong negative effects on leaf-gleaning insectivores, including species of conservation concern due to long-term population declines. However, given high forest productivity, we expected temporal duration of effects to be short. Richness of leaf-gleaning bird species was reduced by 20-50% during the first four years post-harvest (when herbicide treatments were on-going), but the effect size declined over the next four years once treatments were completed (13-20% reduction). Effect sizes were substantially smaller for the non-leaf-gleaner group during years 1-4 (19-27%) and disappeared during years 5-8 (2-3%). However, in our final year of observation, we did find an average of five fewer non-leaf-gleaner species on Light vs. Control units. In the last two years of observation, turnover probabilities for the leaf-gleaner species remained higher on all treatments compared to the Control (0.11-0.21), indicating that new species continued to colonize treatments. Planted conifers were 40-44% taller and 74-81% larger in diameter in the Moderate and Intensive treatments compared to the Control, leading to substantial gains in wood biomass. Current practices provided more balance between two ecosystem responses, avian diversity and wood production, compared to less intensive alternatives. When short-term negative effects occur, the spatial distribution of harvesting and regeneration regionally indicates that habitat is often available locally to support leaf-gleaning and non-leaf-gleaning bird populations while releasing other portions of the region for high priority conservation objectives including late-successional forest reserves.

Integrating multiple sources of ecological data to unveil macroscale species abundance

The pattern of species abundance, represented by the number of individuals per species within an ecological community, is one of the fundamental characteristics of biodiversity. However, despite their obvious significance in ecology and biogeography, there is still no clear understanding of these patterns at large spatial scales. Here, we develop a hierarchical modelling approach to estimate macroscale patterns of species abundance. Using this approach, estimates of absolute abundance of 1248 woody plant species at a 10-km-grid-square resolution over East Asian islands across subtropical to temperate biomes are obtained. We provide two examples of the basic and applied use of the estimated species abundance for (1) inference of macroevolutionary processes underpinning regional biodiversity patterns and (2) quantitative community-wide assessment of a national red list. These results highlight the potential of the elucidation of macroscale species abundance that has thus far been an inaccessible but critical property of biodiversity.

Measurement of species abundance is fundamental in ecology, yet challenging at large spatial scales. Here, the authors show estimates of abundance of 1248 woody plant species over the East Asian islands that highlight macroevolutionary processes of biodiversity and the status of the national red listing.

Is genomic diversity a useful proxy for census population size? Evidence from a species-rich community of desert lizards

Species abundance data are critical for testing ecological theory, but obtaining accurate empirical estimates for many taxa is challenging. Proxies for species abundance can help researchers circumvent time and cost constraints that are prohibitive for long-term sampling. Under simple demographic models, genetic diversity is expected to correlate with census size, such that genome-wide heterozygosity may provide a surrogate measure of species abundance. We tested whether nucleotide diversity is correlated with long-term estimates of abundance, occupancy and degree of ecological specialization in a diverse lizard community from arid Australia. Using targeted sequence capture, we obtained estimates of genomic diversity from 30 species of lizards, recovering an average of 5,066 loci covering 3.6 Mb of DNA sequence per individual. We compared measures of individual heterozygosity to a metric of habitat specialization to investigate whether ecological preference exerts a measurable effect on genetic diversity. We find that heterozygosity is significantly correlated with species abundance and occupancy, but not habitat specialization. Demonstrating the power of genomic sampling, the correlation between heterozygosity and abundance/occupancy emerged from considering just one or two individuals per species. However, genetic diversity does no better at predicting abundance than a single day of traditional sampling in this community. We conclude that genetic diversity is a useful proxy for regional-scale species abundance and occupancy, but a large amount of unexplained variation in heterozygosity suggests additional constraints or a failure of ecological sampling to adequately capture variation in true population size.

Community-level species' correlated distribution can be scale-independent and related to the evenness of abundance

The spatial distribution of species is not random; instead, individuals tend to gather, resulting in a non-random pattern. Previous studies used the independent negative binomial distribution (NBD) to model the distributional aggregation of a single species, in which the independence of the distribution of individuals of a species in different quadrats had been assumed. This way of analyzing aggregation will result in the scale-dependent estimation of the aggregation or shape parameter. However, because non-random (and therefore non-independent) distribution of individuals of a species in a finite area can be caused by either correlated or clumped distribution of individuals of a species between neighboring sites, an alternative model would assume that the distribution of individuals of a species over different sampling areas is multinomial. Here, we showed that, by assuming that regional species abundance followed a NBD while using a multinomial distribution to assign individuals of species in different non-overlapped sampling quadrats that are from a partition of the entire region (quantifying positive correlation or synchrony), the estimation of the shape parameter in this probabilistic model, which is the negative multinomial distribution (NMD), was scale-invariant (i.e., the estimated shape parameter is identical across different partitions of the study region). Accordingly, the estimation of the shape parameter was related to regional species distribution alone. This implied that, the shape parameter at the community level, using the NMD model, reflected the evenness of interspecific abundance. As a comparison, if the distribution of individuals of a single species followed independent NBDs as studied previously, the shape parameter would measure the evenness of intraspecific abundance (quantifying single-species' distributional aggregation). Moreover, our study highlighted the necessity for adjusting the model for the effects of unsampled species when studying community-level distributional patterns. Collectively, as long as a target area is partitioned into non-overlapping quadrats (no matter how their sizes vary), the proposed NMD model in this study, along with the independent NBDs model, can be jointly formulated as a framework to reconcile the scale-dependent debate on the shape parameter, unifying the relationship between inter- or intraspecific abundance and distributional patterns.

Wild boar mapping using population-density statistics: From polygons to high resolution raster maps

The wild boar is an important crop raider as well as a reservoir and agent of spread of swine diseases. Due to increasing densities and expanding ranges worldwide, the related economic losses in livestock and agricultural sectors are significant and on the rise. Its management and control would strongly benefit from accurate and detailed spatial information on species distribution and abundance, which are often available only for small areas. Data are commonly available at aggregated administrative units with little or no information about the distribution of the species within the unit. In this paper, a four-step geostatistical downscaling approach is presented and used to disaggregate wild boar population density statistics from administrative units of different shape and size (polygons) to 5 km resolution raster maps by incorporating auxiliary fine scale environmental variables. 1) First a stratification method was used to define homogeneous bioclimatic regions for the analysis; 2) Under a geostatistical framework, the wild boar densities at administrative units, i.e. subnational areas, were decomposed into trend and residual components for each bioclimatic region. Quantitative relationships between wild boar data and environmental variables were estimated through multiple regression and used to derive trend components at 5 km spatial resolution. Next, the residual components (i.e., the differences between the trend components and the original wild boar data at administrative units) were downscaled at 5 km resolution using area-to-point kriging. The trend and residual components obtained at 5 km resolution were finally added to generate fine scale wild boar estimates for each bioclimatic region. 3) These maps were then mosaicked to produce a final output map of predicted wild boar densities across most of Eurasia. 4) Model accuracy was assessed at each different step using input as well as independent data. We discuss advantages and limits of the method and its potential application in animal health.

Sampling scales define occupancy and underlying occupancy-abundance relationships in animals

Occupancy-abundance (OA) relationships are a foundational ecological phenomenon and field of study, and occupancy models are increasingly used to track population trends and understand ecological interactions. However, these two fields of ecological inquiry remain largely isolated, despite growing appreciation of the importance of integration. For example, using occupancy models to infer trends in abundance is predicated on positive OA relationships. Many occupancy studies collect data that violate geographical closure assumptions due to the choice of sampling scales and application to mobile organisms, which may change how occupancy and abundance are related. Little research, however, has explored how different occupancy sampling designs affect OA relationships. We develop a conceptual framework for understanding how sampling scales affect the definition of occupancy for mobile organisms, which drives OA relationships. We explore how spatial and temporal sampling scales, and the choice of sampling unit (areal vs. point sampling), affect OA relationships. We develop predictions using simulations, and test them using empirical occupancy data from remote cameras on 11 medium-large mammals. Surprisingly, our simulations demonstrate that when using point sampling, OA relationships are unaffected by spatial sampling grain (i.e., cell size). In contrast, when using areal sampling (e.g., species atlas data), OA relationships are affected by spatial grain. Furthermore, OA relationships are also affected by temporal sampling scales, where the curvature of the OA relationship increases with temporal sampling duration. Our empirical results support these predictions, showing that at any given abundance, the spatial grain of point sampling does not affect occupancy estimates, but longer surveys do increase occupancy estimates. For rare species (low occupancy), estimates of occupancy will quickly increase with longer surveys, even while abundance remains constant. Our results also clearly demonstrate that occupancy for mobile species without geographical closure is not true occupancy. The independence of occupancy estimates from spatial sampling grain depends on the sampling unit. Point-sampling surveys can, however, provide unbiased estimates of occupancy for multiple species simultaneously, irrespective of home-range size. The use of occupancy for trend monitoring needs to explicitly articulate how the chosen sampling scales define occupancy and affect the occupancy-abundance relationship.

Combining occurrence and abundance distribution models for the conservation of the Great Bustard

Species distribution models (SDMs) have become important and essential tools in conservation and management. However, SDMs built with count data, referred to as species abundance models (SAMs), are still less commonly used to date, but increasingly receiving attention. Species occurrence and abundance do not frequently display similar patterns, and often they are not even well correlated. Therefore, only using information based on SDMs or SAMs leads to an insufficient or misleading conservation efforts. How to combine information from SDMs and SAMs and how to apply the combined information to achieve unified conservation remains a challenge. In this study, we introduce and propose a priority protection index (PI). The PI combines the prediction results of the occurrence and abundance models. As a case study, we used the best-available presence and count records for an endangered farmland species, the Great Bustard (Otis tarda dybowskii), in Bohai Bay, China. We then applied the Random Forest algorithm (Salford Systems Ltd. Implementation) with eleven predictor variables to forecast the spatial occurrence as well as the abundance distribution. The results show that the occurrence model had a decent performance (ROC: 0.77) and the abundance model had a RMSE of 26.54. It is noteworthy that environmental variables influenced bustard occurrence and abundance differently. The area of farmland, and the distance to residential areas were the top important variables influencing bustard occurrence. While the distance to national roads and to expressways were the most important influencing abundance. In addition, the occurrence and abundance models displayed different spatial distribution patterns. The regions with a high index of occurrence were concentrated in the south-central part of the study area; and the abundance distribution showed high populations occurrence in the central and northwestern parts of the study area. However, combining occurrence and abundance indices to produce a priority protection index (PI) to be used for conservation could guide the protection of the areas with high occurrence and high abundance (e.g., in Strategic Conservation Planning). Due to the widespread use of SDMs and the easy subsequent employment of SAMs, these findings have a wide relevance and applicability than just those only based on SDMs or SAMs. We promote and strongly encourage researchers to further test, apply and update the priority protection index (PI) elsewhere to explore the generality of these findings and methods that are now readily available.

Estimating negative binomial parameters from occurrence data with detection times

The negative binomial distribution is a common model for the analysis of count data in biology and ecology. In many applications, we may not observe the complete frequency count in a quadrat but only that a species occurred in the quadrat. If only occurrence data are available then the two parameters of the negative binomial distribution, the aggregation index and the mean, are not identifiable. This can be overcome by data augmentation or through modeling the dependence between quadrat occupancies. Here, we propose to record the (first) detection time while collecting occurrence data in a quadrat. We show that under what we call proportionate sampling, where the time to survey a region is proportional to the area of the region, that both negative binomial parameters are estimable. When the mean parameter is larger than two, our proposed approach is more efficient than the data augmentation method developed by Solow and Smith (, Am. Nat. 176, 96-98), and in general is cheaper to conduct. We also investigate the effect of misidentification when collecting negative binomially distributed data, and conclude that, in general, the effect can be simply adjusted for provided that the mean and variance of misidentification probabilities are known. The results are demonstrated in a simulation study and illustrated in several real examples.

Abundance models improve spatial and temporal prioritization of conservation resources

Conservation prioritization requires knowledge about organism distribution and density. This information is often inferred from models that estimate the probability of species occurrence rather than from models that estimate species abundance, because abundance data are harder to obtain and model. However, occurrence and abundance may not display similar patterns and therefore development of robust, scalable, abundance models is critical to ensuring that scarce conservation resources are applied where they can have the greatest benefits. Motivated by a dynamic land conservation program, we develop and assess a general method for modeling relative abundance using citizen science monitoring data. Weekly estimates of relative abundance and occurrence were compared for prioritizing times and locations of conservation actions for migratory waterbird species in California, USA. We found that abundance estimates consistently provided better rankings of observed counts than occurrence estimates. Additionally, the relationship between abundance and occurrence was nonlinear and varied by species and season. Across species, locations prioritized by occurrence models had only 10-58% overlap with locations prioritized by abundance models, highlighting that occurrence models will not typically identify the locations of highest abundance that are vital for conservation of populations.