Delineating generalized species boundaries from species distribution data and a species distribution model

Species distribution models (SDM) are commonly used to provide information about species ranges or extents, and often are intended to represent the entire area of potential occupancy or suitable habitat in which individuals occur. While SDMs can provide results over various geographic extents, they normally operate within a grid and cannot delimit distinct, smooth boundaries. Additionally, there are instances where a zone of primary occupancy (i.e., a mostly continuous region where species exists, excluding outliers) is better suited for particular analyses, such as when examining source/sink population dynamics or modeling movement into new habitats. We present a semi-automated method to delineate a generalized species boundary (GSB) from SDM output, which provides a practical alternative to digitizing. This preliminary boundary is then manually updated based on inventory data and historical ranges. We used the method to generate contemporary boundaries for 132 tree species of the eastern United States, which are complementary to the ranges generated by Elbert Little for North America during the 1970s, but are not replacements. The methods we present can broadly be applied to other grid-based SDM to generate GSBs.     

Incorporating movement in species distribution models: how do simulations of dispersal affect the accuracy and uncertainty of projections?

Species distribution models (SDMs) are one of the most important GIScience research areas in biogeography and are the primary means by which the potential effects of climate change on species’ distributions and ranges are investigated. Dispersal is an important ecological process for species responding to changing climates, however, SDMs and their subsequent spatial products rarely reflect accessibility to any future suitable environment. Dispersal-related movement can be confounded by factors that vary across landscapes and climates, as well as within and among species, and it has therefore remained difficult to parametrise in SDMs. Here we compared 20 models that have previously been used (or have the potential to be used) to represent dispersal processes in SDM to predict future range shifts in response to climate change. We assessed the different dispersal models in terms of their accuracy at predicting future distributions, as well as the uncertainty associated with their predictions. Atlas data for 50 bird species from 1988 to 1991 in Great Britain were treated as base distributions (t₁), with the species–environment relationships extrapolated (using three commonly used statistical methods) to 2008–2011 (t₂). Dispersal (in the form of the 20 different models) was simulated from the base distribution (t₁) to 2008–2011 (t₂). The results were then combined and used to identify locations that were both abiotically suitable (obtained from the statistical methods) and accessible (obtained from the dispersal models). The accuracy of these coupled projections was assessed with the 2008–2011 atlas data (the observed t₂ distribution). There was substantial variation in the accuracy of the different dispersal models, and in general, the more restrictive dispersal models (e.g. fixed rate dispersal) resulted in lower accuracy for the metrics which reward correct prediction of presences. Ensemble models of the dispersal methods (generated by combining multiple projection outcomes) were created for each species, and a new Ensemble Agreement Index (EAI), which ranges from 0 (no agreement among models) to 1 (full agreement among models) was developed to quantify uncertainty among the projections. EAI values ranged from 0.634 (some areas of disagreement and therefore medium uncertainty among dispersal models) to 0.999 (large areas of agreement and low uncertainty among dispersal models). The results of this research highlight the importance of incorporating dispersal and also illustrate that the method with which dispersal is simulated greatly impacts the projected future distribution. This has important implications for studies aimed at predicting the effects of changing environmental conditions on species’ distributions.     

Aggregating the conceptualization of movement data better captures real world and simulated animal–environment relationships

ABSTRACT

Habitat selection analysis is a widely applied statistical framework used in spatial ecology. Many of the methods used to generate movement and couple it with the environment are strongly integrated within GIScience. The choice of movement conceptualisation and environmental space can potentially have long-lasting implications on the spatial statistics used to infer movement–environment relationships. The aim of this study was to explore how systematically altering the conceptualisation of movement, environmental space and temporal resolution affects the results of habitat selection analyses using both real-world case studies and a virtual ecologist approach. Model performance and coefficient estimates did not differ between the finest conceptualisations of movement (e.g. vector and move), while substantial differences were found for the more aggregated representations (e.g. segment and area). Only segments modelled the expected movement–environment relationship with increasing linear feature resistance in the virtual ecologist approach and altering the temporal resolution identified inversions in the movement–environment relationship for vectors and moves. The results suggest that spatial statistics employed to investigate movement–environment relationships should advance beyond conceptualising movement as the (relatively) static conceptualisation of vectors and moves and replace these with (more) dynamic aggregations of longer-lasting movement processes such as segments and areal representations.     

A context-sensitive correlated random walk: a new simulation model for movement

Computational Movement Analysis focuses on the characterization of the trajectory of individuals across space and time. Various analytic techniques, including but not limited to random walks, Brownian motion models, and step selection functions have been used for modeling movement. These fall under the rubric of signal models which are divided into deterministic and stochastic models. The difficulty of applying these models to the movement of dynamic objects (e.g. animals, humans, vehicles) is that the spatiotemporal signal produced by their trajectories a complex composite that is influenced by the Geography through which they move (i.e. the network or the physiography of the terrain), their behavioral state (i.e. hungry, going to work, shopping, tourism, etc.), and their interactions with other individuals. This signal reflects multiple scales of behavior from the local choices to the global objectives that drive movement. In this research, we propose a stochastic simulation model that incorporates contextual factors (i.e. environmental conditions) that affect local choices along its movement trajectory. We show how actual global positioning systems observations can be used to parameterize movement and validate movement models and argue that incorporating context is essential in modeling movement.     

Valorization of Agricultural By-Products Within the “Energyscapes”: Renewable Energy as Driving Force in Modeling Rural Landscape

Due to several growing environmental constraints, renewable energy sources currently play an increasingly crucial role that, owing to their high temporal and spatial variability, needs a careful planning approach. It is important therefore to develop a framework that examines the distribution of different energy sources in a spatio-temporal context. From an energy point of view, a regional territory, such as an internal southern Italian region (the Basilicata Region), can be considered as a paradigmatic case study, because it is characterized by significant sources of renewable energy (e.g., biomass, wind, solar, hydro) connected to its morphological and environmental structure, as well as to its agricultural and food productions. The present paper in based on an analysis of the spatial supply and relationships between renewable energy potentials and rural land, through the use of a Geographic Information System that has been implemented with the aim to analyze the energy system, as well as to optimize the valorization of biomass resources that may be still unutilized within the bioenergy production chain. This study has been carried out even to bridge the gap between energy systems modeling and landscape planning. Within this context, an “Energyscape” may be considered as the effect resulting on the rural landscape from a combination of the supply, demand and infrastructure for energy. This framework could be therefore a starting point for an interdisciplinary analysis able to figure out optimal solutions in decision-making processes, which duly respect the protection and restoring of endangered ecosystems, supporting the decision about an optimal spatial localization of energy plants as well.

     

How do species and data characteristics affect species distribution models and when to use environmental filtering?

ABSTRACT

Species distribution models (SDMs) are widely used in ecology and conservation. However, their performance is known to be affected by a variety of factors related to species occurrence characteristics. In this study, we used a virtual species approach to overcome the difficulties associated with testing of combined effects of those factors on performance of presence-only SDMs when using real data. We focused on the individual and combined roles of factors related to response variable (i.e. sample size, sampling bias, environmental filtering, species prevalence, and species response to environmental gradients). Results suggest that environmental filtering is not necessarily helpful and should not be performed blindly, without evidence of bias in species occurrences. The more gradual the species response to environmental gradients is, the greater is the model sensitivity to an inappropriate use of environmental filtering, although this sensitivity decreases with higher species prevalence. Results show that SDMs are affected to the greatest degree by the species response to environmental gradients, species prevalence, and sample size. Models’ accuracy decreased with sample size below 300 presences. Furthermore, a high level of interactions among individual factors was observed. Ignoring the combined effects of factors may lead to misleading outcomes and conclusions.     

LUCC驱动力模型研究综述

驱动力研究是土地利用变化研究中的核心问题。土地利用变化驱动力模型是分析土地利用变化原因和结果的有力工具，模型通过情景分析可为土地利用规划与决策提供依据。基于不同理论的驱动力研究方法很多，论文选取了几种国内外应用较多的LUCC驱动力模型进行综述，分析了每个模型的优缺点及适用范围，最后得出结论：1) 基于过程的动态模型更适于研究复杂的土地利用系统。2) 基于经验的统计模型能弥补基于过程的动态模型的不足。3) 基于不同学科背景的模型进一步集成将是LUCC驱动力模型未来的发展趋势。     

Modeling mobile individuals in dynamic landscapes

Geographic modeling systems should support simulation of mobile individuals in dynamic landscapes. In modeling a small population (e.g. an endangered species), it is important to keep track of individuals over time and space, interactions between individuals, landscape processes over time and space, interactions among landscape processes, and interactions of individuals with landscape processes. An object-oriented simulation modeling system was designed to meet these requirements. A prototype demonstrates the capabilities with hypothetical animals in static and dynamic landscapes.     

A hybrid parallel cellular automata model for urban growth simulation over GPU/CPU heterogeneous architectures

As an important spatiotemporal simulation approach and an effective tool for developing and examining spatial optimization strategies (e.g., land allocation and planning), geospatial cellular automata (CA) models often require multiple data layers and consist of complicated algorithms in order to deal with the complex dynamic processes of interest and the intricate relationships and interactions between the processes and their driving factors. Also, massive amount of data may be used in CA simulations as high-resolution geospatial and non-spatial data are widely available. Thus, geospatial CA models can be both computationally intensive and data intensive, demanding extensive length of computing time and vast memory space. Based on a hybrid parallelism that combines processes with discrete memory and threads with global memory, we developed a parallel geospatial CA model for urban growth simulation over the heterogeneous computer architecture composed of multiple central processing units (CPUs) and graphics processing units (GPUs). Experiments with the datasets of California showed that the overall computing time for a 50-year simulation dropped from 13,647 seconds on a single CPU to 32 seconds using 64 GPU/CPU nodes. We conclude that the hybrid parallelism of geospatial CA over the emerging heterogeneous computer architectures provides scalable solutions to enabling complex simulations and optimizations with massive amount of data that were previously infeasible, sometimes impossible, using individual computing approaches.     

In Situ Maintenance of Traditional Ecological Knowledge on Malekula Island,Vanuatu

Traditional ecological knowledge (TEK) guides resource management across the globe, but is at risk amid social and ecological change. This has prompted numerous calls for TEK maintenance efforts, but these remain largely unexamined in the literature. Here, we discuss three examples of in situ TEK maintenance from Malekula Island in Vanuatu, locally known as kastom schools. Based on qualitative data, we find that the kastom schools may create several opportunities to maintain TEK (e.g., establishing local control over education), and argue that they represent the creative and adaptive management of tradition in dynamic social–ecological contexts. However, a number of challenges, both practical (e.g., lack of funding) and epistemological (e.g., changing modes of cultural transmission), threaten the efficacy of the kastom schools. We argue that in situ modes of TEK maintenance have promise, but that issues of power and heterogeneity require serious consideration if such measures are to succeed.     

Phytoplankton composition and dynamics in three shallow temporary salt lakes (Monegros, Spain)

The present study deals with phytoplankton composition and dynamics of three ephemeral saline lakes (La Muerte, Piñol and Sulfúrica) situated in one of the most arid inland zone in Europe (Los Monegros). The lakes show marked differences among one another, although they are all temporary, shallow and saline systems (>30 g l⁻¹). La Muerte is dominated by microbial mats that are absent from the other two. Piñol is more exposed to strong winds than the other two lakes and exhibits higher turbidity. Sulfúrica appears to be the most extreme system, with the highest salinity, which leads to a complete absence of macrozooplankton. Phytoplankton composition in these lakes varies as a consequence of the different physico-chemical conditions; it is fundamentally composed of typical hypersaline species such as Dunaliella sp., Aphanothece sp., and soil benthic species (e.g. Hantzschia amphyoxis) that appear to play an important role in these systems. Comparative analysis of the microbial component (phytoplankton diversity and temporal evolution) under in situ conditions and the presence of various abiotic and biotic factors, including the impact of macrozooplankton grazing, shows that it is easy to overlook large differences in the community structure of apparently similar ecosystems. The present study is a contribution to the understanding of the often-neglected microbial ecology of ephemeral shallow lakes.     

Assessing potential abiotic and biotic complications of crayfish-induced gravel transport in experimental streams

Biogeomorphology adds the element “biological dynamics” (of populations or communities) to chemical and physical geomorphic factors and thus complicates the framework of geomorphic processes. Such biological complications of the animal-induced transport of solids in streams should be particularly important in crayfish, as crayfish affect this transport through their overall activity and intraspecific aggression levels, which could be modified by shelter availability or the establishment of dominance hierarchies among individuals not knowing each other. Using experimental streams, we tested these hypotheses by measuring how shelter availability or residential crayfish group invasion by unknown individuals affected the impact of the crayfish Orconectes limosus on the (i) transport of gravel at baseflow (during 12 experimental days); (ii) sediment surface characteristics (after 12 days); and (iii) critical shear stress causing incipient gravel motion during simulated floods (after 12 days). The two potentially important factors shelter availability or residential group invasion negligibly affected the crayfish impact on gravel sediments, suggesting that habitat unfamiliarity (a third potentially important factor affecting crayfish activity) should increase the crayfish-induced sediment transport. Because habitat unfamiliarity is associated with sporadic long-distance migrations of a few crayfish individuals, this third factor should play a minor role in real streams, where crayfish biomass should be a key factor in relations with crayfish effects on sediments. Therefore, we combined the results of this study with those of previous crayfish experiments to assess how crayfish biomass could serve in modelling the gravel transport. Crayfish biomass explained 47% of the variability in the baseflow gravel transport and, in combination with the coefficient of variation of the bed elevation and algal cover, 72% of the variability in the critical gravel shear stress. These results encourage more research on the topic, as an increasing number of eliminations of abiotic and biotic factors that could complicate the animal-induced sediment transport in streams would facilitate the use of biological variables (e.g., bioturbator biomass) in future modelling of the transport of solids.     

Ecology of stone-encrusting organisms in the Greenland Sea—a review

The Arctic is one of the most rapidly warming regions on Earth. This area is therefore very suitable for conducting studies focused on the influence of climate change on the biota. Marine communities of coastal waters are particularly sensitive to the current environmental changes (e.g., ice-scour intensity); understanding how community structure changes in response to local perturbations is thus important for providing an insight into how future communities may respond to climate change. This review focuses on the fauna colonizing stones of the Greenland Sea. It summarizes the current state of knowledge about the ecology of organisms inhabiting these substrata across a range of depths, from the intertidal to deeper parts of the continental shelf. In the intertidal zone, no stable or developed assemblage on the rocks is visible. The intertidal zone seems to be fully controlled by physical forces. In contrast, below the intertidal zone a rich and abundant fauna starts to appear on these substrata. Both biotic (e.g., competitive interactions) and abiotic (e.g., ice scour, size of the rock) processes seem to shape stone assemblages in the subtidal zone, yet their influence varies with depth. For example, the abundance of encrusting organisms decreases with depth, as does the intensity of competitive interactions. However, species richness on rocks seems to be in general higher in the deeper parts of the shelf. Possible scenarios of climate change influence on the encrusting biota, gaps in our knowledge about the ecology of stone-dwelling faunal assemblages, as well as possible directions of future research, are discussed.     

Comparison of the Application of Different Process-based Models in the Study of Spatio-temporal Patterns of Ecosystem Service Value

Quantitative assessments of ecosystem service value (ESV) are of great significance for rational allocation of environmental resources and making regional ecological protection decisions. The method of equivalence factor per unit area is widely used for this purpose because of its simple algorithm. However, ESV is also affected by biotic and environmental factors (e.g., net ecosystem productivity (NEP) or precipitation), which are difficult to obtain at the regional scale, leading to uncertainty in ESV estimations. In this study, according to the equivalent factor modified by precipitation and NEP from four state-of-the-art process-based productivity models, i.e., CLM4.0, LPJDGVM, LPJGUESS and ORCHIDEE, we explored the temporal and spatial patterns of ESV of 15 administrative regions in northern China. The results show that the simulation accuracy of different models varied among four representative ecosystem types, i.e., typical steppe in northern China, alpine steppe in northwest China, farmland ecosystem in central China, and forest in northeast China, implying that model-based ESV estimates are ecosystem-specific. The ESV tends to decline from northeast to southwest in northern China. Regions with dense vegetation usually had high ESV due to better hydrological and thermal conditions. Low vegetation coverage areas, such as Qinghai and Xinjiang, had higher ESV because of their large geographical areas. The central and eastern developed regions without abundant natural resources had lower ESV due to their lower NEP. For different categories of ecosystem services, the regulation services (mainly water flow regulation services and climate regulation services) contributed the most to ESV. For the temporal dynamics, the total ESV of the 15 provinces, autonomous regions and municipalities showed an insignificant downward trend over the years. The regions with increasing trends of ESV were distributed in northwestern China, while pixels with decreasing trends of ESV were concentrated in northeastern China. Land use cover change may be the most important factor controlling the temporal dynamics of ESV. Our results can provide support for the enaction of reasonable strategies for ecological protection and economic development in northern China.     

Task-oriented information value measurement based on space-time prisms

Recent years have witnessed a large increase in the amount of information available from the Web and many other sources. Such an information deluge presents a challenge for individuals who have to identify useful information items to complete particular tasks in hand. Information value theory (IVT) from economics and artificial intelligence has provided some guidance on this issue. However, existing IVT studies often focus on monetary values, while ignoring the spatiotemporal properties which can play important roles in everyday tasks. In this paper, we propose a theoretical framework for task-oriented information value measurement. This framework integrates IVT with the space-time prism from time geography and measures the value of information based on its impact on an individual’s space-time prisms and its capability of improving task planning. We develop and formalize this framework by extending the utility function from space-time accessibility studies and elaborate it using a simplified example from time geography. We conduct a simulation on a real-world transportation network using the proposed framework. Our research could be applied to improving information display on small-screen mobile devices (e.g., smartwatches) by assigning priorities to different information items.     

Location,extent, and magnitude of dynamic topography in the Late Cretaceous Cordilleran Foreland Basin,USA: New insights from 3D flexural backstripping

Mantle-induced dynamic topography (i.e., subsidence and uplift) has been increasingly recognized as an important process in foreland basin development. However, characterizing and distinguishing the effects (i.e., location, extent and magnitude) of dynamic topography in ancient foreland basins remains challenging because the spatio-temporal footprint of dynamic topography and flexural topography (i.e., generated by topographic loading) can overlap. This study employs 3D flexural backstripping of Upper Cretaceous strata in the central part of the North American Cordilleran foreland basin (CFB) to better quantify the effects of dynamic topography. The extensive stratigraphic database and good age control of the CFB permit the regional application of 3D flexural backstripping in this basin for the first time. Dynamic topography started to influence the development of the CFB during the late Turonian to middle Campanian (90.2–80.2 Ma) and became the dominant subsidence mechanism during the middle to late Campanian (80.2–74.6 Ma). The area influenced by >100 m dynamic subsidence is approximately 400 by 500 km, within which significant (>200 m) dynamic subsidence occurs in an irregular-shaped (i.e., lunate) subregion. The maximum magnitude of dynamic subsidence is 300 ± 100 m based on the 80.2–74.6 Ma tectonic subsidence maps. With the maximum magnitude of dynamic uplift being constrained to be 200–300 m, the gross amount of dynamic topography in the Late Cretaceous CFB is 500–600 m. Although the location of dynamic subsidence revealed by tectonic subsidence maps is generally consistent with isopach map trends, tectonic subsidence maps developed through 3D flexural backstripping provide more accurate constraints of the areal extent, magnitude and rate of dynamic topography (as well as flexural topography) in the CFB through the Late Cretaceous. This improved understanding of dynamic topography in the CFB is critical for refining current geodynamic models of foreland basins and understanding the surface expression of mantle processes.     

基于知识图谱和综合征的科学环境知识挖掘——以民勤荒漠化为例

科学环境知识是实现区域土地可持续利用和管理的依据。民勤作为典型的荒漠化区域,对其荒漠化的发展过程及机制等科学环境知识的认识有助于指导干旱区土地荒漠化的防治。因此基于知识图谱方法,结合传统的文献综述方法,挖掘民勤科学环境知识并运用综合征框架进行知识组织管理。研究发现,常兆丰等、肖笃宁等、颉耀文等、康绍忠等、孙丹峰等、冯起等几个较成熟的团队在民勤进行了大量且较为成熟可信的研究,是本文获取科学环境知识的基础;民勤土地荒漠化研究主要集中于土地利用/覆被、荒漠化监测、景观格局变化,水资源利用及监测,荒漠植被演化和保护,环境变化及沙尘暴以及气候和人类活动的驱动5个相互联系耦合的热点方面;近60 a民勤土地荒漠化主要分为1949-1998年和1998-2010年主要的两个阶段,1949-1998年呈现耕地增长和荒漠化加剧的恶性循环过程,1998-2010年呈现荒漠化减弱以及地下水位下降的减缓,但仍表现为耕地的增长和自然植被的减少过程;并以民勤荒漠化的发展过程及机制为基础建立了综合征框架--民勤土地荒漠化是一个人类和自然综合作用下,土地利用/覆被、水资源、植被以及区域环境之间相互联系、相互影响的过程;科学环境知识综合征框架的挖掘集成可为后期系统建模与情景分析提供支持,为后续政策响应和措施制订提供参考。     

From small sets of GPS trajectories to detailed movement profiles: quantifying personalized trip-dependent movement diversity

ABSTRACT

The ubiquity of personal sensing devices has enabled the collection of large, diverse, and fine-grained spatio-temporal datasets. These datasets facilitate numerous applications from traffic monitoring and management to location-based services. Recently, there has been an increasing interest in profiling individuals' movements for personalized services based on fine-grained trajectory data. Most approaches identify the most representative paths of a user by analyzing coarse location information, e.g., frequently visited places. However, even for trips that share the same origin and destination, individuals exhibit a variety of behaviors (e.g., a school drop detour, a brief stop at a supermarket). The ability to characterize and compare the variability of individuals' fine-grained movement behavior can greatly support location-based services and smart spatial sampling strategies. We propose a TRip DIversity Measure --TRIM – that quantifies the regularity of users' path choice between an origin and destination. TRIM effectively captures the extent of the diversity of the paths that are taken between a given origin and destination pair, and identifies users with distinct movement patterns, while facilitating the comparison of the movement behavior variations between users. Our experiments using synthetic and real datasets and across geographies show the effectiveness of our method.     

Agent-based modeling within a cyberinfrastructure environment: a service-oriented computing approach

Agent-based models (ABM) allow for the bottom-up simulation of dynamics in complex adaptive spatial systems through the explicit representation of pattern–process interactions. This bottom-up simulation, however, has been identified as both data- and computing-intensive. While cyberinfrastrucutre provides such support for intensive computation, the appropriate management and use of cyberinfrastructure (CI)-enabled computing resources for ABM raise a challenging and intriguing issue. To gain insight into this issue, in this article we present a service-oriented simulation framework that supports spatially explicit agent-based modeling within a CI environment. This framework is designed at three levels: intermodel, intrasimulation, and individual. Functionalities at these levels are encapsulated into services, each of which is an assembly of new or existing services. Services at the intermodel and intrasimulation levels are suitable for generic ABM; individual-level services are designed specifically for modeling intelligent agents. The service-oriented simulation framework enables the integration of domain-specific functionalities for ABM and allows access to high-performance and distributed computing resources to perform simulation tasks that are often computationally intensive. We used a case study to investigate the utility of the framework in enabling agent-based modeling within a CI environment. We conducted experiments using supercomputing resources on the TeraGrid – a key element of the US CI. It is indicated that the service-oriented framework facilitates the leverage of CI-enabled resources for computationally intensive agent-based modeling.