Two-dimensional habitat modeling of Chinese sturgeon spawning sites

Since the construction of the Gezhouba Dam in the 1980s, the number of Chinese sturgeon in the Yangtze River has been rapidly declining. The Gezhouba Dam has cutoff the migration path of these sturgeon, resulting in an overall reduction of suitable sturgeon habitat. This paper describes a habitat suitability index model that is used to evaluate the impacts of the Gezhouba Dam and Three Gorges Project on Chinese sturgeon spawning sites. Based on research concerning the reproduction characteristics of Chinese sturgeon, ten major ecological factors that influence reproduction were analyzed, including: water temperature, velocity, water depth, substrate, suspended sediment concentration, and the amount of egg predatory fish. The suitability index (SI) curves based on these ten ecological factors were obtained, and a habitat suitability function was developed. A two-dimensional mathematical model was also created to simulate and predict physical habitat situation (such as hydraulic, sediment, and substrate) of the Chinese sturgeon. By coupling the habitat suitability function and a two-dimensional mathematical model, a habitat suitability index model for Chinese sturgeon was established. The habitat suitability index model was validated by comparing measured data with predictions from the model. These comparisons showed that the computed results agreed well with the measured results, and the high calculated habitat suitability index (HSI) corresponded to high measured quantity of eggs per unit (1000 m³) discharge (CPUE_d). The calculated habitat suitability index for Chinese sturgeon also showed that the habitat suitability index was better in 1999, before the impoundment of the Three Gorges Project, compared with the habitat suitability in 2003. Simulation results of different discharges from Gezhouba Dam predicted that flow discharges between 10,000 and 30,000 m³/s were most suitable for sturgeon spawning.     

Predicting barrier passage and habitat suitability for migratory fish species

Fish migrate to spawn, feed, seek refuge from predators, and escape harmful environmental conditions. The success of upstream migration is limited by the presence of barriers that can impede the passage of fish. We used a spatially explicit modeling strategy to examine the effects of barriers on passage for 21 native and non-native migratory fish species and the amount of suitable habitat blocked for each species. Spatially derived physical parameter estimates and literature based fish capabilities and tolerances were used to predict fish passage success and habitat suitability. Both the fish passage and the habitat suitability models accurately predicted fish presence above barriers for most common, non-stocked species. The fish passage model predicted that barriers greater than or equal to 6 m block all migratory species. Chinook salmon (Oncorhynchus tshawytscha) was expected to be blocked the least. The habitat suitability model predicted that low gradient streams with intact habitat quality were likely to support the highest number of fish species. The fish passage and habitat suitability models were intended to be used by environmental managers as strategy development tools to prioritize candidate dams for field assessment and make decisions regarding the management of migratory fish populations.     

Predicting suitable habitat for the European lobster (Homarus gammarus), on the Basque continental shelf (Bay of Biscay), using Ecological-Niche Factor Analysis

Predicting species distribution and habitat suitability (HS) modelling, across broad spatial scales, is now a major challenge in marine ecology. The resulting knowledge is of considerable use in supporting the implementation of environmental legislation, integrated coastal zone management and ecosystem-based fisheries management. This contribution considers the identification of seafloor morphological characteristics, together with wave energy conditions, that determine the presence of European lobster (Homarus gammarus); and it predicts suitable habitats over the Basque continental shelf (Bay of Biscay), in summer. The results obtained, by applying Ecological-Niche Factor Analysis (ENFA), indicate that lobster habitat differs considerably from the mean environmental condition over the study area; likewise, that it is restrictive in terms of the range of conditions in which they dwell. The best of the environmental predictors found to be: distance to the rock substrate; Benthic Position Index; wave flux over the seafloor; and the underlying bathymetry. A habitat suitability map was produced, with a high model quality (Boyce index: 0.98 ± 0.06). The most suitable habitat for European lobster are locations at the boundary between sedimentary- and rocky-bottoms, coincident with seafloor depressions with a steep slope, with medium to high wave energy conditions, and located within a range of water depths of 35–40 m. This approach demonstrates the applicability of the method in case studies where only presence data are available, together with the inclusion of environmental variables obtained from different sources.     

Contrasting ecological information content in whaling archives with modern cetacean surveys for conservation planning and identification of historical distribution changes

Many species are restricted to a marginal or suboptimal fraction of their historical range due to anthropogenic impacts, making it hard to interpret their ecological preferences from modern-day data alone. However, inferring past ecological states is limited by the availability of robust data and biases in historical archives, posing a challenge for policy makers . To highlight how historical records can be used to understand the ecological requirements of threatened species and inform conservation, we investigated sperm whale (Physeter macrocephalus) distribution in the Western Indian Ocean. We assessed differences in information content and habitat suitability predictions based on whale occurrence data from Yankee whaling logs (1792–1912) and from modern cetacean surveys (1995–2020). We built maximum entropy habitat suitability models containing static (bathymetry-derived) variables to compare models comprising historical-only and modern-only data. Using both historical and modern habitat suitability predictions  we assessed marine protected area (MPA) placement by contrasting suitability in- and outside MPAs. The historical model predicted high habitat suitability in shelf and coastal regions near continents and islands, whereas the modern model predicted a less coastal distribution with high habitat suitability more restricted to areas of steep topography. The proportion of high habitat suitability inside versus outside MPAs was higher when applying the historical predictions than the modern predictions, suggesting that different marine spatial planning optimums can be reached from either data sources. Moreover, differences in relative habitat suitability predictions between eras were consistent with the historical depletion of sperm whales from coastal regions, which were easily accessed and targeted by whalers, resulting in a modern distribution limited more to steep continental margins and remote oceanic ridges. The use of historical data can provide important new insights and, through cautious interpretation, inform conservation planning and policy, for example, by identifying refugee species and regions of anticipated population recovery.     

Spatial memory, habitat auto-facilitation and the emergence of fractal home range patterns

Animals interact with their habitat in a manner which involves both negative and positive feedback mechanisms. We apply a specific modeling approach, “multi-scaled random walk”, for the scenario where a spatially explicit positive feedback process emerges from a combination of a spatial memory-dependent tendency to return to familiar patches and a consequently objective or subjective improvement of the quality of these patches (habitat auto-facilitation). In addition to the potential for local resource improvement from physically altering a patch, primarily known from the ecology of grazing ungulates, auto-facilitation from site fidelity may also embed more subtle subjective, individual-specific advantages from patch familiarity. Under the condition of resource superabundance, fitness gain from intra-home range patch fidelity creates a self-reinforcing use of the preferred patches on expense of a broader foraging in a priori equally favorable patches. Through this process, our simulations show that a spatially fractal dispersion of accumulated locations of the individual will emerge under the given model assumptions. Based on a conjecture that intra-home range patch fidelity depends on spatial memory we apply the multi-scaled random walk model to construct a spatially explicit habitat suitability parameter H_ij, which quantifies the dispersion of the generally most constraining resource from the individual's perspective. An intra-home range set of observed H-scores, H_obs, can then be estimated from a simple 2-scale calculation that is derived from the local dispersion of fixes. We show how the spatially explicit habitat utilization index H_obs not necessarily correlates positively with the local density fluctuations of fixes. The H-index solves some well-known problems from using the pattern of local densities of telemetry fixes - the classic utilization distribution - as a proxy variable for relative intra-home range habitat quality and resource selection. A pilot study on a set of telemetry fixes collected from a herd of free-ranging domestic sheep with overlapping summer home ranges illustrates how the H-index may be estimated and interpreted as a first-level approach towards a more extensive analysis of intra-home range habitat resource availability and patch preferences. Spatial memory in combination with site fidelity requires a modeling framework that explicitly describes the property of positive feedback mechanism under auto-facilitation in a spatio-temporally explicit manner.     

Using Niche-Based Models to Improve the Sampling of Rare Species

Abstract:  Because data on rare species usually are sparse, it is important to have efficient ways to sample additional data. Traditional sampling approaches are of limited value for rare species because a very large proportion of randomly chosen sampling sites are unlikely to shelter the species. For these species, spatial predictions from niche-based distribution models can be used to stratify the sampling and increase sampling efficiency. New data sampled are then used to improve the initial model. Applying this approach repeatedly is an adaptive process that may allow increasing the number of new occurrences found. We illustrate the approach with a case study of a rare and endangered plant species in Switzerland and a simulation experiment. Our field survey confirmed that the method helps in the discovery of new populations of the target species in remote areas where the predicted habitat suitability is high. In our simulations the model-based approach provided a significant improvement (by a factor of 1.8 to 4 times, depending on the measure) over simple random sampling. In terms of cost this approach may save up to 70% of the time spent in the field.     

Transcending scale dependence in identifying habitat with resource selection functions

Multi-scale resource selection modeling is used to identify factors that limit species distributions across scales of space and time. This multi-scale nature of habitat suitability complicates the translation of inferences to single, spatial depictions of habitat required for conservation of species. We estimated resource selection functions (RSFs) across three scales for a threatened ungulate, woodland caribou (Rangifer tarandus caribou), with two objectives: (1) to infer the relative effects of two forms of anthropogenic disturbance (forestry and linear features) on woodland caribou distributions at multiple scales and (2) to estimate scale-integrated resource selection functions (SRSFs) that synthesize results across scales for management-oriented habitat suitability mapping. We found a previously undocumented scale-specific switch in woodland caribou response to two forms of anthropogenic disturbance. Caribou avoided forestry cut-blocks at broad scales according to first- and second-order RSFs and avoided linear features at fine scales according to third-order RSFs, corroborating predictions developed according to predator-mediated effects of each disturbance type. Additionally, a single SRSF validated as well as each of three single-scale RSFs when estimating habitat suitability across three different spatial scales of prediction. We demonstrate that a single SRSF can be applied to predict relative habitat suitability at both local and landscape scales in support of critical habitat identification and species recovery.     

Increase in Quantity and Quality of Suitable Areas for Invasive Species as Climate Changes

As climatically suitable range projections become increasingly used to assess distributions of species, we recommend systematic assessments of the quality of habitat in addition to the classical binary classification of habitat. We devised a method to assess occurrence probability, captured by a climatic suitability index, through which we could determine variations in the quality of potential habitat. This relative risk assessment circumvents the use of an arbitrary suitability threshold. We illustrated our method with 2 case studies on invasive ant species. We estimated invasion potential of the destroyer ant (Monomorium destructor) and the European fire ant (Myrmica rubra) on a global scale currently and by 2080 with climate change. We found that 21.1% of the world's landmass currently has a suitable climate for the destroyer ant and 16% has a suitable climate for European fire ant. Our climatic suitability index showed that both ant species would benefit from climate change, but in different ways. The size of the potential distribution increased by 35.8% for the destroyer ant. Meanwhile, the total area of potential distribution remained the same for the European fire ant (>0.05%), but the level of climatic suitability within this range increased greatly and led to an improvement in habitat quality (i.e., of invasive species’ establishment likelihood). Either through quantity or quality of suitable areas, both invasive ant species are likely to increase the extent of their invasion in the future, following global climate change. Our results show that species may increase their range if either more areas become suitable or if the available areas present improved suitability. Studies in which an arbitrary suitability threshold was used may overlook changes in area quality within climatically suitable areas and as a result reach incorrect predictions. Incremento de la Cantidad y Calidad de Áreas Idóneas para Especies Invasoras a Medida que Cambia el Clima     

Quantifying the impact of vegetation-based metrics on species persistence when choosing offsets for habitat destruction

Developers are often required by law to offset environmental impacts through targeted conservation actions. Most offset policies specify metrics for calculating offset requirements, usually by assessing vegetation condition. Despite widespread use, there is little evidence to support the effectiveness of vegetation-based metrics for ensuring biodiversity persistence. We compared long-term impacts of biodiversity offsetting based on area only; vegetation condition only; area × habitat suitability; and condition × habitat suitability in development and restoration simulations for the Hunter Region of New South Wales, Australia. We simulated development and subsequent offsetting through restoration within a virtual landscape, linking simulations to population viability models for 3 species. Habitat gains did not ensure species persistence. No net loss was achieved when performance of offsetting was assessed in terms of amount of habitat restored, but not when outcomes were assessed in terms of persistence. Maintenance of persistence occurred more often when impacts were avoided, giving further support to better enforce the avoidance stage of the mitigation hierarchy. When development affected areas of high habitat quality for species, persistence could not be guaranteed. Therefore, species must be more explicitly accounted for in offsets, rather than just vegetation or habitat alone. Declines due to a failure to account directly for species population dynamics and connectivity overshadowed the benefits delivered by producing large areas of high-quality habitat. Our modeling framework showed that the benefits delivered by offsets are species specific and that simple vegetation-based metrics can give misguided impressions on how well biodiversity offsets achieve no net loss.     

Ranking individual habitat patches as connectivity providers: Integrating network analysis and patch removal experiments

Here we propose an integrated framework for modeling connectivity that can help ecologists, conservation planners and managers to identify patches that, more than others, contribute to uphold species dispersal and other ecological flows in a landscape context. We elaborate, extend and partly integrate recent network-based approaches for modeling and supporting the management of fragmented landscapes. In doing so, experimental patch removal techniques and network analytical approaches are merged into one integrated modeling framework for assessing the role of individual patches as connectivity providers. In particular, we focus the analyses on the habitat availability metrics PC and IIC and on the network metric Betweenness Centrality. The combination and extension of these metrics jointly assess both the immediate connectivity impacts of the loss of a particular patch and the resulting increased vulnerability of the network to subsequent disruptions. In using the framework to analyze the connectivity of two real landscapes in Madagascar and Catalonia (NE Spain), we suggest a procedure that can be used to rank individual habitat patches and show that the combined metrics reveal relevant and non-redundant information valuable to assert and quantify distinctive connectivity aspects of any given patch in the landscape. Hence, we argue that the proposed framework could facilitate more ecologically informed decision-making in managing fragmented landscapes. Finally, we discuss and highlight some of the advantages, limitations and key differences between the considered metrics.     

Assessment of the aquatic biodiversity of a threatened coastal lagoon at Bimini, Bahamas

Coastal biodiversity is threatened worldwide by both direct and indirect anthropogenic activities. To more effectively manage and protect coastal biodiversity, accurate assessments of genetic, species, and ecosystem level diversity are required. We present the results from an assessment of the aquatic species diversity of a small (3?km²), shallow, mangrove-fringed Bahamian lagoon (the North Sound) subject to ongoing anthropogenic development. The assessment was conducted through a collation of field observations and data in published literature. We found that eight angiosperm species, 30 macroalgal species, and 370 animal species (including 95 fishes, 69 arthropods, 56 birds, and 45 mollusks) were documented within the lagoon. At least 11 of these species are of conservation concern, such as the critically endangered smalltooth sawfish (Pristis pectinata) and hawksbill turtle (Eretmochelys imbricata). Comparisons of community similarity indicated that the North Sound has a relatively distinct fauna and flora, but available data suggest that the species found there are most similar to those found in nearby habitats in Cuba. The lagoon forms a key nursery habitat for many species, including lemon sharks (Negaprion brevirostris), Caribbean spiny lobsters (Panulirus argus), and queen conch (Strombas gigas). Recently, the lagoon was included as part of a new marine protected area (MPA), but much of the habitat has already experienced considerable anthropogenic disturbance and the MPA boundaries have yet to be established. We have therefore analyzed the lagoon biodiversity and expect the data presented here to serve as a baseline for future comparisons.     

Evaluation of a fast response pressure solver for flow around an isolated cube

This work describes and evaluates a pressure solver that has been incorporated into a fast response three-dimensional building-resolving diagnostic wind modeling system. The solver computes the three-dimensional pressure field around buildings and on exterior walls in terms of a coefficient of pressure by solving a simplified pressure Poisson equation (that neglects turbulence stresses in the Navier-Stokes) for incompressible flow. The input to the solver is the three-dimensional mean wind field obtained from a fast response empirical-diagnostic urban wind model. The present study is an evaluation of the pressure solver using wind-tunnel data for flow normal to and at a 45° angle to an isolated cubical building. Results for the normal incident wind angle case indicate that the model satisfactorily reproduces the general spatial patterns and the magnitude of the pressure difference around much of the cube. Details of the flow field that are not satisfactorily predicted include the spatial distribution of pressure on the roof and the lower half of the front side of the building and the magnitude along the sidewalls where pressures are over predicted. The results for the 45° case show reasonable agreement between the model and experiments on the front and the back walls, but over predict pressures on the leading edge of the rooftop. Regions with poor pressure predictions appear to be a result of unsatisfactory mean wind modeling.     

Modeling a spatially restricted distribution in the Neotropics: How the size of calibration area affects the performance of five presence-only methods

We here examine species distribution models for a Neotropical anuran restricted to ombrophilous areas in the Brazilian Atlantic Forest hotspot. We extend the known occurrence for the treefrog Hypsiboas bischoffi (Anura: Hylidae) through GPS field surveys and use five modeling methods (BIOCLIM, DOMAIN, OM-GARP, SVM, and MAXENT) and selected bioclimatic and topographic variables to model the species distribution. Models were first trained using two calibration areas: the Brazilian Atlantic Forest (BAF) and the whole of South America (SA). All modeling methods showed good levels of predictive power and accuracy with mean AUC ranging from 0.77 (BIOCLIM/BAF) to 0.99 (MAXENT/SA). MAXENT and SVM were the most accurate presence-only methods among those tested here. All but the SVM models calibrated with SA predicted larger distribution areas when compared to models calibrated in BAF. OM-GARP dramatically overpredicted the species distribution for the model calibrated in SA, with a predicted area around 10⁶ km² larger than predicted by other SDMs. With increased calibration area (and environmental space), OM-GARP predictions followed changes in the environmental space associated with the increased calibration area, while MAXENT models were more consistent across calibration areas. MAXENT was the only method that retrieved consistent predictions across calibration areas, while allowing for some overprediction, a result that may be relevant for modeling the distribution of other spatially restricted organisms.     

Adaptive droving behavior observed in the fiddler crab Uca vocans vocans

Droving is conspicuous in Uca vocans vocans in summer. The crabs burrowing on the upper habitat have a higher tendency to wander compared to the ones burrowing on the lower habitat. Most of the wandering crabs captured on the low tide levels are relatively large and male. Larger crabs and males prefer to burrow on the upper zones of U. vocans habitat, but the smaller ones and females prefer the lower habitat. The upper level of the U. vocans habitat has relatively low N-content compared to the water's edge. Therefore droving is advantageous in crabs that have burrows on the upper level. Female and smaller resident crabs have faster feeding motions than male and larger ones, and can satisfy their feeding demands more rapidly. Therefore, for males and larger crabs it is advantageous to move away from the burrow area and forage in areas of higher food content near the low tide level where the number of feeding motions increases.     

A method to extract morphological traits of plant organs from 3D point clouds as a database for an architectural plant model

Combining process-based and three-dimensional (3D) structural models for specific crops to functional–structural plant models (FSPMs) enable ecophysiologists to investigate the interaction of single plants or plant stands with their biotic and abiotic environment in a unique way. The present study was part of a collaborative research program on the development of a FSPM for the sample plant (Hordeum vulgare L.). The emphasis of this paper is put on two main aspects. First, improved generic and flexible functions are formulated for modeling the shape of leaves and stems of graminaceous plants as organ-related triangulated surfaces, where the parameters may be directly interpreted in terms of morphological traits. The proposed functions constitute the structural model, which is amplified by topological information to a so-called architectural model. Second, we suggest a new approach to parameterize these functions based on 3D point cloud data obtained by digitization of entire plants. Since no automated technique is available to process 3D point clouds in a way appropriate for parameterization of the architectural model, the required algorithms are developed and implemented in Matlab^®. Our approach comprises the following steps. First, the measured set of points is partitioned into subsets representing each organ. Each subset is then divided further to represent organ segments. Next, the centroid of each partial point cloud representing an organ segment is computed. The sequence of these centroid points describes the organ axis. By means of the architectural model for leaves and stems, triangulated surfaces are assembled from the computed organ axis points and from user-defined initial values for the various parameters in the model (e.g. maximum leaf width). Finally, the parameters in the functions describing leaf and stem surfaces are estimated by fitting computed triangulated surfaces into the related point cloud using least squares optimization. Hence, the proposed method allows the use of 3D point clouds obtained with modern 3D digitizing techniques for the parameterization of an organ-based architectural model.     

A dispersal-constrained habitat suitability model for predicting invasion of alpine vegetation

Developing tools to predict the location of new biological invasions is essential if exotic species are to be controlled before they become widespread. Currently, alpine areas in Australia are largely free of exotic plant species but face increasing pressure from invasive species due to global warming and intensified human use. To predict the potential spread of highly invasive orange hawkweed (Hieracium aurantiacum) from existing founder populations on the Bogong High Plains in southern Australia, we developed an expert-based, spatially explicit, dispersal-constrained, habitat suitability model. The model combines a habitat suitability index, developed from disturbance, site wetness, and vegetation community parameters, with a phenomenological dispersal kernel that uses wind direction and observed dispersal distances. After generating risk maps that defined the relative suitability of H. aurantiacum establishment across the study area, we intensively searched several locations to evaluate the model. The highest relative suitability for H. aurantiacum establishment was southeast from the initial infestations. Native tussock grasslands and disturbed areas had high suitability for H. aurantiacum establishment. Extensive field searches failed to detect new populations. Time-step evaluation using the location of populations known in 1998-2000, accurately assigned high relative suitability for locations where H. aurantiacum had established post-2003 (AUC [area under curve] = 0.855 +/- 0.035). This suggests our model has good predictive power and will improve the ability to detect populations and prioritize areas for ongoing monitoring.     

Schnecken unter Stress

Background In ecophysiology and ecotoxicology, gastropods are important both as target organisms for molluscicides and non-target organisms for environmental pollutants or other environmental stressors. With respect to both aspects, biomarkers are investigated at different levels of biological organization in order to understand mechanisms which enable gastropods to cope with or even to benefit from unfavourable environmental conditions. Main topics The paper focuses on the ecotoxicological and ecophysiological work of the author on gastropods which will be reviewed in the context of the state of knowledge in this field of research. In addition to cellular aspects in biomarker research, also biochemical responses of snails to environmental stress (stress proteins, metallothioneins, and metabolic enzymes) will be addressed. Conclusions The paper highlights the suitability of terrestrial and aquatic gastropods as sensitive indicators of environmental stress induced by chemicals or other non-chemical factors. Biomarker studies have been shown not only to be applicable in environmental risk assessment but also to provide fundamental and background knowledge necessary to understand correlations of responses at different levels of biological organization. Recommendations and perspectives A standardized toxicity test with the grapevine snail (ISO 15952) has been established for toxicity assessment in terrestrial habitats. However, freshwater gastropods display a high sensitivity as well, e.?g. to endocrine disrupters, and should be incorporated into future standardized assays for aquatic toxicity testing on the basis of existing knowledge.     

Nest selection by snow petrels Pagodroma nivea in East Antarctica: Validating predictive habitat selection models at the continental scale

Little is known on the factors controlling distribution and abundance of snow petrels in Antarctica. Studying habitat selection through modeling may provide useful information on the relationships between this species and its environment, especially relevant in a climate change context, where habitat availability may change. Validating the predictive capability of habitat selection models with independent data is a vital step in assessing the performance of such models and their potential for predicting species’ distribution in poorly documented areas.From the results of ground surveys conducted in the Casey region (2002–2003, Wilkes Land, East Antarctica), habitat selection models based on a dataset of 4000 nests were created to predict the nesting distribution of snow petrels as a function of topography and substrate. In this study, the Casey models were tested at Mawson, 3800 km away from Casey. The location and characteristics of approximately 7700 snow petrel nests were collected during ground surveys (Summer 2004–2005). Using GIS, predictive maps of nest distribution were produced for the Mawson region with the models derived from the Casey datasets and predictions were compared to the observed data. Models performance was assessed using classification matrixes and Receiver operating characteristic (ROC) curves. Overall correct classification rates for the Casey models varied from 57% to 90%. However, two geomorphologically different sub-regions (coastal islands and inland mountains) were clearly distinguished in terms of habitat selection by Casey model predictions but also by the specific variations in coefficients of terms in new models, derived from the Mawson data sets. Observed variations in the snow petrel aggregations were found to be related to local habitat availability.We discuss the applicability of various types of models (GLM, CT) and investigate the effect of scale on the prediction of snow petrel habitats. While the Casey models created with data collected at the nest scale did not perform well at Mawson due to regional variations in nest micro-characteristics, the predictive performance of models created with data compiled at a coarser scale (habitat units) was satisfactory. Substrate type was the most robust predictor of nest presence between Casey and Mawson. This study demonstrate that it is possible to predict at the large scale the presence of snow petrel nests based on simple predictors such as topography and substrate, which can be obtained from aerial photography. Such methodologies have valuable applications in the management and conservation of this top predator and associated resources and may be applied to other Antarctic, Sub-Antarctic and lower latitudes species and in a variety of habitats.     

Spatial selection and inheritance: applying evolutionary concepts to population dynamics in heterogeneous space

Organisms in highly suitable sites generally produce more offspring, and offspring can inherit this suitability by not dispersing far. This combination of spatial selection and spatial inheritance acts to bias the distribution of organisms toward suitable sites and thereby increase mean fitness (i.e., per capita population increase). Thus, population growth rates in heterogeneous space change over time by a process conceptually analogous to evolution by natural selection, opening avenues for theoretical cross-pollination between evolutionary biology and ecology. We operationally define spatial inheritance and spatial selective differential and then combine these two factors in a modification of the breeder's equation, derived from simple models of population growth in heterogeneous space. The modified breeder's equation yields a conservative criterion for persistence in hostile environments estimable from field measurements. We apply this framework for understanding gypsy moth population persistence amidst abundant predators and find that the predictions of the modified breeder's equation match initial changes in population growth rate in independent simulation output. The analogy between spatial dynamics and natural selection conceptually links ecology and evolution, provides a spatially implicit framework for modeling spatial population dynamics, and represents an important null model for studying habitat selection.