On the expected distribution of species' ranges

Paleobiology ◽  
1988 ◽  
Vol 14 (2) ◽  
pp. 126-138 ◽  
Author(s):  
Carl F. Koch ◽  
John P. Morgan
Keyword(s):  
Species Richness ◽  
Sample Size ◽  
Species Range ◽  
Data Set ◽  
Species Ranges ◽  
Richness Patterns ◽  
Fossil Data

A method is provided to calculate the expected distribution of species' ranges for use as a basis of comparison for paleontological interpretation of species' range charts. If the method is used for such studies, the possibility of suggesting an environmental or biological cause for observed patterns, when in fact no such cause exists, will be diminished. The method is applied to a large, well-studied fossil data set to illustrate some possible species' range patterns which result only from sample size inequities. As examples, stepwise extinction and species richness patterns are predicted for situations where, in reality, none exist.

scholarly journals Low predictive power of mid-domain effect to explain geographic species richness patterns in Palearctic songbirds

2007 ◽  
Vol 76 (3) ◽  
pp. 197-204 ◽  
Author(s):  
M. Aliabadian ◽  
C. S. Roselaar ◽  
R. Sluys ◽  
V. Nijman
Keyword(s):  
Species Richness ◽  
Predictive Power ◽  
Explanatory Power ◽  
Null Model ◽  
Species Range ◽  
Geometric Constraints ◽  
Diversity Patterns ◽  
Distribution Maps ◽  
Biogeographic Patterns ◽  
Richness Patterns

In the study of diversity patterns, the Mid-domain effect (MDE), which explains gradients in diversity solely on the basis of geometric constraints, has emerged as a null-model against which other hypotheses can be tested. The effectiveness, measured by its predictive power, of these MDE models appears to depend on the size of the study area and the range-sizes of the taxa considered. Here we test the predictive power of MDE on the species richness patterns of birds and assess its effectiveness for a variety of species range sizes. We digitised distribution maps of 889 species of songbird endemic to the Palearctic, and analysed the emergent biogeographic patterns with WORLDMAP software. MDE had a predictive power of 20% when all songbirds were included. Major hotspots were located south of the area where MDE predicted the highest species-richness, and some of the observed coldspots were in the centre of the Palearctic, contradicting the predictions of the MDE. MDE had little explanatory power (3-19%) for all but the largest range sizes, whereas MDE performed equal or better for the large-ranged species (20-34%) compared to the overall model. Overall MDE did not accurate explain species-richness patterns in Palearctic songbirds. Subsets of larger-range species did not always have a larger predictive power than smaller-range species or the overall model. Despite their low predictive power, MDE models can have a role to play in explaining biogeographic patterns but other variables need to be included in the model as well.

Calibrating spatial and temporal species richness patterns in tropical American Marginellid gastropods

2001 ◽  
Vol 75 (3) ◽  
pp. 680-696 ◽  
Author(s):  
Ross H. Nehm
Keyword(s):  
Species Richness ◽  
Sample Size ◽  
Temporal Patterns ◽  
South Florida ◽  
Tropical Eastern Pacific ◽  
Time And Space ◽  
Western Atlantic ◽  
Temporal Intervals ◽  
Recent Species ◽  
Richness Patterns

Collectively, studies of the structure of Neogene diversity change in tropical American mollusks have lacked 1) species-level analyses within well-established clades; 2) consideration of abundance and sample size on diversity estimates and comparisons; and 3) geographic comparisons within temporal intervals. This study takes all three factors into consideration and compares Miocene to Recent species richness patterns in tropical American marginellid gastropod species within the clade Prunum+Volvarina. Rarefaction analyses of more than 16,000 specimens from more than 500 samples are used to standardize comparisons of species richness through time and space. Species richness in Prunum+Volvarina from the Miocene to the Recent of the Tropical Western Atlantic (TWA) is compared along a latitudinal gradient from north to south (Florida, the Dominican Republic, and Venezuela). Additionally, temporal patterns of diversity change are compared between the TWA and the Tropical Eastern Pacific (TEP: Peru, Ecuador, Colombia, Panama, and Costa Rica).As is the case with most Neogene lineages, the number of marginellid specimens and samples differ significantly through both time and space. Rarefaction analyses of both specimens and samples indicate that: 1) significant geographic differences in species richness were detected between the Miocene, Pliocene, Pleistocene, and Recent of the TWA; 2) temporal patterns of species richness were similar in the northern and southern TWA; 3) from the Miocene to the Recent, marginellid species richness in the TEP has always been significantly less than TWA diversity; and 4) from the Miocene to the Recent, TWA diversity decreased significantly, whereas TEP diversity was stable and low. Separate rarefaction analyses using the numbers of specimens and samples did not always produce concordant results and indicate that the unit of analysis influences estimates of species richness. Discordant specimen/sample rarefaction results may be a product of sample size. Intrinsic ecological and evolutionary differences do not appear to be primary contributors to differences in marginellid species richness between the TEP and TWA.

scholarly journals Does climate limit species richness by limiting individual species’ ranges?

2014 ◽  
Vol 281 (1776) ◽  
pp. 20132695 ◽  
Author(s):  
Véronique Boucher-Lalonde ◽  
Jeremy T. Kerr ◽  
David J. Currie
Keyword(s):  
Species Richness ◽  
Natural Experiment ◽  
Migratory Birds ◽  
Individual Species ◽  
Strongly Correlated ◽  
Top Down ◽  
Bottom Up ◽  
Species Ranges ◽  
Number Of Species ◽  
Richness Patterns

Broad-scale geographical variation in species richness is strongly correlated with climate, yet the mechanisms underlying this correlation are still unclear. We test two broad classes of hypotheses to explain this pattern. Bottom-up hypotheses propose that the environment determines individual species’ ranges. Ranges then sum up to yield species richness patterns. Top-down hypotheses propose that the environment limits the number of species that occur in a region, but not which ones. We test these two classes of hypotheses using a natural experiment: seasonal changes in environmental variables and seasonal range shifts of 625 migratory birds in the Americas. We show that richness seasonally tracks the environment. By contrast, individual species’ geographical distributions do not. Rather, species occupy different sets of environmental conditions in two seasons. Our results are inconsistent with extant bottom-up hypotheses. Instead, a top-down mechanism appears to constrain the number of species that can occur in a given region.

Prediction of sample size effects on the measured temporal and geographic distribution patterns of species

Paleobiology ◽  
1987 ◽  
Vol 13 (1) ◽  
pp. 100-107 ◽  
Author(s):  
Carl F. Koch
Keyword(s):  
Species Richness ◽  
Sample Size ◽  
Size Effects ◽  
Large Data ◽  
Distribution Patterns ◽  
Data Sets ◽  
Data Set ◽  
Large Computer ◽  
Two Samples ◽  
Unique Species

Few paleontological studies of species distribution in time and space have adequately considered the effects of sample size. Most species occur very infrequently, and therefore sample size effects may be large relative to the faunal patterns reported. Examination of 10 carefully compiled large data sets (each more than 1,000 occurrences) reveals that the species-occurrence frequency distribution of each fits the log series distribution well and therefore sample size effects can be predicted. Results show that, if the materials used in assembling a large data set are resampled, as many as 25% of the species will not be found a second time even if both samples are of the same size. If the two samples are of unequal size, then the larger sample may have as many as 70% unique species and the smaller sample no unique species. The implications of these values are important to studies of species richness, origination, and extinction patterns, and biogeographic phenomena such as endemism or province boundaries. I provide graphs showing the predicted sample size effects for a range of data set size, species richness, and relative data size. For data sets that do not fit the log series distribution well, I provide example calculations and equations which are usable without a large computer. If these graphs or equations are not used, then I suggest that species which occur infrequently be eliminated from consideration. Studies in which sample size effects are not considered should include sample size information in sufficient detail that other workers might make their own evaluation of observed faunal patterns.

scholarly journals The Effect of Climate and Human Pressures on Functional Diversity and Species Richness Patterns of Amphibians, Reptiles and Mammals in Europe

Diversity ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 275
Author(s):  
Mariana A. Tsianou ◽  
Maria Lazarina ◽  
Danai-Eleni Michailidou ◽  
Aristi Andrikou-Charitidou ◽  
Stefanos P. Sgardelis ◽  
...  
Keyword(s):  
Species Richness ◽  
Functional Diversity ◽  
Agricultural Land ◽  
Influential Factor ◽  
Climatic Variables ◽  
Quadratic Entropy ◽  
Functional Richness ◽  
Precipitation Seasonality ◽  
Rao’S Quadratic Entropy ◽  
Richness Patterns

The ongoing biodiversity crisis reinforces the urgent need to unravel diversity patterns and the underlying processes shaping them. Although taxonomic diversity has been extensively studied and is considered the common currency, simultaneously conserving other facets of diversity (e.g., functional diversity) is critical to ensure ecosystem functioning and the provision of ecosystem services. Here, we explored the effect of key climatic factors (temperature, precipitation, temperature seasonality, and precipitation seasonality) and factors reflecting human pressures (agricultural land, urban land, land-cover diversity, and human population density) on the functional diversity (functional richness and Rao’s quadratic entropy) and species richness of amphibians (68 species), reptiles (107 species), and mammals (176 species) in Europe. We explored the relationship between different predictors and diversity metrics using generalized additive mixed model analysis, to capture non-linear relationships and to account for spatial autocorrelation. We found that at this broad continental spatial scale, climatic variables exerted a significant effect on the functional diversity and species richness of all taxa. On the other hand, variables reflecting human pressures contributed significantly in the models even though their explanatory power was lower compared to climatic variables. In most cases, functional richness and Rao’s quadratic entropy responded similarly to climate and human pressures. In conclusion, climate is the most influential factor in shaping both the functional diversity and species richness patterns of amphibians, reptiles, and mammals in Europe. However, incorporating factors reflecting human pressures complementary to climate could be conducive to us understanding the drivers of functional diversity and richness patterns.

PSVI-8 Meta-regression Analysis to Determine the Relationship Between Growing Pig Body Weight and Variation

2021 ◽  
Vol 99 (Supplement_1) ◽  
pp. 218-219
Author(s):  
Andres Fernando T Russi ◽  
Mike D Tokach ◽  
Jason C Woodworth ◽  
Joel M DeRouchey ◽  
Robert D Goodband ◽  
...  
Keyword(s):  
Body Weight ◽  
Regression Analysis ◽  
Sample Size ◽  
Polynomial Regression ◽  
Data Sets ◽  
Regression Equations ◽  
Prediction Equations ◽  
Data Set ◽  
Rate Of Increase ◽  
The Relationship

Abstract The swine industry has been constantly evolving to select animals with improved performance traits and to minimize variation in body weight (BW) in order to meet packer specifications. Therefore, understanding variation presents an opportunity for producers to find strategies that could help reduce, manage, or deal with variation of pigs in a barn. A systematic review and meta-analysis was conducted by collecting data from multiple studies and available data sets in order to develop prediction equations for coefficient of variation (CV) and standard deviation (SD) as a function of BW. Information regarding BW variation from 16 papers was recorded to provide approximately 204 data points. Together, these data included 117,268 individually weighed pigs with a sample size that ranged from 104 to 4,108 pigs. A random-effects model with study used as a random effect was developed. Observations were weighted using sample size as an estimate for precision on the analysis, where larger data sets accounted for increased accuracy in the model. Regression equations were developed using the nlme package of R to determine the relationship between BW and its variation. Polynomial regression analysis was conducted separately for each variation measurement. When CV was reported in the data set, SD was calculated and vice versa. The resulting prediction equations were: CV (%) = 20.04 – 0.135 × (BW) + 0.00043 × (BW)2, R2=0.79; SD = 0.41 + 0.150 × (BW) - 0.00041 × (BW)2, R2 = 0.95. These equations suggest that there is evidence for a decreasing quadratic relationship between mean CV of a population and BW of pigs whereby the rate of decrease is smaller as mean pig BW increases from birth to market. Conversely, the rate of increase of SD of a population of pigs is smaller as mean pig BW increases from birth to market.

scholarly journals Optimal breeding-value prediction using a Sparse Selection Index

Genetics ◽  
2021 ◽  
Author(s):  
Marco Lopez-Cruz ◽  
Gustavo de los Campos
Keyword(s):  
Sample Size ◽  
Dna Sequences ◽  
Genomic Prediction ◽  
Prediction Accuracy ◽  
Regularization Parameter ◽  
Selection Index ◽  
Prediction Method ◽  
Training Data ◽  
Breeding Value ◽  
Data Set

Abstract Genomic prediction uses DNA sequences and phenotypes to predict genetic values. In homogeneous populations, theory indicates that the accuracy of genomic prediction increases with sample size. However, differences in allele frequencies and in linkage disequilibrium patterns can lead to heterogeneity in SNP effects. In this context, calibrating genomic predictions using a large, potentially heterogeneous, training data set may not lead to optimal prediction accuracy. Some studies tried to address this sample size/homogeneity trade-off using training set optimization algorithms; however, this approach assumes that a single training data set is optimum for all individuals in the prediction set. Here, we propose an approach that identifies, for each individual in the prediction set, a subset from the training data (i.e., a set of support points) from which predictions are derived. The methodology that we propose is a Sparse Selection Index (SSI) that integrates Selection Index methodology with sparsity-inducing techniques commonly used for high-dimensional regression. The sparsity of the resulting index is controlled by a regularization parameter (λ); the G-BLUP (the prediction method most commonly used in plant and animal breeding) appears as a special case which happens when λ = 0. In this study, we present the methodology and demonstrate (using two wheat data sets with phenotypes collected in ten different environments) that the SSI can achieve significant (anywhere between 5-10%) gains in prediction accuracy relative to the G-BLUP.

scholarly journals Arthropod community structure in pastures of an island archipelago (Azores): looking for local–regional species richness patterns at fine-scales

2004 ◽  
Vol 94 (2) ◽  
pp. 111-121 ◽  
Author(s):  
P.A.V. Borges ◽  
V.K. Brown
Keyword(s):  
Species Richness ◽  
Regional Scale ◽  
Local Scale ◽  
Local Species Richness ◽  
Β Diversity ◽  
Arthropod Species ◽  
Α Diversity ◽  
Local Species ◽  
Richness Patterns

AbstractThe arthropod species richness of pastures in three Azorean islands was used to examine the relationship between local and regional species richness over two years. Two groups of arthropods, spiders and sucking insects, representing two functionally different but common groups of pasture invertebrates were investigated. The local–regional species richness relationship was assessed over relatively fine scales: quadrats (= local scale) and within pastures (= regional scale). Mean plot species richness was used as a measure of local species richness (= α diversity) and regional species richness was estimated at the pasture level (= γ diversity) with the ‘first-order-Jackknife’ estimator. Three related issues were addressed: (i) the role of estimated regional species richness and variables operating at the local scale (vegetation structure and diversity) in determining local species richness; (ii) quantification of the relative contributions of α and β diversity to regional diversity using additive partitioning; and (iii) the occurrence of consistent patterns in different years by analysing independently between-year data. Species assemblages of spiders were saturated at the local scale (similar local species richness and increasing β-diversity in richer regions) and were more dependent on vegetational structure than regional species richness. Sucking insect herbivores, by contrast, exhibited a linear relationship between local and regional species richness, consistent with the proportional sampling model. The patterns were consistent between years. These results imply that for spiders local processes are important, with assemblages in a particular patch being constrained by habitat structure. In contrast, for sucking insects, local processes may be insignificant in structuring communities.

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