Linking local ecological knowledge and habitat modelling to predict absolute species abundance on large scales

Assessing the spatial structure of abundance of a species is a basic requirement to carry out adequate conservation strategies. However, existing attempts to predict species abundance, particularly in absolute units and on large scales, are scarce and have led to weak results. In this work we present a scheme to obtain, in an affordable way, a predictive model of absolute animal abundance on large scales based on the modelling of data obtained from local ecological knowledge (LEK) and its calibration. To exemplify this scheme, we build and validate a predictive absolute abundance model of the endangered terrestrial tortoise Testudo graeca in Southeast Iberian Peninsula. For that purpose, we collected distribution and relative abundance data of T. graeca using a low cost methodology, such as LEK, by means of interviewing shepherds. The information from LEK was employed to build a predictive habitat-based model of relative abundance. The relative abundance model was transformed into an absolute abundance model by means of calibration with a classical absolute abundance sampling method such as distance sampling. The obtained absolute abundance model predicted the observed absolute abundances values well in independent locations when compared with other works (R ² = 36%) and thus can offer a cost-effective predictive ability. Our results show that reliable habitat-based predictive maps of absolute species abundance on regional scales can be obtained starting from low cost sampling methods of relative abundance, such as LEK, and its calibration.     

Inferring macro‐ecological patterns from local presence/absence data

Biodiversity provides support for life, vital provisions, regulating services and has positive cultural impacts. It is therefore important to have accurate methods to measure biodiversity, in order to safeguard it when we discover it to be threatened. For practical reasons, biodiversity is usually measured at fine scales whereas diversity issues (e.g. conservation) interest regional or global scales. Moreover, biodiversity may change across spatial scales. It is therefore a key challenge to be able to translate local information on biodiversity into global patterns. Many databases give no information about the abundances of a species within an area, but only its occurrence in each of the surveyed plots. In this paper, we introduce an analytical framework (implemented in a ready‐to‐use R code) to infer species richness and abundances at large spatial scales in biodiversity‐rich ecosystems when species presence/absence information is available on various scattered samples (i.e. upscaling). This framework is based on the scale‐invariance property of the negative binomial. Our approach allows to infer and link within a unique framework important and well‐known biodiversity patterns of ecological theory, such as the species accumulation curve (SAC) and the relative species abundance (RSA) as well as a new emergent pattern, which is the relative species occupancy (RSO). Our estimates are robust and accurate, as confirmed by tests performed on both in silico‐generated and real forests. We demonstrate the accuracy of our predictions using data from two well‐studied forest stands. Moreover, we compared our results with other popular methods proposed in the literature to infer species richness from presence to absence data and we showed that our framework gives better estimates. It has thus important applications to biodiversity research and conservation practice.     

Ontogenetic trait variation and metacommunity effects influence species relative abundances during tree community assembly

Predicting species abundance is one of the most fundamental pursuits of ecology. Combining the information encoded in functional traits and metacommunities provides a new perspective to predict the abundance of species in communities. We applied a community assembly via trait selection model to predict quadrat-scale species abundances using functional trait variation on ontogenetic stages and metacommunity information for over 490 plant species in a subtropical forest and a lowland tropical forest in Yunnan, China. The relative importance of trait-based selection, mass effects, and stochasticity in shaping local species abundances is evaluated using different null models. We found both mass effects and trait selection contribute to local abundance patterns. Trait selection was detectable at all studied spatial scales (0.04–1 ha), with its strength stronger at larger scales and in the subtropical forest. In contrast, the importance of stochasticity decreased with spatial scale. A significant mass effect of the metacommunity was observed at small spatial scales. Our results indicate that tree community assembly is primarily driven by ontogenetic traits and metacommunity effects. Our findings also demonstrate that including ontogenetic trait variation into predictive frameworks allows ecologists to infer ecological mechanisms operating in community assembly at the individual level.     

A statistical theory for sampling species abundances

The pattern of species abundances is central to ecology. But direct measurements of species abundances at ecologically relevant scales are typically unfeasible. This limitation has motivated a long-standing interest in the relationship between the abundance distribution in a large, regional community and the distribution observed in a small sample from the community. Here, we develop a statistical sampling theory to describe how observed patterns of species abundances are influenced by the spatial distributions of populations. For a wide range of regional-scale abundance distributions we derive exact expressions for the sampled abundance distributions, as a function of sample size and the degree of conspecific spatial aggregation. We show that if populations are randomly distributed in space then the sampled and regional-scale species-abundance distribution typically have the same functional form: sampling can be expressed by a simple scaling relationship. In the case of aggregated spatial distributions, however, the shape of a sampled species-abundance distribution diverges from the regional-scale distribution. Conspecific aggregation results in sampled distributions that are skewed towards both rare and common species. We discuss our findings in light of recent results from neutral community theory, and in the context of estimating biodiversity.     

Terrestrial–aquatic transitions: Local abundances and movements of mature female caddisflies are related to oviposition habits but not flight capability

1. Movement behaviours of adult aquatic insects can produce distinct spatial distribution patterns. Studies of adult abundance with distance away from water bodies are common and may invoke flight capability to explain species differences. In contrast, distribution patterns along river channels are poorly described, but are no less important for understanding population dynamics. Longitudinal patterns in adult abundance along short river lengths may differ between sexes and at different life stage transitions between aquatic and terrestrial environments, i.e. at emergence and oviposition. Flight capability is unlikely to influence longitudinal patterns created at emergence, but may influence local abundances of mature females seeking to lay eggs. We tested hypotheses about how local abundances of mature females might differ according to oviposition habits and flight capability.
2. We surveyed abundances of mature female caddisflies at adjacent riffle–pool pairs along short river lengths with homogeneous riparian cover. Our survey included nine species in three families (Hydrobiosidae, Leptoceridae, Hydropsychidae), which encompassed multiple different oviposition habits and a range of wing sizes and shapes. Several of the species oviposit preferentially in riffles. Accordingly, we tested for differences in female abundance between channel units (adjacent riffle–pool pairs). We also tested whether females attained higher abundances in some places along channels than others (i.e. over larger spatial scales and regardless of channel unit) which imply movements along the channel and aggregation in some locations. Wing morphology was used as a proxy measure of flight capability and included measures of wing span, area, aspect ratio and the second moment of wing area.
3. Three distinctly different distribution patterns of mature female caddisflies were identified. The abundance of three species varied over larger scales only (multiple channel units). Six species that oviposit preferentially in riffles had higher female abundances at riffles than pools, but for only one did abundances also vary over larger scales. There was no association between these different patterns and measures of wing morphology, after removing metrics that were correlated and that differed systematically between taxonomic families. However, we could not reject the hypothesis that some aspect of flight behaviour may have contributed to observed patterns.
4. The diverse but distinct distributions of mature female caddisflies we observed along short channel lengths are novel and suggest that species differ in their propensity for movement along streams, which could have consequences for local densities of eggs and juveniles in the aquatic environment. The degree to which population sizes are coupled across the terrestrial-to-aquatic transition is rarely investigated in aquatic insects and may provide fresh insight into sources of spatial variation within populations. Similarly, a more nuanced approach to research on the flight of aquatic insects, including age- and sex-specific phenomena, may provide greater insight into the diverse ecological functions and consequences of movement.

     

Modelling changes in species’ abundance in response to projected climate change

Aim Existing climate envelope models give an indication of broad scale shifts in distribution, but do not specifically provide information on likely future population changes useful for conservation prioritization and planning. We demonstrate how these techniques can be developed to model likely future changes in absolute density and population size as a result of climate change. Location Great Britain. Methods Generalized linear models were used to model breeding densities of two northerly‐ and two southerly‐distributed bird species as a function of climate and land use. Models were built using count data from extensive national bird monitoring data and incorporated detectability to estimate absolute abundance. Projections of likely future changes in the distribution and abundance of these species were made by applying these models to projections of future climate change under two emissions scenarios. Results Models described current spatial variation in abundance for three of the four species and produced modelled current estimates of national populations that were similar to previously published estimates for all species. Climate change was projected to result in national population declines in the two northerly‐distributed species, with declines for Eurasian curlew Numenius arquata projected to be particularly severe. Conversely, the abundances of the two southerly distributed species were projected to increase nationally. Projected maps of future abundance may be used to identify priority areas for the future conservation of each species. Main conclusions The analytical methods provide a framework to make projections of impacts of climate change on species abundance, rather than simply projected range changes. Outputs may be summarized at any spatial scale, providing information to inform future conservation planning at national, regional and local scales. Results suggest that as a consequence of climate change, northerly distributed bird species in Great Britain are likely to become an increasingly high conservation priority within the UK.     

Statistical methods for predicting the spatial abundance of reef fish species

Understanding the spatial distribution of organism abundance is fundamental to assessing and managing ecological populations. Marine species can be difficult and logistically challenging and expensive to observe. This often results in spatial data containing low detection rates when sampling underwater, biasing spatial predictions from many modeling approaches. We propose a multistage statistical workflow that can use zero inflated sampling data to develop non-linear predictive spatial distributions of reef fish abundance. The workflow includes: (1) an individual-based discrete event simulation which generates simulated survey data under different abundance settings; (2) empirical maximum likelihood analysis to establish the relationship between survey data and abundance from the simulation; (3) a two-step random smoothing method to estimate reliable block spatial abundance around each survey station; (4) an ensemble of different machine learning models which use the estimated abundance from step three as input to compute a stable non-linear prediction of abundance across the entire study area (Gulf of Mexico). Applying our workflow greatly improved the ability to forecast abundance at small spatial scales. The ability to forecast at fine spatial scales is critical when working with species that are patchily distributed. This workflow can apply to many ecological populations to develop abundance maps even if sample data is not well distributed across the study area or is zero inflated.     

Inside‐container effects drive mosquito community structure in Brazilian Atlantic forest

Metacommunity theory is a convenient framework in which to investigate how local communities linked by dispersal influence patterns of species distribution and abundance across large spatial scales. For organisms with complex life cycles, such as mosquitoes, different pressures are expected to act on communities due to behavioral and ecological partitioning of life stages. Adult females select habitats for oviposition, and resulting offspring are confined to that habitat until reaching adult stages capable of flight; outside‐container effects (OCE) (i.e., spatial factors) are thus expected to act more strongly on species distributions as a function of adult dispersal capability, which should be limited by geographic distances between sites. However, larval community dynamics within a habitat are influenced by inside‐container effects (ICE), mainly interactions with conspecifics and heterospecifics (e.g., through effects of competition and predation). We used a field experiment in a mainland‐island scenario to assess whether environmental, spatial, and temporal factors influence mosquito prey and predator distributions and abundances across spatial scales: within‐site, between‐site, and mainland‐island. We also evaluated whether predator abundances inside containers play a stronger role in shaping mosquito prey community structure than do OCE (e.g., spatial and environmental factors). Temporal influence was more important for predators than for prey mosquito community structure, and the changes in prey mosquito species composition over time appear to be driven by changes in predator abundances. There was a negligible effect of spatial and environmental factors on mosquito community structure, and temporal effects on mosquito abundances and distributions appear to be driven by changes in abundance of the dominant predator, perhaps because ICE are stronger than OCE due to larval habitat restriction, or because adult dispersal is not limited at the chosen spatial scales.     

Climate change likely to reduce orchid bee abundance even in climatic suitable sites

Studies have tested whether model predictions based on species’ occurrence can predict the spatial pattern of population abundance. The relationship between predicted environmental suitability and population abundance varies in shape, strength and predictive power. However, little attention has been paid to the congruence in predictions of different models fed with occurrence or abundance data, in particular when comparing metrics of climate change impact. Here, we used the ecological niche modeling fit with presence–absence and abundance data of orchid bees to predict the effect of climate change on species and assembly level distribution patterns. In addition, we assessed whether predictions of presence–absence models can be used as a proxy to abundance patterns. We obtained georeferenced abundance data of orchid bees (Hymenoptera: Apidae: Euglossina) in the Brazilian Atlantic Forest. Sampling method consisted in attracting male orchid bees to baits of at least five different aromatic compounds and collecting the individuals with entomological nets or bait traps. We limited abundance data to those obtained by similar standard sampling protocol to avoid bias in abundance estimation. We used boosted regression trees to model ecological niches and project them into six climate models and two Representative Concentration Pathways. We found that models based on species occurrences worked as a proxy for changes in population abundance when the output of the models were continuous; results were very different when outputs were discretized to binary predictions. We found an overall trend of diminishing abundance in the future, but a clear retention of climatically suitable sites too. Furthermore, geographic distance to gained climatic suitable areas can be very short, although it embraces great variation. Changes in species richness and turnover would be concentrated in western and southern Atlantic Forest. Our findings offer support to the ongoing debate of suitability–abundance models and can be used to support spatial conservation prioritization schemes and species triage in Atlantic Forest.     

Thematic and Spatial Resolutions Affect Model-Based Predictions of Tree Species Distribution

Subjective decisions of thematic and spatial resolutions in characterizing environmental heterogeneity may affect the characterizations of spatial pattern and the simulation of occurrence and rate of ecological processes, and in turn, model-based tree species distribution. Thus, this study quantified the importance of thematic and spatial resolutions, and their interaction in predictions of tree species distribution (quantified by species abundance). We investigated how model-predicted species abundances changed and whether tree species with different ecological traits (e.g., seed dispersal distance, competitive capacity) had different responses to varying thematic and spatial resolutions. We used the LANDIS forest landscape model to predict tree species distribution at the landscape scale and designed a series of scenarios with different thematic (different numbers of land types) and spatial resolutions combinations, and then statistically examined the differences of species abundance among these scenarios. Results showed that both thematic and spatial resolutions affected model-based predictions of species distribution, but thematic resolution had a greater effect. Species ecological traits affected the predictions. For species with moderate dispersal distance and relatively abundant seed sources, predicted abundance increased as thematic resolution increased. However, for species with long seeding distance or high shade tolerance, thematic resolution had an inverse effect on predicted abundance. When seed sources and dispersal distance were not limiting, the predicted species abundance increased with spatial resolution and vice versa. Results from this study may provide insights into the choice of thematic and spatial resolutions for model-based predictions of tree species distribution.     

A unified model explains commonness and rarity on coral reefs

Abundance patterns in ecological communities have important implications for biodiversity maintenance and ecosystem functioning. However, ecological theory has been largely unsuccessful at capturing multiple macroecological abundance patterns simultaneously. Here, we propose a parsimonious model that unifies widespread ecological relationships involving local aggregation, species‐abundance distributions, and species associations, and we test this model against the metacommunity structure of reef‐building corals and coral reef fishes across the western and central Pacific. For both corals and fishes, the unified model simultaneously captures extremely well local species‐abundance distributions, interspecific variation in the strength of spatial aggregation, patterns of community similarity, species accumulation, and regional species richness, performing far better than alternative models also examined here and in previous work on coral reefs. Our approach contributes to the development of synthetic theory for large‐scale patterns of community structure in nature, and to addressing ongoing challenges in biodiversity conservation at macroecological scales.     

Large-scale spatial variation in palm fruit abundance across a tropical moist forest estimated from high-resolution aerial photographs

Fruit abundance is a critical factor in ecological studies of tropical forest animals and plants, but difficult to measure at large spatial scales. We tried to estimate spatial variation in fruit abundance on a relatively large spatial scale using low altitude, high‐resolution aerial photography. We measured fruit production for all 555 individuals of the arborescent palm Astrocaryum standleyanum across 25 ha of mapped tropical moist forest on Barro Colorado Island, Panama, by visually counting fruits from the ground. Simultaneously, we used high‐resolution aerial photographs to map sun‐exposed crowns of the palm across the same area, which were then linked to ground‐mapped stems. First, we verified that the fruit crop size of individual trees was positively associated with both crown presence on aerial photos and crown area visible on aerial photos. Then, we determined how well spatial variation in Astrocaryum fruit density across the study area was predicted by spatial densities of photo‐detected crowns and crown area compared to spatial densities of ground‐mapped stems and stem diameters. We found a positive association of fruit crop size with crown visibility on aerial photographs. Although representing just one third of all individuals in the study area, photo‐detected crowns represented 57% of all fruits produced. The spatial pattern of photo‐detected crowns was strongly correlated with the spatial pattern of fruit abundance based on direct fruit counts, and correctly showed the areas with the highest and lowest fruit abundances. The spatial density of photo‐detected crowns predicted spatial variation in fruit abundance equally well as did the spatial density of ground‐mapped stems. Photo‐detected crown area did not yield a better prediction. Our study indicates that remote sensing of crowns can be a reliable and cost‐effective method for estimating spatial variation in fruit abundance across large areas for highly distinctive canopy species. Our study is also among the few to provide empirical evidence for a positive relationship between crown exposure of forest trees and fruit production.     

Incorporating ecological principles into statistical models for the prediction of species’ distribution and abundance

Understanding the determinants of species’ distributions and abundances is a central theme in ecology. The development of statistical models to achieve this has a long history and the notion that the model should closely reflect underlying scientific understanding has encouraged ecologists to adopt complex statistical methods as they arise. In this paper we describe a Bayesian hierarchical model that reflects a conceptual ecological model of multi‐scaled environmental determinants of riverine fish species’ distributions and abundances. We illustrate this with distribution and abundance data of a small‐bodied fish species, the Empire gudgeon Hypseleotris galii, in the Mary and Albert Rivers, Queensland, Australia. Specifically, the model sought to address; 1) the extent that landscape‐scale abiotic variables can explain the species’ distribution compared to local‐scale variables, 2) how local‐scale abiotic variables can explain species’ abundances, and 3) how are these local‐scale relationships mediated by landscape‐scale variables. Overall, the model accounted for around 60% of variation in the distribution and abundance of H. galii. The findings show that the landscape‐scale variables explain much of the distribution of the species; however, there was considerable improvement in estimating the species’ distribution with the addition of local‐scale variables. There were many strong relationships between abundance and local‐scale abiotic variables; however, several of these relationships were mediated by some of the landscape‐scale variables. The extent of spatial autocorrelation in the data was relatively low compared to the distances among sampling reaches. Our findings exemplify that Bayesian statistical modelling provides a robust framework for statistical modelling that reflects our ecological understanding. This allows ecologists to address a range of ecological questions with a single unified probability model rather than a series of disconnected analyses.     

Temporal turnover and the maintenance of diversity in ecological assemblages

Temporal variation in species abundances occurs in all ecological communities. Here, we explore the role that this temporal turnover plays in maintaining assemblage diversity. We investigate a three-decade time series of estuarine fishes and show that the abundances of the individual species fluctuate asynchronously around their mean levels. We then use a time-series modelling approach to examine the consequences of different patterns of turnover, by asking how the correlation between the abundance of a species in a given year and its abundance in the previous year influences the structure of the overall assemblage. Classical diversity measures that ignore species identities reveal that the observed assemblage structure will persist under all but the most extreme conditions. However, metrics that track species identities indicate a narrower set of turnover scenarios under which the predicted assemblage resembles the natural one. Our study suggests that species diversity metrics are insensitive to change and that measures that track species ranks may provide better early warning that an assemblage is being perturbed. It also highlights the need to incorporate temporal turnover in investigations of assemblage structure and function.     

Effect of Landscape Structure on Species Diversity

The effects of habitat fragmentation and their implications for biodiversity is a central issue in conservation biology which still lacks an overall comprehension. There is not yet a clear consensus on how to quantify fragmentation even though it is quite common to couple the effects of habitat loss with habitat fragmentation on biodiversity. Here we address the spatial patterns of species distribution in fragmented landscapes, assuming a neutral community model. To build up the fragmented landscapes, we employ the fractional Brownian motion approach, which in turn permits us to tune the amount of habitat loss and degree of clumping of the landscape independently. The coupling between the neutral community model, here simulated by means of the coalescent method, and fractal neutral landscape models enables us to address how the species–area relationship changes as the spatial patterns of a landscape is varied. The species–area relationship is one of the most fundamental laws in ecology, considered as a central tool in conservation biology, and is used to predict species loss following habitat disturbances. Our simulation results indicate that the level of clumping has a major role in shaping the species–area relationship. For instance, more compact landscapes are more sensitive to the effects of habitat loss and speciation rate. Besides, the level of clumping determines the existence and extension of the power-law regime which is expected to hold at intermediate scales. The distributions of species abundance are strongly influenced by the degree of fragmentation. We also show that the first and second commonest species have approximately self-similar spatial distributions across scales, with the fractal dimensions of the support of the first and second commonest species being very robust to changes in the spatial patterns of the landscape.     

物种多度格局研究进展

物种多度格局研究始于20世纪30年代,是种群生态学和群落生态学研究的起点。物种多度格局研究主要在两个水平上进行：1）初期研究主要集中于群落水平,希望在不同群落之间发现一个共同的整体格局来描述群落的组织结构。常用模型包括几何级数、对数级数、对数正态和断棍模型,不同模型代表了不同的生态学过程。2）目前转向重视物种水平,并以物种多度的区域分布规律及其生态学机制研究为主。物种分布区多度关系有正相关、无相关和负相关3种形式。局部多度高的物种一般趋于广布,而局部多度低的物种趋于受限分布。物种多度区域分布的生态位模型预测为单峰型,还经常会出现“热点地区”;而异质种群模型预测为双峰型。物种多度的区域分布主要由环境资源特性、物种生态位和扩散过程等因素决定。3）物种多度格局的时间变化与空间变异类似,代表了这些生态学过程的时间异质性。4）物种多度格局的尺度变化经常表现出自相似性,但该规律并非一直存在,因为生物多样性由不同尺度上的不同生态学过程决定。5）多度（稀有度）是物种保护的基本依据,而群落多度模型能够指示生态学和干扰过程变化对群落结构的影响。物种多度格局的模型手段仍需改进,机制研究尚不系统,应用研究亟待扩展,对于物种多度格局的深入理解将为揭示生物多样性分布机制和有效保护提供帮助。     

The quest for a null model for macroecological patterns: geometry of species distributions at multiple spatial scales

There have been several attempts to build a unified framework for macroecological patterns. However, these have mostly been based either on questionable assumptions or have had to be parameterized to obtain realistic predictions. Here, we propose a new model explicitly considering patterns of aggregated species distributions on multiple spatial scales, the property which lies behind all spatial macroecological patterns, using the idea we term 'generalized fractals'. Species' spatial distributions were modelled by a random hierarchical process in which the original 'habitat' patches were randomly replaced by sets of smaller patches nested within them, and the statistical properties of modelled species assemblages were compared with macroecological patterns in observed bird data. Without parameterization based on observed patterns, this simple model predicts realistic patterns of species abundance, distribution and diversity, including fractal-like spatial distributions, the frequency distribution of species occupancies/abundances and the species–area relationship. Although observed macroecological patterns may differ in some quantitative properties, our concept of random hierarchical aggregation can be considered as an appropriate null model of fundamental macroecological patterns which can potentially be modified to accommodate ecologically important variables.     

Applying conservation reserve design strategies to define ecosystem monitoring priorities

In an era of unprecedented ecological upheaval, monitoring ecosystem change at large spatial scales and over long‐time frames is an essential endeavor of effective environmental management and conservation. However, economic limitations often preclude revisiting entire monitoring networks at high frequency. We aimed here to develop a prioritization strategy for monitoring networks to select a subset of existing sites that meets the principles of complementarity and representativeness of the whole ecological reality, and maximizes ecological complementarity (species accumulation) and the spatial and environmental representativeness. We applied two well‐known approaches for conservation design, the “minimum set” and the “maximal coverage” problems, using a suite of alpha and beta biodiversity metrics. We created a novel function for the R environment that performs biodiversity metric comparisons and site prioritization on a plot‐by‐plot basis. We tested our procedures using plot data provided by the Terrestrial Ecosystem Research Network (TERN) AusPlots, an Australian long‐term monitoring network of 774 vegetation and soil monitoring plots. We selected 250 plots and 80% of the total species recorded as targets for the maximal coverage and minimum set problems, respectively. We compared the subsets selected by the different biodiversity metrics in terms of complementarity and spatial and environmental representativeness. We found that prioritization based on species turnover (i.e., iterative selection of the most dissimilar plot to a cumulative sample in terms of species replacement) maximized ecological complementarity and spatial representativeness, while also providing high environmental coverage. Species richness was an unreliable metric for spatial representation. Selection based on range‐rarity‐richness was balanced in terms of complementarity and representativeness, whereas its richness‐corrected implementation failed to capture ecological and environmental variation. Prioritization based on species turnover is desirable to cover the maximum variability of the whole network. Synthesis and applications: Our results inform monitoring design and conservation priorities, which can benefit by considering the turnover component of beta diversity in addition to univariate metrics. Our tool is computationally efficient, free, and can be readily applied to any species versus sites dataset, facilitating rapid decision‐making.     

Mapping multiple plant species abundance patterns - A multiobjective optimization procedure for combining reflectance spectroscopy and species ordination

Nature conservation and ecological restoration crucially depends on the knowledge about spatial patterns of plant species that control habitat conversion and disturbance regimes. Especially, species abundances are capable of indicating early development tendencies for setting habitat management strategies. This study demonstrates the transfer of field spectroscopy to hyperspectral imagery to map multiple plant species abundances in an open dryland area using two imaging spectrometers in two different phenological phases. We show that species abundances can partially be described by multiple gradients forming different coordinates in a contour map. For this purpose, species abundances were projected into an ordination space using non-metric multidimensional scaling and subsequent spatial interpolation. It was demonstrated that different gradients can be modeled in a Partial Least Squares regression framework resulting in distinct spectral features for certain gradient directions. We combine both objectives in a multiobjective NSGA-II procedure to maximize the quantitative determination of species abundance in ordination and spectral predictability in related field spectra, simultaneously. NSGA-II was finally used to select optimal spectral models for n = 35 single species that were transferred to hyperspectral imagery for mapping purpose. We can show that abundance predictabilities can be evaluated on the basis of individual model performances that hold different spectral features for each species in a designated phenological phase. Finally, we present spatially explicit multi-species maps for the best n = 18 and abundance maps for n = 8 models that could be linked to patterns of species richness, coexistence, succession stages and habitat type conditions.     

Species abundance distributions over time

It has been known for 50 years that the time period over which data are collected affects the shape of empirical species abundance distributions. However, despite a recent resurgence of interest in characterizing and explaining these patterns the temporal component of species abundance distributions has been largely ignored. I argue that it is essential to take account of time, and not only because sampling duration can have a profound influence on the perceived shape of the distribution. Partitions of species abundance distributions based on temporal occurrence in the record will facilitate tests of both biological and neutral models and may lead to a better understanding of rarity. These temporal partitions also have interesting, but as yet barely explored, parallels with spatial ones such as the core-satellite division. Moreover, changes in abundance distributions across all three of Preston's temporal scales (sampling time, ecological time and evolutionary time) present rich opportunities for ecological research.