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1.
Aim Climate limits the ranges of many animals, but the mechanism whereby it does so remains poorly understood. One explanation is that climate (e.g. temperature or rainfall) affects energy expenditure, eventually limiting where a species can occur. We propose that climate can also affect energy uptake through its effect on foraging efficiency. We examined this idea for the case of the hadeda ibis (Bostrychia hagedash) which has considerably expanded its range in southern Africa over the past 80 years. Hadedas forage mainly by extracting earthworms and other invertebrates from soft soil. Soil moisture, in the absence of irrigation largely determined by climate and soil composition, may therefore be a factor limiting feeding efficiency in hadedas. Location We tested this hypothesis by observing foraging hadedas in Cape Town, South Africa. Results We found that soil moisture limited the rate at which hadedas caught prey items, with an optimum on relatively moist ground. We further measured the energy content of the hadedas’ main prey, earthworms. Using published physiological relationships, we estimated that hadedas need to forage for about 6.3 h to meet their daily energy requirements under optimal soil moisture conditions. On dry soils, they need to forage for >12 h, thus showing that soil moisture has the potential to limit the range of this species. Main conclusions Hadedas originally only occurred in the wettest parts of South Africa, but gradually colonized drier areas, and are now absent only from the driest parts of the country. Our results support the view that climate (determining soil moisture) originally limited the hadedas range and that irrigation has been an important factor facilitating their range expansion. The hadeda is an example for a species whose range expansion is driven by interactions between climate and land‐use change. 相似文献
2.
Chrystal S. Mantyka‐pringle Tara G. Martin Jonathan R. Rhodes 《Global Change Biology》2012,18(4):1239-1252
Climate change and habitat loss are both key threatening processes driving the global loss in biodiversity. Yet little is known about their synergistic effects on biological populations due to the complexity underlying both processes. If the combined effects of habitat loss and climate change are greater than the effects of each threat individually, current conservation management strategies may be inefficient and at worst ineffective. Therefore, there is a pressing need to identify whether interacting effects between climate change and habitat loss exist and, if so, quantify the magnitude of their impact. In this article, we present a meta‐analysis of studies that quantify the effect of habitat loss on biological populations and examine whether the magnitude of these effects depends on current climatic conditions and historical rates of climate change. We examined 1319 papers on habitat loss and fragmentation, identified from the past 20 years, representing a range of taxa, landscapes, land‐uses, geographic locations and climatic conditions. We find that current climate and climate change are important factors determining the negative effects of habitat loss on species density and/or diversity. The most important determinant of habitat loss and fragmentation effects, averaged across species and geographic regions, was current maximum temperature, with mean precipitation change over the last 100 years of secondary importance. Habitat loss and fragmentation effects were greatest in areas with high maximum temperatures. Conversely, they were lowest in areas where average rainfall has increased over time. To our knowledge, this is the first study to conduct a global terrestrial analysis of existing data to quantify and test for interacting effects between current climate, climatic change and habitat loss on biological populations. Understanding the synergistic effects between climate change and other threatening processes has critical implications for our ability to support and incorporate climate change adaptation measures into policy development and management response. 相似文献
3.
Aim To highlight the benefit of using habitat use to improve the accuracy of predictive road fatality models.
Location The Snowy Mountains Highway in southern New South Wales, Australia.
Methods A binary logistic regression model was constructed using wombat fatality presences and randomly generated absences. Species-specific habitat variables were included as predictors in the model selection process as well as two spatially explicit measures of wombat habitat use. Generalized additive models (GAMs) were constructed for each possible combination of predictors in R. The final model was selected by comparing all models subsets for the eight predictors and employing the one standard error rule to select the best model set.
Results The final predictive model had high discriminatory power and incorporated both measures of species habitat use, greatly exceeding the variation explained by a previously published model for the same species and road.
Main Conclusions Our findings highlight the importance of incorporating variables that describe habitat use by fauna for predictive modelling of animal-vehicle crashes. Reliance upon models that ignore landscape patterns are limited in their capacity to identify hotspots and inform managers of locations to engage in mitigation. 相似文献
Location The Snowy Mountains Highway in southern New South Wales, Australia.
Methods A binary logistic regression model was constructed using wombat fatality presences and randomly generated absences. Species-specific habitat variables were included as predictors in the model selection process as well as two spatially explicit measures of wombat habitat use. Generalized additive models (GAMs) were constructed for each possible combination of predictors in R. The final model was selected by comparing all models subsets for the eight predictors and employing the one standard error rule to select the best model set.
Results The final predictive model had high discriminatory power and incorporated both measures of species habitat use, greatly exceeding the variation explained by a previously published model for the same species and road.
Main Conclusions Our findings highlight the importance of incorporating variables that describe habitat use by fauna for predictive modelling of animal-vehicle crashes. Reliance upon models that ignore landscape patterns are limited in their capacity to identify hotspots and inform managers of locations to engage in mitigation. 相似文献
4.
Werner G. Dörgeloh 《African Journal of Ecology》2006,44(3):329-336
The distribution and numbers of tsessebe (Damaliscus lunatus lunatus) have declined considerably in South Africa, partly due to deteriorating habitat conditions. Identifying important habitat variables will assist in managing the species. The objective of this study was to identify habitat variables important for tsessebe and to develop a predictive model of habitat selection for this species in a savanna biome. The study was conducted in the Nylsvley Nature Reserve over a 2‐year period. A total of eighteen habitat variables were measured in ten plant communities at 200 sites. Logistic regression analyses were used to identify predictor variables and to construct a habitat model. Tsessebe were found <2 km from the nearest source of water, in flat areas with slopes of <3° and with <10% rockiness. Their distribution was not influenced by the woody component. Sites where tsessebe were present had significantly lower grass heights and tuft heights, with a higher grass density compared with areas not utilized by tsessebe. Nitrogen and sodium levels were also higher at present sites. Habitat type and grass height were the most significant predictors of tsessebe presence. The selected model had an overall percentage prediction of 85.0%. The model was subdivided into five vegetation‐specific models and each model was tested with independent data. 相似文献
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- 1 Environmental heterogeneity is important in determining the distribution and abundance of organisms at various spatial scales. The ability to understand and predict distribution patterns is important for solving many management problems in conservation biology and wildlife epidemiology.
- 2 The badger Meles meles is a highly adaptable, medium‐sized carnivore, distributed throughout temperate Eurasia, which shows a wide diversity of social and spatial organization. Within Britain, badgers are not only legally protected, but they also serve as a wildlife host for bovine tuberculosis Mycobacterium bovis. An evaluation of the role of badgers in the dynamics of this infection depends on understanding the responses of badgers to the environment at different spatial scales.
- 3 The use of digital data to provide information on habitats for distribution models is becoming common. Digital data are increasingly accessible and are generally cheaper than field surveys. There has been little research, however, to compare the accuracy of models based on field‐derived and remotely derived data.
- 4 In this paper, we make quantified comparisons between large‐scale presence/absence models for badgers in Britain, based on field‐surveyed habitat data and remotely derived digital data, comprising elevation, geology and soil.
- 5 We developed four models: 1980s badger survey data using field‐based and digital data, and 1990s badger survey data using field‐based and digital data. We divided each of the four datasets into two subsets and used one subset for training (developing) the model and the other for testing it.
- 6 All four training models had classification accuracies in excess of 69%. The models generated from digital data were slightly more accurate than those generated from field‐derived habitat data.
- 7 The high classificatory ability of the digital‐based models suggests that the use of digital data may overcome many of the problems associated with field data in wildlife‐habitat modelling, such as cost and restricted geographical coverage, without any significant impact on model performance for some species. The more widespread use of digital data in wildlife‐habitat models should enhance their accuracy, repeatability and applicability and make them better‐suited as tools to aid policy‐ and decision‐making processes.
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Quantifying the relative influence of multiple mechanisms driving recent range expansion of non‐native species is essential for predicting future changes and for informing adaptation and management plans to protect native species. White‐tailed deer (Odocoileus virginianus) have been expanding their range into the North American boreal forest over the last half of the 20th century. This has already altered predator–prey dynamics in Alberta, Canada, where the distribution likely reaches the northern extent of its continuous range. Although current white‐tailed deer distribution is explained by both climate and human land use, the influence each factor had on the observed range expansion would depend on the spatial and temporal pattern of these changes. Our objective was to quantify the relative importance of land use and climate change as drivers of white‐tailed deer range expansion and to predict decadal changes in white‐tailed deer distribution in northern Alberta for the first half of the 21st century. An existing species distribution model was used to predict past decadal distributions of white‐tailed deer which were validated using independent data. The effects of climate and land use change were isolated by comparing predictions under theoretical “no‐change between decades” scenarios, for each factor, to predictions under observed climate and land use change. Climate changes led to more than 88%, by area, of the increases in probability of white‐tailed deer presence across all decades. The distribution is predicted to extend 100 km further north across the northeastern Alberta boreal forest as climate continues to change over the first half of the 21st century. 相似文献
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Geographic information system (GIS) and landscape-level data offer a new opportunity for modeling and evaluating the quality of wildlife habitats. Models of habitat quality have not been developed for some species, and existing models could be improved by incorporating updated information on wildlife–habitat relationships and habitat variables. We developed a GIS-based habitat suitability index (HSI) model for the Korean water deer (Hydropotes inermis argyropus), which often causes human–wildlife conflicts in the Chungnam Province of Korea because of industrialization and urbanization. The model is based on logistic regression analysis, which addresses the impact of multiple habitat variables, such as habitat components, topographic characteristics, and human disturbances. The model yielded a p-value of .289 (χ2?=?9.672) and 65.4% correct prediction level with the overall observation–prediction comparison data. The model demonstrated that a large portion of the province (61.6%) could be regarded as a poor habitat (mean HSI value of the province?=?0.22), while the current habitats of the province could be considered of moderate quality (mean HSI value?=?0.31). In addition, the chance of observation of the deer increases as the HSI level increases, which means that the model yields a good predictive power. Lastly, we used the model to produce a habitat suitability map. Our HSI model enabled us to quantify habitat preferences, which could be the basis for decision-making on habitat protection, mitigation, and enhancement of the Korean water deer. The proposed model is also applicable for improving and enhancing the existing management practices, as well as for establishing an effective wildlife protection policy. 相似文献
8.
Atte Moilanen 《The Journal of animal ecology》2000,69(1):143-153
1. The construction of a predictive metapopulation model includes three steps: the choice of factors affecting metapopulation dynamics, the choice of model structure, and finally parameter estimation and model testing.
2. Unless the assumption is made that the metapopulation is at stochastic quasi-equilibrium and unless the method of parameter estimation of model parameters uses that assumption, estimates from a limited amount of data will usually predict a trend in metapopulation size.
3. This implicit estimation of a trend occurs because extinction-colonization stochasticity, possibly amplified by regional stochasticity, leads to unequal numbers of observed extinction and colonization events during a short study period.
4. Metapopulation models, such as those based on the logistic regression model, that rely on observed population turnover events in parameter estimation are sensitive to the implicit estimation of a trend.
5. A new parameter estimation method, based on Monte Carlo inference for statistically implicit models, allows an explicit decision about whether metapopulation quasi-stability is assumed or not.
6. Our confidence in metapopulation model parameter estimates that have been produced from only a few years of data is decreased by the need to know before parameter estimation whether the metapopulation is in quasi-stable state or not.
7. The choice of whether metapopulation stability is assumed or not in parameter estimation should be done consciously. Typical data sets cover only a few years and rarely allow a statistical test of a possible trend. While making the decision about stability one should consider any information about the landscape history and species and metapopulation characteristics. 相似文献
2. Unless the assumption is made that the metapopulation is at stochastic quasi-equilibrium and unless the method of parameter estimation of model parameters uses that assumption, estimates from a limited amount of data will usually predict a trend in metapopulation size.
3. This implicit estimation of a trend occurs because extinction-colonization stochasticity, possibly amplified by regional stochasticity, leads to unequal numbers of observed extinction and colonization events during a short study period.
4. Metapopulation models, such as those based on the logistic regression model, that rely on observed population turnover events in parameter estimation are sensitive to the implicit estimation of a trend.
5. A new parameter estimation method, based on Monte Carlo inference for statistically implicit models, allows an explicit decision about whether metapopulation quasi-stability is assumed or not.
6. Our confidence in metapopulation model parameter estimates that have been produced from only a few years of data is decreased by the need to know before parameter estimation whether the metapopulation is in quasi-stable state or not.
7. The choice of whether metapopulation stability is assumed or not in parameter estimation should be done consciously. Typical data sets cover only a few years and rarely allow a statistical test of a possible trend. While making the decision about stability one should consider any information about the landscape history and species and metapopulation characteristics. 相似文献
9.
X. Zhang J. Halder R.P. White D.J. Hughes Z. Ye C. Wang R. Xu B. Gan B.D.L. Fitt 《The Annals of applied biology》2014,164(3):384-395
To estimate potential impact of climate change on wheat fusarium ear blight (FEB), simulated weather for the A1B climate change scenario was input into a model for estimating FEB in central China. In this article, a logistic weather‐based regression model for estimating incidence of wheat FEB in central China was developed, using up to 10 years (2001–2010) of disease, anthesis date and weather data available for 10 locations in Anhui and Hubei provinces. In the model, the weather variables were defined with respect to the anthesis date for each location in each year. The model suggested that incidence of FEB is related to number of days of rainfall in a 30‐day period after anthesis and that high temperatures before anthesis increase the incidence of disease. Validation was done to test whether this relationship was satisfied for another five locations in Anhui province with FEB data for 4–5 years but no nearby weather data, using simulated weather data obtained employing the regional climate modelling system PRECIS. How climate change may affect wheat anthesis date and FEB in central China was investigated for period 2020–2050 using wheat growth model Sirius and climate data simulated using PRECIS. The projection suggested that wheat anthesis dates will generally be earlier and FEB incidence will increase substantially for most locations. 相似文献
10.
Climate change is expected to drive species ranges towards the poles and to have a strong influence on species distributions. In this study, we focused on diadromous species that are of economical and ecological importance in the whole of Europe. We investigated the potential distribution of all diadromous fish regularly encountered in Europe, North Africa and the Middle East (28 species) under conditions predicted for twenty‐first century climate change. To do so, we investigated the 1900 distribution of each species in 196 basins spread across all of Europe, North Africa and the Middle East. Four levels were used to semiquantitatively describe the abundance of species, that is missing, rare, common and abundant. We then selected five variables describing the prevailing climate in the basins, the physical nature of the basins and reflecting historical events known to have affected freshwater fish distribution. Logistic regressions with a four‐level ordinal response variable were used to develop species‐specific models. These predictive models related the observed distribution of these species in 1900 to the most explanatory combination of variables. Finally, we selected the A2 SRES scenario and the HadCM3 (Hadley Centre Coupled Model version 3) global climate model (GCM) to obtain climate variables (temperature and precipitation) at the end of this century. We used these 2100 variables in our models and obtained maps of climatically suitable and unsuitable basins, percentages of contraction or expansion for each species. Twenty‐two models were successfully built, that is there were five species for which no model could be established because their distribution range was too narrow and the Acipenser sturio model failed during calibration. All the models selected temperature or/and precipitation as explanatory variables. Responses to climate change were species‐specific but could be classified into three categories: little or no change in the distribution (five species), expansion of the distribution range (three species gaining suitable basins mainly northward) and contraction of the distribution (14 species losing suitable basins). Shifting ranges were in accordance with those found in other studies and underlined the high sensitivity of diadromous fish to modifications in their environment. 相似文献
11.
ABSTRACT Ecologists often develop complex regression models that include multiple categorical and continuous variables, interactions among predictors, and nonlinear relationships between the response and predictor variables. Nomograms, which are graphical devices for presenting mathematical functions and calculating output values, can aid biologists in interpreting and presenting these complex models. To illustrate benefits of nomograms, we developed a logistic regression model of elk (Cervus elaphus) resource selection. With this model, we demonstrated how a nomogram helps scientists and managers interpret interactions among variables, compare the relative biological importance of variables, and examine predicted shapes of relationships (e.g., linear vs. nonlinear) between response and predictor variables. Although our example focused on logistic regression, nomograms are equally useful for other linear and nonlinear models. Regardless of the approach used for model development, nomograms and other graphical summaries can help scientists and managers develop, interpret, and apply statistical models. 相似文献
12.
Phenotypic differentiation is often interpreted as a result of local adaptation of individuals to their environment. Here, we investigated the skull morphological differentiation in 11 populations of the white‐footed mouse (Peromyscus leucopus). These populations were sampled in an agricultural landscape in the Montérégie region (Québec, Canada), at the northern edge of the distribution of the white‐footed mouse. We found a strong pattern of phenotypic differentiation matching the genetic structure across these populations. Landscape fragmentation and the presence of geographic barriers, in particular north–south oriented rivers, contribute to this differentiation and modulate the pattern of rapid ongoing northward range expansion of the white‐footed mouse in response to climate warming. We conclude that while large rivers and postglacial recolonization routes have shaped the current pattern of distribution and differentiation of white‐footed mouse populations, further local differentiation is occurring, at the scale of the landscape. We posit that the northern expansion of the white‐footed mouse is achieved through successive independent founder events in a fragmented landscape at the northern range edge of the species. The phenotypic differentiation we observe is thus a result of a number of mechanisms operating at different spatial and temporal scales. 相似文献
13.
近年来湿地生态系统遭到不同程度破坏,湿地水鸟及其生存空间日益受到威胁。以香港米埔-后海湾湿地为例,收集2003年1月份与鹭科水鸟密切相关的15个自变量和鹭科水鸟实测数据作为因变量构建逻辑斯蒂回归模型,通过筛选获取9个变量因子,分别为土地利用,NDVI,坡度,降雨,TM4纹理,TM3纹理,道路密度,道路距离,人居密度。经Nagelkerke R2检验模型精度达到0.743,拟合度较高。利用模型结果快速聚类,对栖息地进行适宜性分级,分级结果与同期鹭科水鸟实测数据做拟合,精度达到77.4%。最后采集2009年1月份各变量因子数据对回归方程进行时间尺度检验,与同期实测鹭科水鸟数据拟合精度同样达到75.8%,模型具有较好的通用性。 相似文献
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Abstract Habitat models are now broadly used in conservation planning on public lands. If implemented correctly, habitat modelling is a transparent and repeatable technique for describing and mapping biodiversity values, and its application in peri‐urban and agricultural landscape planning is likely to expand rapidly. Conservation planning in such landscapes must be robust to the scrutiny that arises when biodiversity constraints are placed on developers and private landholders. A standardized modelling and model evaluation method based on widely accepted techniques will improve the robustness of conservation plans. We review current habitat modelling and model evaluation methods and provide a habitat modelling case study in the New South Wales central coast region that we hope will serve as a methodological template for conservation planners. We make recommendations on modelling methods that are appropriate when presence‐absence and presence‐only survey data are available and provide methodological details and a website with data and training material for modellers. Our aim is to provide practical guidelines that preserve methodological rigour and result in defendable habitat models and maps. The case study was undertaken in a rapidly developing area with substantial biodiversity values under urbanization pressure. Habitat maps for seven priority fauna species were developed using logistic regression models of species‐habitat relationships and a bootstrapping methodology was used to evaluate model predictions. The modelled species were the koala, tiger quoll, squirrel glider, yellow‐bellied glider, masked owl, powerful owl and sooty owl. Models ranked sites adequately in terms of habitat suitability and provided predictions of sufficient reliability for the purpose of identifying preliminary conservation priority areas. However, they are subject to multiple uncertainties and should not be viewed as a completely accurate representation of the distribution of species habitat. We recommend the use of model prediction in an adaptive framework whereby models are iteratively updated and refined as new data become available. 相似文献
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A. Cochrane 《Plant biology (Stuttgart, Germany)》2020,22(Z1):103-112
- The study of climate‐driven effects on seed traits such as germination has gained momentum over the past decade as the impact of global warming becomes more apparent on the health and survival of plant diversity.
- Seed response to warming was evaluated in a suite of short‐range endemic species from the biodiverse Greenstone Belt of southern Western Australia. The temperature dimensions for germination in 20 woody perennials were identified using small unreplicated samples over 6 weeks on a temperature gradient plate (constant and fluctuating temperatures between 5 and 40 °C). These data were subsequently modelled against current and forecast (2070) mean monthly minimum and maximum temperatures to illustrate seasonal changes to germination timing and final percentage germination.
- All but one species attained full germination in at least one cell on the gradient plate. Modelling of the data suggested only minimal changes to percentage germination despite a forecast rise in diurnal temperatures over the next 50 years. Nine species were predicted to experience declines of between <1% and 7%, whilst 11 species were predicted to increase their germination by <1% to 3%. Overall, the speed of germination is predicted to increase but the timing of germination for most species shifts seasonally (both advances and delays) as a result of changing diurnal temperatures.
- The capacity of this suite of species to cope with warmer temperatures during a critical early life stage shows a degree of adaptation to heterogeneous environments. Predicting the effects of global change on terrestrial plant communities is crucial to managing and conserving plant diversity.
19.
CLIVE A. MCALPINE MICHIALA E. BOWEN JOHN G. CALLAGHAN DANIEL LUNNEY JONATHAN R. RHODES DAVID L. MITCHELL DAVID V. PULLAR HUGH P. POSZINGHAM 《Austral ecology》2006,31(4):529-544
Abstract Predicting the various responses of different species to changes in landscape structure is a formidable challenge to landscape ecology. Based on expert knowledge and landscape ecological theory, we develop five competing a priori models for predicting the presence/absence of the Koala (Phascolarctos cinereus) in Noosa Shire, south‐east Queensland (Australia). A priori predictions were nested within three levels of ecological organization: in situ (site level) habitat (<1 ha), patch level (100 ha) and landscape level (100–1000 ha). To test the models, Koala surveys and habitat surveys (n = 245) were conducted across the habitat mosaic. After taking into account tree species preferences, the patch and landscape context, and the neighbourhood effect of adjacent present sites, we applied logistic regression and hierarchical partitioning analyses to rank the alternative models and the explanatory variables. The strongest support was for a multilevel model, with Koala presence best predicted by the proportion of the landscape occupied by high quality habitat, the neighbourhood effect, the mean nearest neighbour distance between forest patches, the density of forest patches and the density of sealed roads. When tested against independent data (n = 105) using a receiver operator characteristic curve, the multilevel model performed moderately well. The study is consistent with recent assertions that habitat loss is the major driver of population decline, however, landscape configuration and roads have an important effect that needs to be incorporated into Koala conservation strategies. 相似文献
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1998～2000年在山西芦芽山自然保护区对褐马鸡的越冬栖息地选择进行了研究。采用4种空间尺度(10m、100m、300m和距离尺度),对影响褐马鸡越冬栖息地选择的主要因子进行了深入分析,并建立了褐马鸡越冬栖息地选择的逻辑斯谛回归模型。在300m尺度上．活动点和非活动点的生境类型有针叶林、针阔混交林、灌木林和草丛等。活动点周围针叶林面积显著高于非活动点(F=-3．116,P=0．002),虽然针阔混交林在两者中的面积比例都较小,但活动点周围针阔混交林的面积明显地低于非活动点(F=-2．255,P=0．024)．在灌木林和草丛的面积上两者无显著差异。这表明褐马鸡在300m尺度上喜欢活动于针叶林较多的地域,由于冬季针阔混交林不如针叶林能提供很好的隐蔽条件,褐马鸡避免选择针阔混交林;在100m尺度上,活动点和非活动点的生境类型有针叶林、针阔混交林和草丛,无灌木林生境,活动点的针叶林面积明显地高于非活动点(F=-2．931,P=0．003)。这表明褐马鸡在100m尺度上虽然倾向于选择针叶林,但对其它类型的生境如针阔混交林和草丛是可以利用的,这可能与其广泛取食活动有关。褐马鸡大尺度上的隐蔽条件满足以后,在小尺度上主要是为了获取更为丰富的食物。在距离尺度上活动点距居民点的距离、距道路的距离显著大于非活动点(F=15．621;6．048,P=0．000;0．018)。通过逐步逻辑斯谛回归分析,发现距灌草丛的距离、距居民点的距离、100m范围内针叶林的面积、树高以及食物的丰盛度是冬季褐马鸡栖息地选择的重要因子。以另外一个研究地收集的数据对所建立的栖息地选择模型的可靠程度进行了检验,结果表明该模型能有效地对褐马鸡的越冬栖息地进行预测。 相似文献