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Increasing evidence shows that anthropogenic climate change is affecting biodiversity. Reducing or stabilizing greenhouse gas emissions may slow global warming, but past emissions will continue to contribute to further unavoidable warming for more than a century. With obvious signs of difficulties in achieving effective mitigation worldwide in the short term at least, sound scientific predictions of future impacts on biodiversity will be required to guide conservation planning and adaptation. This is especially true in Mediterranean type ecosystems that are projected to be among the most significantly affected by anthropogenic climate change, and show the highest levels of confidence in rainfall projections. Multiple methods are available for projecting the consequences of climate change on the main unit of interest - the species - with each method having strengths and weaknesses. Species distribution models (SDMs) are increasingly applied for forecasting climate change impacts on species geographic ranges. Aggregation of models for different species allows inferences of impacts on biodiversity, though excluding the effects of species interactions. The modelling approach is based on several further assumptions and projections and should be treated cautiously. In the absence of comparable approaches that address large numbers of species, SDMs remain valuable in estimating the vulnerability of species. In this review we discuss the application of SDMs in predicting the impacts of climate change on biodiversity with special reference to the species-rich South West Australian Floristic Region and South African Cape Floristic Region. We discuss the advantages and challenges in applying SDMs in biodiverse regions with high levels of endemicity, and how a similar biogeographical history in both regions may assist us in understanding their vulnerability to climate change. We suggest how the process of predicting the impacts of climate change on biodiversity with SDMs can be improved and emphasize the role of field monitoring and experiments in validating the predictions of SDMs.
Interestingly, relationships between demographic parameters and occurrence probability did not vary substantially across degrees of shade tolerance and regions. Although they were influenced by the uncertainty in the estimation of the demographic parameters, we found that r was generally negatively correlated with P-occ, while N, and for most regions K, was generally positively correlated with P-occ. Thus, in temperate forest trees the regions of highest occurrence probability are those with high densities but slow intrinsic population growth rates. The uncertain relationships between demography and occurrence probability suggests caution when linking species distribution and demographic models.
How to understand species' niches and range dynamics: a demographic research agenda for biogeography
(2012)
Range dynamics causes mismatches between a species geographical distribution and the set of suitable environments in which population growth is positive (the Hutchinsonian niche). This is because sourcesink population dynamics cause species to occupy unsuitable environments, and because environmental change creates non-equilibrium situations in which species may be absent from suitable environments (due to migration limitation) or present in unsuitable environments that were previously suitable (due to time-delayed extinction). Because correlative species distribution models do not account for these processes, they are likely to produce biased niche estimates and biased forecasts of future range dynamics. Recently developed dynamic range models (DRMs) overcome this problem: they statistically estimate both range dynamics and the underlying environmental response of demographic rates from species distribution data. This process-based statistical approach qualitatively advances biogeographical analyses. Yet, the application of DRMs to a broad range of species and study systems requires substantial research efforts in statistical modelling, empirical data collection and ecological theory. Here we review current and potential contributions of these fields to a demographic understanding of niches and range dynamics. Our review serves to formulate a demographic research agenda that entails: (1) advances in incorporating process-based models of demographic responses and range dynamics into a statistical framework, (2) systematic collection of data on temporal changes in distribution and abundance and on the response of demographic rates to environmental variation, and (3) improved theoretical understanding of the scaling of demographic rates and the dynamics of spatially coupled populations. This demographic research agenda is challenging but necessary for improved comprehension and quantification of niches and range dynamics. It also forms the basis for understanding how niches and range dynamics are shaped by evolutionary dynamics and biotic interactions. Ultimately, the demographic research agenda should lead to deeper integration of biogeography with empirical and theoretical ecology.
Environmental heterogeneity is a major determinant of plant population dynamics. In semi-arid Kalahari savannas, heterogeneity is created by savanna structure, i.e. by the spatial arrangement and temporal dynamics of woody plant and open grassland microsites. We formulate a conceptual model describing the effects of savanna dynamics on the population dynamics of the animal-dispersed shrub Grewia flava. From empirical results we derive model rules describing effects of savanna structure on several processes in Grewia's life cycle. By formulating the model, we summarise existing information on Grewia demography and identify gaps in this knowledge. Despite a number of such gaps, the model can be used to make certain quantitative predictions. As an example, we apply the model to investigate the role of seed dispersal in Grewia encroachment on rangelands. Model results show that cattle promote encroachment by depositing substantial numbers of seeds in open areas, where Grewia is otherwise dispersal-limited. Finally, we draw some general conclusions about Grewia's life history and population dynamics. Under natural conditions, concentrated seed deposition under woody plants appears to be a key process causing the observed association between Grewia and other woody plants. Furthermore, low rates of recruitment and high adult survival result in slow-motion dynamics of Grewia populations. As a consequence, Grewia populations interact with savanna dynamics on long temporal and short to intermediate spatial scales.
Ecologists increasingly use spatial statistics to study vegetation patterns. Mostly, however, these techniques are applied in a purely descriptive fashion without a priori statements on the pattern characteristics expected. We formulated such a priori predictions in a study of spatial pattern in a semi-arid Karoo shrubland, South Africa. Both seed dispersal and root competition have been discussed as processes shaping the spatial structure of this community. If either of the two processes dominates pattern formation, patterns within and between shrub functional groups are expected to show distinct deviations from null models. We predicted the type and scale of these deviations and compared predicted to observed pattern characteristics. As predicted by the seed dispersal hypothesis, small-scale co-occurrence within and between groups of colonisers and successors was increased as compared to complete spatially random arrangement of shrubs. The root competition predictions, however, were not met as shrubs of similar rooting depth co- occurred more frequently than expected under random shrub arrangement. Since the distribution of rooting groups to the given shrub locations also failed to match the root competition predictions, there was little evidence for dominance of root competition in pattern formation. Although other processes may contribute to small-scale plant co-occurrence, the sufficient and most parsimonious explanation for the observed pattern is that its formation was dominated by seed dispersal. To characterise point patterns we applied both cumulative (uni- and bivariate K-function) and local (pair- and mark-correlation function) techniques. Based on our results we recommend that future studies of vegetation patterns include local characteristics as they independently describe a pattern at different scales and can be easily related to processes changing with interplant distance in a predictable fashion.
1 Secondary seed dispersal by wind, the wind-driven movement of seeds along the ground surface, is an important dispersal mechanism for plant species in a range of environments. 2 We formulate a mechanistic model that describes how secondary dispersal by wind is affected by seed traits, wind conditions and obstacles to seed movement. The model simulates the movement paths of individual seeds and can be fully specified using independently measured parameters. 3 We develop an explicit version of the model that uses a spatially explicit representation of obstacle patterns, and also an aggregated version that uses probability distributions to model seed retention at obstacles and seed movement between obstacles. The aggregated version is computationally efficient and therefore suited to large-scale simulations. It provides a very good approximation of the explicit version (R-2 > 0.99) if initial seed positions vary randomly relative to the obstacle pattern. 4 To validate the model, we conducted a field experiment in which we released seeds of seven South African Proteaceae species that differ in seed size and morphology into an arena in which we systematically varied obstacle patterns. When parameterized with maximum likelihood estimates obtained from independent measurements, the explicit model version explained 70-77% of the observed variation in the proportion of seeds dispersed over 25 m and 67- 69% of the observed variation in the direction of seed dispersal. 5 The model tended to underestimate dispersal rates, possibly due to the omission of turbulence from the model, although this could also be explained by imprecise estimation of one model parameter (the aerodynamic roughness length). 6 Our analysis of the aggregated model predicts a unimodal relationship between the distance of secondary dispersal by wind and seed size. The model can also be used to identify species with the potential for long-distance seed transport by secondary wind dispersal. 7 The validated model expands the domain of mechanistic dispersal models, contributes to a functional understanding of seed dispersal, and provides a tool for predicting the distances that seeds move
The hypothesis that females of socially monogamous species obtain indirect benefits (good or compatible genes) from extra-pair mating behaviour has received enormous attention but much less generally accepted support. Here we ask whether selection for adult survival and fecundity or sexual selection contribute to indirect selection of the extra- pair mating behaviour in socially monogamous coal tits (Periparus ater). We tracked locally recruited individuals with known paternity status through their lives predicting that the extra-pair offspring (EPO) would outperform the within- pair offspring (WPO). No differences between the WPO and EPO recruits were detected in lifespan or age of first reproduction. However, the male WPO had a higher lifetime number of broods and higher lifetime number of social offspring compared with male EPO recruits, while no such differences were evident for female recruits. Male EPO recruits did not compensate for their lower social reproductive success by higher fertilization success within their social pair bonds. Thus, our results do not support the idea that enhanced adult survival, fecundity or within-pair fertilization success are manifestations of the genetic benefits of extra-pair matings. But we emphasize that a crucial fitness component, the extra-pair fertilization success of male recruits, has yet to be taken into account to fully appreciate the fitness consequences of extra-pair matings.
Female extra-pair mating, fitness and genetic diversity: Expression in socially monogamous Coal Tits
(2006)
Avian extrapair mating systems provide an interesting model to assess the role of genetic benefits in the evolution of female multiple mating behavior, as potentially confounding nongenetic benefits of extrapair mate choice are seen to be of minor importance. Genetic benefit models of extrapair mating behavior predict that females engage in extrapair copulations with males of higher genetic quality compared to their social mates, thereby improving offspring reproductive value. The most straightforward test of such good genes models of extrapair mating implies pail-wise comparisons of maternal half-siblings raised in the same environment, which permits direct assessment of Paternal genetic effects oil offspring traits. But genetic benefits of mate choice may be difficult to detect. Furthermore, the extent of genetic benefits (in terms of increased offspring viability or fecundity) may depend oil the environmental context Such that the proposed differences between extrapair offspring (EPO) and within-pair offspring (WPO) only appear under comparatively poor environmental conditions. We tested the hypothesis that genetic benefits of female extrapair mate choice are context dependent by analyzing offspring fitness-related traits in the coal tit (Parus ater) in relation to seasonal variation in environmental conditions. Paternal genetic effects on offspring fitness were context dependent. as shown by a significant interaction effect of differential paternal genetic contribution and offspring hatching date. EPO showed a higher local recruitment probability than their maternal half-siblings if born comparatively late in the season (i.e.. when overall performance had significantly declined), while WPO performed better early in the season. The same general pattern of context dependence was evident when using the number of grandchildren born to a cuckolding female via her female WPO or EPO progeny as the respective fitness measure. However, we were unable to demonstrate that cuckolding females obtained a general genetic fitness benefit from extrapair fertilizations in terms of offspring viability or fecundity. Thus, another type of benefit Could be responsible for maintaining female extrapair mating preferences in the study population. Our results suggest that more than a single selective pressure may have shaped the evolution of female extrapair mating behavior in socially monogamous passerines.
Question: How can we disentangle facilitation and seed dispersal from environmental heterogeneity as mechanisms causing spatial associations of plant species?
Location: Semi-arid savanna in the Kimberley Thorn Bushveld, South Africa.
Methods: We developed a two-step protocol for the statistical differentiation of association-promoting mechanisms in plants based on the Acacia erioloba-Grewia flava association. Individuals of the savanna shrub G. flava and the tree A. erioloba were mapped on four study plots. Disentangling the mechanism causing the association of G. flava and A. erioloba involved tests of three spatial and one non-spatial null model. The spatial null models include homogeneous and heterogeneous Poisson processes for spatial randomness based on the bivariate spatial point patterns of the four plots. With the non-spatial analysis, we determined the relationship between the canopy diameter of A. erioloba trees and presence or absence of G. flava shrubs in the tree understorey to find whether shrub presence requires a minimum tree canopy diameter.
Results: We first showed a significant positive spatial association of the two species. Thereafter, the non-spatial analysis supported an exclusion of environmental heterogeneity as the sole cause of this positive association. We found a minimum tree size under which no G. flava shrubs occurred.
Conclusions: Our two-step analysis showed that it is unlikely that heterogeneous environmental conditions caused the spatial association of A. erioloba and G. flava. Instead, this association may have been caused by seed dispersal and/or facilitation (e.g. caused by hydraulic lift and/or nitrogen fixation by the host tree).
Aim To assess how habitat loss and climate change interact in affecting the range dynamics of species and to quantify how predicted range dynamics depend on demographic properties of species and the severity of environmental change. Location South African Cape Floristic Region. Methods We use data-driven demographic models to assess the impacts of past habitat loss and future climate change on range size, range filing and abundances of eight species of woody plants (Proteaceae). The species-specific models employ a hybrid approach that simulates population dynamics and long-distance dispersal on top of expected spatio-temporal dynamics of suitable habitat. Results Climate change was mainly predicted to reduce range size and range filling (because of a combination of strong habitat shifts with low migration ability). In contrast, habitat loss mostly decreased mean local abundance. For most species and response measures, the combination of habitat loss and climate change had the most severe effect. Yet, this combined effect was mostly smaller than expected from adding or multiplying effects of the individual environmental drivers. This seems to be because climate change shifts suitable habitats to regions less affected by habitat loss. Interspecific variation in range size responses depended mostly on the severity of environmental change, whereas responses in range filling and local abundance depended mostly on demographic properties of species. While most surviving populations concentrated in areas that remain climatically suitable, refugia for multiple species were overestimated by simply overlying habitat models and ignoring demography. Main conclusions Demographic models of range dynamics can simultaneously predict the response of range size, abundance and range filling to multiple drivers of environmental change. Demographic knowledge is particularly needed to predict abundance responses and to identify areas that can serve as biodiversity refugia under climate change. These findings highlight the need for data-driven, demographic assessments in conservation biogeography.
Wildflower harvesting is an economically important activity of which the ecological effects are poorly understood. We assessed how harvesting of flowers affects shrub persistence and abundance at multiple spatial extents. To this end, we built a process-based model to examine the mean persistence and abundance of wild shrubs whose flowers are subject to harvest (serotinous Proteaceae in the South African Cape Floristic Region). First, we conducted a general sensitivity analysis of how harvesting affects persistence and abundance at nested spatial extents. For most spatial extents and combinations of demographic parameters, persistence and abundance of flowering shrubs decreased abruptly once harvesting rate exceeded a certain threshold. At larger extents, metapopulations supported higher harvesting rates before their persistence and abundance decreased, but persistence and abundance also decreased more abruptly due to harvesting than at smaller extents. This threshold rate of harvest varied with species' dispersal ability, maximum reproductive rate, adult mortality, probability of extirpation or local extinction, strength of Allee effects, and carrying capacity. Moreover, spatial extent interacted with Allee effects and probability of extirpation because both these demographic properties affected the response of local populations to harvesting more strongly than they affected the response of metapopulations. Subsequently, we simulated the effects of harvesting on three Cape Floristic Region Proteaceae species and found that these species reacted differently to harvesting, but their persistence and abundance decreased at low rates of harvest. Our estimates of harvesting rates at maximum sustainable yield differed from those of previous investigations, perhaps because researchers used different estimates of demographic parameters, models of population dynamics, and spatial extent than we did. Good demographic knowledge and careful identification of the spatial extent of interest increases confidence in assessments and monitoring of the effects of harvesting. Our general sensitivity analysis improved understanding of harvesting effects on metapopulation dynamics and allowed qualitative assessment of the probability of extirpation of poorly studied species.
Aim The study and prediction of speciesenvironment relationships is currently mainly based on species distribution models. These purely correlative models neglect spatial population dynamics and assume that species distributions are in equilibrium with their environment. This causes biased estimates of species niches and handicaps forecasts of range dynamics under environmental change. Here we aim to develop an approach that statistically estimates process-based models of range dynamics from data on species distributions and permits a more comprehensive quantification of forecast uncertainties.
Innovation We present an approach for the statistical estimation of process-based dynamic range models (DRMs) that integrate Hutchinson's niche concept with spatial population dynamics. In a hierarchical Bayesian framework the environmental response of demographic rates, local population dynamics and dispersal are estimated conditional upon each other while accounting for various sources of uncertainty. The method thus: (1) jointly infers species niches and spatiotemporal population dynamics from occurrence and abundance data, and (2) provides fully probabilistic forecasts of future range dynamics under environmental change. In a simulation study, we investigate the performance of DRMs for a variety of scenarios that differ in both ecological dynamics and the data used for model estimation.
Main conclusions Our results demonstrate the importance of considering dynamic aspects in the collection and analysis of biodiversity data. In combination with informative data, the presented framework has the potential to markedly improve the quantification of ecological niches, the process-based understanding of range dynamics and the forecasting of species responses to environmental change. It thereby strengthens links between biogeography, population biology and theoretical and applied ecology.
2. We present a hierarchical model that integrates observations from multiple sources to estimate spatio-temporal abundance trends. The model links annual population densities on a spatial grid to both long-term count data and to opportunistic occurrence records from a citizen science programme. Specific observation models for both data types explicitly account for differences in data structure and quality.
3. We test this novel method in a virtual study with simulated data and apply it to the estimation of abundance dynamics across the range of a butterfly species (Pyronia tithonus) in Great Britain between 1985 and 2004. The application to simulated and real data demonstrates how the hierarchical model structure accommodates various sources of uncertainty which occur at different stages of the link between observational data and the modelled abundance, thereby it accounts for these uncertainties in the inference of abundance variations.
4. We show that by using hierarchical observation models that integrate different types of commonly available data sources, we can improve the estimates of variation in species abundances across space and time. This will improve our ability to detect regional trends and can also enhance the empirical basis for understanding range dynamics.