Species-Energy Theory: An Extension of Species-Area Theory

Wright, David H.

doi:10.2307/3544109

Cited by 1,253 publications

(1,245 citation statements)

References 39 publications

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“…In 1102 summary, the two products aboveground biomass map and biomass change will capture 1103 the successional state and the rate of succession, and the underlying mechanism of 1104 successional trajectory as shown in Figure 9b. The occurrence of such patterns has been 1105 documented for several different mature forest systems and is consistent with the mosaic 1106 dynamics of mature forests (Whitmore 1974, Knight 1975, Hartshorn 1978, Raup 1964, 1107White 1979, and Oliver 1981 (Wright 1983) and over landscapes at 1119 regional scales by topography (Burnett et al, 1998;Thompson and Brown, 1992). 1120…”

Section: Required Biomass Change Measurement Capabilities 1043supporting

confidence: 57%

Characterizing 3D vegetation structure from space: Mission requirements

Hall¹,

Bergen²,

Blair³

et al. 2011

Remote Sensing of Environment

194

118

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18Human and natural forces are rapidly modifying the global distribution and 19 structure of terrestrial ecosystems on which all of life depends, altering the global carbon 20 cycle, affecting our climate now and for the foreseeable future, causing steep reductions 21 in species diversity, and endangering Earth's sustainability. 22To understand changes and trends in terrestrial ecosystems and their functioning 23 as carbon sources and sinks, and to characterize the impact of their changes on climate, 24 habitat and biodiversity, new space assets are urgently needed to produce high spatial 25 resolution global maps of the three-dimensional (3D) structure of vegetation, its biomass 26 above ground, the carbon stored within and the implications for atmospheric green house 27 gas concentrations and climate. These needs were articulated in a 2007 National 28Research Council (NRC) report (NRC, 2007) recommending a new satellite mission, 29DESDynI, carrying an L-band Polarized Synthetic Aperture Radar (Pol-SAR) and a 30 multi-beam lidar (Light RAnging And Detection) operating at 1064 nm. The objectives 31 of this paper are to articulate the importance of these new, multi-year, 3D vegetation 32 structure and biomass measurements, to briefly review the feasibility of radar and lidar 33 remote sensing technology to meet these requirements, to define the data products and 34 measurement requirements, and to consider implications of mission durations. The paper 35 addresses these objectives by synthesizing research results and other input from a broad 36 community of terrestrial ecology, carbon cycle, and remote sensing scientists and 37 working groups. We conclude that: 38(1) current global biomass and 3-D vegetation structure information is unsuitable 39 for both science and management and policy. The only existing global datasets of 40 biomass are approximations based on combining land cover type and representative 41 carbon values, instead of measurements of actual biomass. Current measurement attempts 42 based on radar and multispectral data have low explanatory power outside low biomass 43areas. There is no current capability for repeatable disturbance and regrowth estimates. 44(2) The science and policy needs for information on vegetation 3D structure can 45 be successfully addressed by a mission capable of producing (i) a first global inventory of 46 forest biomass with a spatial resolution 1km or finer and unprecedented accuracy (ii) 47 annual global disturbance maps at a spatial resolution of 1 ha with subsequent biomass 48 accumulation rates at resolutions of 1km or finer, and (iii) transects of vertical and 49 horizontal forest structure with 30 m along-transect measurements globally at 25 m 50 spatial resolution, essential for habitat characterization. 51 We also show from the literature that lidar profile samples together with wall-to-52 wall L-band quad-pol-SAR imagery and ecosystem dynamics models can work together 53 to satisfy these vegetation 3D structure and biomass measurement requirements. ...

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Section: Required Biomass Change Measurement Capabilities 1043supporting

confidence: 57%

Characterizing 3D vegetation structure from space: Mission requirements

Hall¹,

Bergen²,

Blair³

et al. 2011

Remote Sensing of Environment

194

118

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“…That geographical variation in species richness correlates positively with energy availability was recognised in the 19th century (Wallace, 1878) and it has frequently been suggested that spatial variation in energy availability controls species richness (Hutchinson, 1959 ;Connell & Orias, 1964 ;Leigh, 1965 ;MacArthur & Pianka, 1966 ;Rohde, 1978;Schall & Pianka, 1978;Rickerson & Lum, 1980). Wright (1983) built upon such studies to propose the species-energy theory, that energy sets an upper limit to species richness. Interest in species-energy relationships grew during the 1980s and 1990s (e.g.…”

Section: Introductionmentioning

confidence: 99%

Species–energy relationships at the macroecological scale: a review of the mechanisms

2005

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Correlations between the amount of energy received by an assemblage and the number of species that it contains are very general, and at the macro-scale such species-energy relationships typically follow a monotonically increasing curve. Whilst the ecological literature contains frequent reports of such relationships, debate on their causal mechanisms is limited and typically focuses on the role of energy availability in controlling the number of individuals in an assemblage. Assemblages from high-energy areas may contain more individuals enabling species to maintain larger, more viable populations, whose lower extinction risk elevates species richness. Other mechanisms have, however, also been suggested. Here we identify and clarify nine principal mechanisms that may generate positive species-energy relationships at the macro-scale. We critically assess their assumptions and applicability over a range of spatial scales, derive predictions for each and assess the evidence that supports or refutes them. Our synthesis demonstrates that all mechanisms share at least one of their predictions with an alternative mechanism. Some previous studies of species-energy relationships appear not to have recognised the extent of shared predictions, and this may detract from their contribution to the debate on causal mechanisms. The combination of predictions and assumptions made by each mechanism is, however, unique, suggesting that, in principle, conclusive tests are possible. Sufficient testing of all mechanisms has yet to be conducted, and no single mechanism currently has unequivocal support. Each may contribute to species-energy relationships in some circumstances, but some mechanisms are unlikely to act simultaneously. Moreover, a limited number appear particularly likely to contribute frequently to species-energy relationships at the macro-scale. The increased population size, niche position and diversification rate mechanisms are particularly noteworthy in this context.

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“…In previous studies, lake area always contributed the most to the fish species richness patterns, since that large area could support more fish species to coexist, say species richness -area hypothesis (Wright, 1983). Over the last two decades, numerous of case studies in Africa, US, Canada, Tropical Asia and Europe all verified the species richness-area patterns in a broad scale (Amarasinghe and Welcomme, 2002).…”

Section: Prediction and Determinants Of Fish Species Richness In Chinmentioning

confidence: 85%

Predicting fish species richness and assemblages with climatic, geographic and morphometric factors: A broad-scale study in Chinese lakes

Guo

Lek

et al. 2015

Limnologica

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a b s t r a c tThe present study was designed to investigate the relative importance of climatic (temperature and precipitation), geographic (altitude) and morphometric (lake area) factors in predicting fish species richness and assemblages in Chinese lakes at a large spatial scale. Two recursive partitioning tree-based approaches: Classification and Regression Trees (CARTs) and Multivariate Regression Trees (MRTs) were employed to generate predictive models respectively. Six fish assemblages were thus defined from the MRT model. The results indicated that lake altitude was the main determinant for predicting fish assemblages in Chinese lakes (30.43%), followed by precipitation of the driest month (10.47%), temperature annual range (3.62%) and annual mean temperature (3.15%). Validated CART model implied that precipitation of driest month, maximum temperature of warmest month and lake area were the main predictors in determining fish species richness patterns. Overall, our results indicated that the altitudinal extent and range of climatic variation was sufficient to overshadow the area effect in predicting fish species richness and assemblages in Chinese lakes. At the macroecological scale, the effect of temperature and precipitation on fish richness and assemblages also suggests future changes in fish diversity as a consequence of climate change.

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Species-Energy Theory: An Extension of Species-Area Theory

Cited by 1,253 publications

References 39 publications

Characterizing 3D vegetation structure from space: Mission requirements

Characterizing 3D vegetation structure from space: Mission requirements

Species–energy relationships at the macroecological scale: a review of the mechanisms

Predicting fish species richness and assemblages with climatic, geographic and morphometric factors: A broad-scale study in Chinese lakes

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