Species Distribution Modeling: Mammal river species

Species Distribution Modeling: Mammal river species

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When working with mammal river species, what parameters would you use to study them (I have max and min temperature, average rain, slope and obstacles in the river) Something like type of riparian forest? Is there any good example of species distribution modelling of mammal river species?

Thanks, Héctor.

National Species Dataset

NatureServe’s foundational dataset includes more than 900,000 location records (element occurrences) from our Network of biological inventories operating in all 50 states and in most of Canada. Over four decades, this network has collected and managed detailed local information on plants and animals of conservation concern and has become a leading source for information about North America’s endangered species. NatureServe offers one-stop access to this information from throughout the region.


Predicting and mapping species distributions, including geographic range and variability in abundance, is fundamental to the conservation and management of biodiversity and landscapes 1 . The ecological niche defines species-habitat relationships 2,3,4 and provides a useful framework for understanding the range and abundance of species in relation to biotic and abiotic factors. Further, niche relationships across local scales can provide novel information about the ecology, conservation, and management of species at macro scales 5 . Most studies evaluating a species’ niche across their distribution focus on presence-absence occurrence data to predict the geographic range 6 however, conservation and management plans for species can be improved by understanding patterns of population abundance and density across a species’ range 7 . In particular, evaluating population density, compared to occurrence, can reveal novel patterns of species distributions in relation to landscape factors 8 .

There is an ongoing paradigm shift in understanding how biotic and abiotic factors shape species distributions. Until recently, it was widely accepted that abiotic factors, such as temperature and precipitation, played the primary role in shaping distributions of species and biodiversity at broad scales (e.g., regional, continental, global extents) and that biotic factors were most important at fine scales (e.g., site, local extents) 9,10,11 . It is increasingly recognized, however, that biotic factors are important determinants of species distributions at broad spatial scales, especially when considering biotic interactions 12,13,14,15,16 . Although interspecific competition can be an important biotic determinant in species distribution models at broad scales, other forms of biotic interactions, such as predation and symbioses, can also be important determinants 15,17 , but have received less attention 18 . In addition, although researchers have evaluated the effects of biotic interactions on geographic range limits 18 , relatively few studies have evaluated how biotic factors influence population density across a species’ range 19,20 , which can be more informative in understanding macro-ecological patterns 7,21 .

In addition to species interactions, biotic factors related to vegetation can influence species distributions and abundance at broad scales. In particular, anthropogenic land-use change is rarely considered when evaluating species distributions at broad scales however, given the human footprint globally 22 and projections for expanding human impacts on the environment 23,24 , biotic factors created by human activities are potentially important predictors that can contribute to a better understanding of species distributions 8 . For example, agricultural crops are a dominant biotic factor across continents that are facilitated by human engineering and the redistribution of ecological resources and energy, which can have profound impacts on plant and animal populations across broad extents agriculture can increase populations for some species through increased food, resource availability, and landscape heterogeneity, or decrease populations due to loss of habitat 25,26,27 . Ultimately, further evaluation is necessary to understand the relative importance of abiotic and biotic factors in shaping species distributions across broad spatial scales 13,15 .

Invasive species are a primary driver of widespread and severe negative impacts to ecosystems, agriculture, and humans across local to global scales 28 . These introduced plants and animals often exhibit broad geographic distributions, can be relatively well studied across local scales, and provide novel opportunities to evaluate broad-scale patterns of niche relationships 29 . Predictions of potential geographic distribution of invasive species can provide critical information that can inform the prevention, eradication, and control of populations, which has been evaluated for many taxa, including plants 30 , amphibians 31 , and invertebrates 32 . However, few studies have predicted the potential ranges and abundance of non-native mammals 33 . Especially for wide-ranging species that can occur across broad extents of landscapes, predictions of how population density varies spatially can provide important information for prioritizing conservation and management actions.

Few species exhibit a global distribution that extends across Europe, Asia, Africa, North and South America, Australia, and oceanic Islands. Besides naturalized animals, such as the house mouse (Mus musculus) and brown rat (Rattus norvegicus), wild pigs (Sus scrofa other common names include wild boar, wild/feral swine, wild/feral hog, and feral pig) have one of the widest geographic distributions of any mammal further, it exhibits the widest geographic range of any large mammal 34 , with the exception of humans. The expansive global distribution of wild pigs is attributed to its broad native range in Eurasia and northern Africa, widespread introduction by humans outside its native range, and superior adaptability, where it occurs in a wide variety of ecological communities, ranging from deserts to temperate and tropical environments 35,36 , with a corresponding diverse omnivorous diet 37 . Across its non-native range (Fig. 1 Supplementary Methods S1), including North and South America, Australia, sub-Saharan Africa, and many islands, wild pigs are considered one of the 100 most harmful invasive species in the world 38 due to wide-ranging ecosystem disturbance, agricultural damage, pathogen and disease vectors to wildlife, livestock and people, and social impacts to people and property 39,40,41 . Wild pigs are therefore a model species to evaluate biotic and abiotic factors associated with population density because they exhibit a global distribution across six continents, are widely studied across much of their native and non-native (i.e., invasive or introduced) ranges, and previous research has indicated that their population density was related to abiotic factors across a continental scale, although it was ambiguous how biotic factors shape their abundance, warranting further study 42 .

Areas of white indicate locations in which wild pigs are likely not present. This map was created using ArcGIS 10.3.1 98 . See Supplementary Methods S1 for a description of methods and citations used for creating the map of wild pig global distribution across its native and non-native ranges.

To address these ecological questions and understand the relative importance of biotic and abiotic factors in shaping the global distribution of a highly invasive mammal, we evaluated estimates of population density of wild pigs across diverse environments on five continents. Specifically, we (1) evaluate how biotic (i.e., vegetation and predation) and abiotic (i.e., climate) factors (Table 1) shape population density across a global scale and (2) create a predictive distribution map of potential population density across the world. We also compare population density between island and mainland populations. Our results contribute novel insight into the relative roles of biotic and abiotic factors in shaping the distribution of species’ population densities across continental and global scales, particularly relating to human-mediated land-use change, which can provide critical information to management and conservation strategies.

Ecological niche structure and rangewide abundance patterns of species

Spatial abundance patterns across species' ranges have attracted intense attention in macroecology and biogeography. One key hypothesis has been that abundance declines with geographical distance from the range centre, but tests of this idea have shown that the effect may occur indeed only in a minority of cases. We explore an alternative hypothesis: that species' abundances decline with distance from the centroid of the species' habitable conditions in environmental space (the ecological niche). We demonstrate consistent negative abundance–ecological distance relationships across all 11 species analysed (turtles to wolves), and that relationships in environmental space are consistently stronger than relationships in geographical space.

1. Introduction

An important paradigm in ecology concerns population abundance trends across species' geographical distributions [1]. It has been argued that abundances are highest at the geographical centres of species' distributions, and lowest along the periphery [2–4] this notion has been used to predict extinction probabilities [5,6] and is prominent in the conservation biology literature [7,8]. Nonetheless, empirical tests of this idea have yielded mixed results: the geographical distribution–abundance relationship is not straightforward, and many exceptions have emerged [9,10].

Explanations of observed abundance variation across species' ranges invoke dispersal mechanisms in source-sink systems [11] and fitness responses to variation in critical habitat variables [4]. Ultimately, however, geographical abundance patterns should reflect, at least in part, the extent to which niche requirements are fulfilled at each site [12], such that ‘niche’ is the N-dimensional hypervolume within which populations can be self-maintained indefinitely [13]. Maguire [14] proposed that the niche has an internal structure where optimal conditions are at the centroid of the hypervolume if this is true, then geographical abundance patterns across ranges respond to the arrangement of environmental conditions relative to the niche centroid across landscapes.

Ecological niche modelling was developed principally for characterizing distributions of species, but has had little connection to underlying population-biological processes [15,16]. Although theoretical treatments have addressed the distributional consequences of these processes [17–21], no empirical studies have as yet linked niche model outputs rigorously to population processes [19,20]. The niche modelling framework offers an alternative viewpoint on the central–peripheral question: as with other recent efforts [22,23], population processes can be examined in both geographical and ecological dimensions simultaneously. Here, we re-examine the question of abundance patterns, comparing relationships between abundance and geographical centrality with those between abundance and environmental centrality.

2. Material and methods

To develop tests of abundance as a function of distances to centroids of species' distributions in geographical and environmental spaces, we required data for each species at an array of sites, plus independent data on occurrences with which to calibrate models. Abundance data for four bird species (Toxostoma redivivum, Calamospiza melanocorys, Spiza americana, Hylocichla mustelina) were derived from the North American Breeding Bird Survey [24] we used route totals averaged over 1968–2004. Other taxa and data sources included wintering populations of the sandpiper Tryngites subruficollis individuals per trap-night for the mice Peromyscus leucopus and P. maniculatus survey publications (individuals per park) for wolves Canis lupus individuals per 100 km 2 for jaguars P. onca and individuals per hectare for the turtle Clemmys guttata and the howler monkey Alouatta palliata (see electronic supplementary material, appendix S1). In each case, we sought species occurrence data independent of the sources of abundance information, thereby providing a way to calibrate ecological niche models in the data resources served by the Global Biodiversity Information Facility (GBIF Sources for all data are in electronic supplementary material, appendix S1 as necessary, textual locality descriptors were georeferenced via electronic databases [25].

Raster-format data for modelling included the 19 ‘bioclimatic’ dimensions in WorldClim [26], plus elevation, slope and topographic index from Hydro-1K [27], which were resampled to 2–20 km resolution, with finer resolutions for species with smaller distributions to provide sufficient detail.

Ecological niches were modelled, using GARP [28] O pen M odeller Desktop v. 1.1.0 ( GARP estimates niches in environmental dimensions by relating characteristics of known occurrences to those of points randomly sampled from across the study region in order to develop decision rules that summarize factors associated with the species' presence [28,29]. For each species, 100 replicate models were built the 20 with lowest omission retained, and the 10 closest to median area predicted suitable were summed as a final consensus model (modified from Pearson et al. [30]). Finally, we thresholded model predictions to produce binary maps by establishing the level at which 90 per cent of input occurrence points are included in the prediction. GARP's predictive abilities have been tested [31–33], and it typically produces results on par with other methodologies [34]. Our data and the GARP models are deposited in the University of Kansas Repository and made available at [35].

To characterize niches, we combined environmental variables with model prediction in ArcGIS v. 9.3 (ESRI, Redlands, CA, USA), producing a grid with an attributes table summarizing unique environmental combinations across the study region. We identified grid cells corresponding to points where we had abundance data, transformed environmental variables to standard normal variates and calculated the centroid in environmental space as the mean value of suitable pixels in each environmental dimension. We then calculated Euclidean distances from all pixels to the ecological niche centroid for comparison, we calculated distances from all points to the geographical centroid, with geographical distributions drawn from diverse ‘extent of occurrence’ resources (see electronic supplementary material, appendix S1). We related observed abundances to both of these distance measures via regression (best fit of exponential, logarithmic, power, cubic or linear) we also used a bootstrapping routine in R that uses 1000 simulations using 70 per cent of records for training and 30 per cent for testing. We calculated proportions of test records falling within 95% CIs as a probability value measuring performance of the model.

3. Results

As an exemplar, we chose the California thrasher (Toxostoma redivivum), the species used by Grinnell [36] to develop the concept of niches (figure 1). For this species, we found no significant association between abundance and distance to the geographical centroid (R 2 = 0.064, p = 0.234 figure 2a). Centroid distance in environmental space, however, showed considerable explanatory power for abundances (R 2 = 0.312, p = 0.001 figure 2b): populations farther from the niche centroid in environmental space were smaller in numbers. Hence, distance in environmental space explained considerable variation in abundance, whereas geographical distance to the centre of the species' distribution did not.

Figure 1. Geographical and environmental distribution of the California thrasher (Toxostoma redivivum). Map of western North America showing known occurrences with abundance information (white dots dot sizes indicate numbers of individuals per route), the geographical centroid of the species' distribution (black star), modelled distribution (dark grey) and the geographical location of the environmental centroid of the ecological niche (white star). Inset: visualization of the distribution of the species in a space of annual mean temperature and annual precipitation, showing environments across western North America (light grey), environments modelled as suitable for the species (dark grey), abundance occurrences of this species (white dots dot sizes indicate numbers of individuals per route), environmental conditions at the centroid of the species' geographical distribution (black star) and the centroid of niche in environmental space (white star).

Figure 2. Relationships between abundance and distances to (a) geographical and (b) environmental centroids for the California Thrasher (Toxostoma redivivum).

Parallel analyses used 10 additional species with body masses spanning three orders of magnitude (table 1). In 10 of 11 cases, significant (p < 0.05) negative abundance–environmental distance relationships existed regressions explained 7–69% of overall variation and there was no significant dependence of R 2 on sample sizes (p = 0.178). By contrast, abundance–geographical distance relationships were not significant in seven of 11 species, and R 2 -values were lower in nine of 11 species (0.005–0.327 table 1).

Table 1. Relationships between population abundances and distances to geographical and environmental centroids in 11 species. ‘Pixel size’ refers to the spatial resolution of environmental variables used for analyses. ‘Regression’ indicates the form of the curve that best fit the data when a significant relationship was found. ‘Predictive power’ refers to the bootstrap-based assessment of ability to anticipate abundances for data records omitted from particular analyses.

4. Discussion

Our results suggest that the geographical ‘abundant-centre hypothesis’ [4,10] is not causal. It ‘works’ by happenstance when geographical ranges and ecological niches coincide in their central tendencies. Instead, we posit that ecological niches play a role in defining more than range limits [18,37]: the geographical structure of species' abundance patterns [38] maps onto patterns of centrality in ecological niche space [12,14].

The observed inverse relationships between abundance and distance to the centroid are generally nonlinear in nature (except for the turtle). This realization implies that: (i) sites presenting optimal niche conditions support many more individuals than most occupied sites [12] and (ii) optimal niche conditions are relatively narrow, such that few sites hold suitable conditions for maintaining large populations [12]. Implications of this asymmetry for population biology are profound: because more individuals are produced in highly suitable areas, migration rates to suboptimal sites are higher, limiting adaptation to novel conditions and reinforcing niche conservatism [18,21,39].

We also found exceptions to the general trend. First, for the migrant buff-breasted sandpiper the abundance–niche centroid relationship was inverse but not significant. Analyses for this species were conducted across the wintering distribution the rest of the species were analysed across breeding distributions. Some migratory species shift ecological niches between seasons [40], responding to different requirements it is thus possible that the winter niche of this species is less climatically driven, but this possibility needs further exploration. Lastly, the spotted turtle presented an inverse linear relationship, suggesting that optimality of sites reduces monotonically rather than abruptly. This result, however, may be an artefact of small sample sizes (n = 14), clearly lacking a detailed representation of population size variability across the species' geographical range.

The abundant (geographical) centrality idea has been a paradigm in biogeography for decades [2,7,8,41,42]. However, support for this idea as a general relationship has been unclear [9,10]. The environmental centrality result, on the other hand, has both a conceptual underpinning [14,38], and now empirical support.


Environmental DNA (eDNA)

DNA captured from an environmental sample without first isolating any target organisms (Taberlet, Coissac, Hajibabaei, & Rieseberg, 2012 ). Traces of DNA can be from faeces, mucus, skin cells, organelles, gametes or even extracellular DNA. Environmental DNA can be sampled from modern environments (e.g., seawater, freshwater, soil or air) or ancient environments (e.g., cores from sediment, ice or permafrost, see Thomsen & Willerslev, 2015 ).

Community DNA

DNA is isolated from bulk-extracted mixtures of organisms separated from the environmental sample (e.g., soil or water).

Macro-organism environmental DNA

Environmental DNA originating from animals and higher plants.


First defined by Hebert et al. ( 2003 ), the term refers to taxonomic identification of species based on single specimen sequencing of diagnostic barcoding markers (e.g., COI, rbcL).


Taxonomic identification of multiple species extracted from a mixed sample (community DNA or eDNA) which have been PCR-amplified and sequenced on a high-throughput platform (e.g., Illumina, Ion Torrent).

High-throughput sequencing (HTS)

Sequencing techniques that allow for simultaneous analysis of millions of sequences compared to the Sanger sequencing method of processing one sequence at a time.

Community DNA metabarcoding

HTS of DNA extracted from specimens or whole organisms collected together, but first separated from the environmental sample (e.g., water or soil).

Molecular Operational Taxonomic Unit (MOTU)

Group identified through use of cluster algorithms and a predefined percentage sequence similarity (e.g., 97% Blaxter et al., 2005 ).

Since the inception of high-throughput sequencing (HTS, Margulies et al., 2005 ), the use of metabarcoding as a biodiversity detection tool has drawn immense interest (e.g., Creer et al., 2016 Hajibabaei et al., 2011 ). However, there has yet to be clarity regarding what source material is used to conduct metabarcoding analyses (e.g., environmental DNA versus community DNA). Without clarity between these two source materials, differences in sampling, as well as differences in laboratory procedures, can impact subsequent bioinformatics pipelines used for data processing, and complicate the interpretation of spatial and temporal biodiversity patterns. Here, we seek to clearly differentiate among the prevailing source materials used and their effect on downstream analysis and interpretation for environmental DNA metabarcoding of animals and plants compared to that of community DNA metabarcoding.

With community DNA metabarcoding of animals and plants, the targeted groups are most often collected in bulk (e.g., soil, malaise trap or net), and individuals are removed from other sample debris and pooled together prior to bulk DNA extraction (Creer et al., 2016 ). In contrast, macro-organism eDNA is isolated directly from an environmental material (e.g., soil or water) without prior segregation of individual organisms or plant material from the sample and implicitly assumes that the whole organism is not present in the sample. Of course, community DNA samples may contain DNA from parts of tissues, cells and organelles of other organisms (e.g., gut contents, cutaneous intracellular or extracellular DNA). Likewise, macro-organism eDNA samples may inadvertently capture whole microscopic nontarget organisms (e.g., protists, bacteria). Thus, the distinction can at least partly break down in practice.

Another important distinction between community DNA and macro-organism eDNA is that sequences generated from community DNA metabarcoding can be taxonomically verified when the specimens are not destroyed in the extraction process. Here, sequences can then be generated from voucher specimens using Sanger sequencing. As the samples for eDNA metabarcoding lack whole organisms, no such in situ comparisons can be made. Taxonomic affinities can therefore only be established by directly comparing obtained sequences (or through bioinformatically generated operational taxonomic units (MOTUs)), to sequences that are taxonomically annotated such as NCBI's GenBank nucleotide database (Benson et al., 2013 ), bold (Ratnasingham & Hebert, 2007 ), or to self-generatedreference databases from Sanger-sequenced DNA (Olds et al., 2016 Sønstebø et al., 2010 Willerslev et al., 2014 ). Then, to at least partially corroborate the resulting list of taxa, comparisons are made with conventional physical, acoustic or visual-based survey methods conducted at the same time or compared with historical records from surveys for a location (see Table 1).

The difference in source material between community DNA and eDNA therefore has distinct ramifications for interpreting the scale of inference for time and space about the biodiversity detected. From community DNA, it is clear that the individual species were found in that time and place, but for eDNA, the organism that produced the DNA may be upstream from the sampled location (Deiner & Altermatt, 2014 ), or the DNA may have been transported in the faeces of a more mobile predatory species (e.g., birds depositing fish eDNA, Merkes, McCalla, Jensen, Gaikowski, & Amberg, 2014 ) or was previously present, but no longer active in the community and detection is from DNA that was shed years to decades before (Yoccoz et al., 2012 ). The latter means that the scale of inference both in space and in time must be considered carefully when inferring the presence for the species in the community based on eDNA.

Do geographic distribution, niche property and life form explain plants' vulnerability to global change?

We modelled the future distribution in 2050 of 975 endemic plant species in southern Africa distributed among seven life forms, including new methodological insights improving the accuracy and ecological realism of predictions of global changes studies by: (i) using only endemic species as a way to capture the full realized niche of species, (ii) considering the direct impact of human pressure on landscape and biodiversity jointly with climate, and (iii) taking species' migration into account. Our analysis shows important promises for predicting the impacts of climate change in conjunction with land transformation. We have shown that the endemic flora of Southern Africa on average decreases with 41% in species richness among habitats and with 39% on species distribution range for the most optimistic scenario. We also compared the patterns of species' sensitivity with global change across life forms, using ecological and geographic characteristics of species. We demonstrate here that species and life form vulnerability to global changes can be partly explained according to species' (i) geographical distribution along climatic and biogeographic gradients, like climate anomalies, (ii) niche breadth or (iii) proximity to barrier preventing migration. Our results confirm that the sensitivity of a given species to global environmental changes depends upon its geographical distribution and ecological proprieties, and makes it possible to estimate a priori its potential sensitivity to these changes.

V alidation

To validate the proposed methodology, we contrasted estimated values from rase with maximum likelihood inference using range centroids. We simulated one-dimensional trait evolution according to a BM process on simulated phylogenetic trees under a constant birth–death process ( Nee 2006). For every tip point value simulated under BM, we increased and decreased the range width (see below) from the point to obtain an enclosing symmetrical range to be used by rase. If the domain becomes infinitely small, the parameter estimates from rase should converge to the estimates based on domain centroids. We used two different values for the rate parameter ( ⁠ σ 2 = 1 and σ 2 = 2 ⁠ ) as well as six different domain widths but held constant the mean of the BM process ( ⁠ a ⁠ ), the birth–death process parameters, and the number of taxa on each simulation. For clarity, we considered a total of 12 different simulation scenarios: six different domain widths, each with the two different rate parameters. For each combination of σ 2 and width, we performed 1000 random simulations where tree topology, age, and outcome of the BM process varied. In total, we performed 12,000 simulations. For each simulation, we estimated the ancestral state ( ⁠ a ⁠ ) and rate ( ⁠ σ 2 ⁠ ) of the BM process using point likelihood and rase. The point likelihood function was extracted from the ace function in ape package ( Paradis et al. 2004) for R, while tree simulation under birth–death process was performed using the phytools package ( Revell 2013).

We calculated residual sum of squares (RSS) from linear regression between rase and point likelihood estimates for mean ( ⁠ a ⁠ ) and rate ( ⁠ σ 2 ⁠ ) for each of the 12 simulation scenarios. We expect that for small range widths, RSS will be small, and that it would increase with broader range widths. Overall, we found consistent results with our expectations ( Table 1, Fig. 4). The rate ( ⁠ σ 2 ⁠ ) parameter estimates from rase are generally lower than the estimates using points as tip-data. This difference increases with range width ( Fig. 4). This is consistent with our method since we are constraining the BM process to fall within the range thus, tip values that fall within the range but that are closer to the overall mean of the BM process will increase the likelihood of lower rates ( ⁠ σ 2 ⁠ ).

Validation results for the likelihood estimates using rase versus point likelihood. The a parameter corresponds to the ancestral state of the root, while the σ 2 parameter corresponds to the rate of change in both the horizontal and the vertical dimension, according to a BM. The point likelihood is evaluated using the centroids of enclosing squares of 0.1 (first two columns) and 0.6 range widths (last two columns), while the rase estimate is evaluated with the squares as input. For each square width, the distribution of 1000 simulations of BM among random phylogenetic trees is given. The upper row displays the maximum likelihood estimates (MLE) returned by rase across all simulations for both parameters dotted vertical lines correspond to the true parameters used to simulate the data (i.e., the state value of the root ( ⁠ a ⁠ ) = 0 and the rate of change ( ⁠ σ 2 ) = 2 ⁠ ). The lower row displays the MLE of point likelihood against the MLE of rase dashed gray line is a 1:1 correspondence. For additional simulation results, see Table 1.

Validation results for the likelihood estimates using rase versus point likelihood. The a parameter corresponds to the ancestral state of the root, while the σ 2 parameter corresponds to the rate of change in both the horizontal and the vertical dimension, according to a BM. The point likelihood is evaluated using the centroids of enclosing squares of 0.1 (first two columns) and 0.6 range widths (last two columns), while the rase estimate is evaluated with the squares as input. For each square width, the distribution of 1000 simulations of BM among random phylogenetic trees is given. The upper row displays the maximum likelihood estimates (MLE) returned by rase across all simulations for both parameters dotted vertical lines correspond to the true parameters used to simulate the data (i.e., the state value of the root ( ⁠ a ⁠ ) = 0 and the rate of change ( ⁠ σ 2 ) = 2 ⁠ ). The lower row displays the MLE of point likelihood against the MLE of rase dashed gray line is a 1:1 correspondence. For additional simulation results, see Table 1.

Validation results for likelihood estimates of ranges

σ 2 . Width size . RSS a . RSS σ 2 .
1 0.05 0.062 0.214
0.1 0.066 0.084
0.2 0.056 14.765
0.4 0.095 23.508
0.6 0.193 36.460
0.8 0.295 54.313
2 0.05 0.034 0.046
0.1 0.075 0.411
0.2 0.082 1.002
0.4 0.527 1.510
0.6 0.162 100.223
0.8 0.430 157.813
σ 2 . Width size . RSS a . RSS σ 2 .
1 0.05 0.062 0.214
0.1 0.066 0.084
0.2 0.056 14.765
0.4 0.095 23.508
0.6 0.193 36.460
0.8 0.295 54.313
2 0.05 0.034 0.046
0.1 0.075 0.411
0.2 0.082 1.002
0.4 0.527 1.510
0.6 0.162 100.223
0.8 0.430 157.813

Notes: Results from the validation procedure for rase for each of the 12 simulation scenarios were varied between two different rate parameters ( ⁠ σ 2 ⁠ ) to simulate the BM process and between different width sizes to input in rase as ranges. RSS is the residual sum of squares between the estimates of point likelihood against the estimates of rase for the BM process mean a and the rate σ 2 ⁠ .

Validation results for likelihood estimates of ranges

σ 2 . Width size . RSS a . RSS σ 2 .
1 0.05 0.062 0.214
0.1 0.066 0.084
0.2 0.056 14.765
0.4 0.095 23.508
0.6 0.193 36.460
0.8 0.295 54.313
2 0.05 0.034 0.046
0.1 0.075 0.411
0.2 0.082 1.002
0.4 0.527 1.510
0.6 0.162 100.223
0.8 0.430 157.813
σ 2 . Width size . RSS a . RSS σ 2 .
1 0.05 0.062 0.214
0.1 0.066 0.084
0.2 0.056 14.765
0.4 0.095 23.508
0.6 0.193 36.460
0.8 0.295 54.313
2 0.05 0.034 0.046
0.1 0.075 0.411
0.2 0.082 1.002
0.4 0.527 1.510
0.6 0.162 100.223
0.8 0.430 157.813

Notes: Results from the validation procedure for rase for each of the 12 simulation scenarios were varied between two different rate parameters ( ⁠ σ 2 ⁠ ) to simulate the BM process and between different width sizes to input in rase as ranges. RSS is the residual sum of squares between the estimates of point likelihood against the estimates of rase for the BM process mean a and the rate σ 2 ⁠ .

Climate Change And Species Distributions

Scientists have long pointed to physical changes in the Earth and its atmosphere, such as melting polar ice caps, sea level rise and violent storms, as indicators of global climate change.

But changes in climate can wreak havoc in more subtle ways, such as the loss of habitat for plant and animal species. In a series of talks at the Ecological Society of America (ESA) 93rd Annual Meeting, climate change scientists will discuss how temperature-induced habitat loss can spell disaster for many living things.

Climate models project that rising temperatures over time can lead to an increase in dry, desert-like conditions, which will affect not only the survivorship of particular species, but also the natural resources they have adapted to use in their natural environment. Species are thus forced to move elsewhere to find places to live and food to eat.

"Impacts on individual species indicate wider changes at the biome level that will potentially change conditions for many plant and animal species, in addition to ecosystem services to humans," says Patrick Gonzalez, a researcher at The Nature Conservancy and a member of the Intergovernmental Panel on Climate Change (IPCC).

One species whose habitat may be in danger is the Canada lynx, which is listed as threatened in the United States. The feline's main prey, the snowshoe hare, lives in deep snow cover in boreal forest. Because they rely so heavily on hares for food, lynx are adapted to live in areas with snow cover at least four months out of the year. The cats are so specialized to life on snow that their paws are much wider than is required to support their weight the large paws help them stalk hares over deep snow without falling in.

Gonzalez, who has worked with USDA Forest Service scientists to analyze lynx habitat, projects that a temperature increase of 2.5 to 4 degrees Celsius in the coming century across the U.S. and Canada&mdashthe range of warming under the scenarios reported by the IPCC&mdashmay diminish snow cover suitable for lynx by 10 to 20 percent and reduce boreal forest cover by half in the contiguous U.S. Together, these changes could shift lynx habitat northward and decrease the area of habitat in the lower 48 states by two-thirds. This potentially extensive loss of habitat signals serious changes in boreal and alpine ecosystems, says Gonzalez.

Climate change can result in animals and plants migrating northward to escape the heat, but in many cases suitable habitat becomes scarce or unavailable farther away from the species' natural range. The Propertius duskywing butterfly lives throughout the West Coast of the U.S., and during its caterpillar stage is specialized to live on oak trees. Shannon Pelini, a graduate student at the University of Notre Dame, conducted experiments revealing that warmer temperatures increased the survivorship and body size of caterpillars in its most northern habitats. A lack of oak trees in more northern climes, however, would preclude them from moving further north. The range shift of oak trees will happen much slower than the shift for the butterflies, leading to a contracted range, says Pelini.

As if the direct effects of rising temperatures weren't enough, climate change also has impacts that could make climate patterns less consistent over time. Michael Notaro, a scientist at the University of Wisconsin-Madison, used climate data from the past century to model vegetation changes over time. He found that large variability in climate causes an increasing number and intensity of fires and droughts, as well as extreme weather events that could kill long-lived trees and allow short-lived grasses to colonize the leftover space. His models predict that year-to-year variability in precipitation and temperature reduces the Earth's total vegetation cover, expanding its relative grass cover and diminishing its relative tree cover.

"The central U.S. is characterized by an ecotone that's the intersection of forest in the East and grassland in the West," says Notaro. "The border between these ecosystems is largely determined by climate variability and it is likely that climate change will shift the location of this and other ecological boundaries worldwide."

Gonzalez agrees that the research results presented at the ESA Annual Meeting indicate serious vulnerabilities of both individual species and global biomes to climate change.

"Climate change threatens to alter extensive areas of habitat," says Gonzalez. "Lynx is one species that is vulnerable, but the potential impacts of climate change on entire ecosystems are even more alarming."

The researchers will present their results in the following oral sessions:

  1. Shannon Pelini and Patrick Gonzalez - Climate Change: Range and Phenology, Monday, Aug. 4, Midwest Airlines Center
  2. Michael Notaro - Climate Change and Plants I, Tuesday, Aug. 5, Midwest Airlines Center

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Animal Diversity Web

Geographic Range

Inia geoffrensis (boto or Amazon River dolphin) can be found in the Amazon and Orinoco river basins and their main tributaries in Bolivia, Brazil, Colombia, Ecuador, Peru, and Venezuela. Their distribution covers approximately 7 million square kilometers and is limited mainly by marine waters, impassable rapids, waterfalls, and excessively shallow parts of the rivers. Three subspecies are recognized, with each subspecies occupying a different area of these river systems: I. g. geoffrensis occupies the central Amazon River basin I. g. humboldtiana resides in the Orinoco River basin and I. g. boliviensis can be found in the upper Madeira River, cut off from the Amazon River by impassable rapids. The current distribution of this species does not appear to differ significantly from its estimated distribution in the past. (Best and da Silva, 1993 da Silva, 2002)


Within the aforementioned river systems, botos can be found in nearly all types of microhabitats, including in main rivers, small channels, mouths of rivers, lakes, and just below waterfalls and rapids. The water level cycle exerts the strongest influence on habitat use by these dolphins during different parts of the year, both directly, by determining which areas are navigable, and indirectly, by dictating where fish are most abundant. During the dry season, Inia geoffrensis is most abundant in the main river channels because smaller water channels are too shallow and prey items are concentrated along the margins of these rivers. During the wet season, botos can easily navigate smaller tributaries, and individuals even venture into river floodplains and flooded forests. Males and females appear to have different habitat preferences, with males returning to main river channels while water levels are still rising and females and their calves continuing farther inland. Females and calves may remain in the floodplains longer for several reasons. The calmer waters could prevent young botos from getting drawn away by strong river currents, allowing them to rest, nurse, and catch fish in a calmer environment. They may also be at a lower risk of aggression from adult males and predation from other species. (Best and da Silva, 1993 da Silva, 2002 Martin and da Silva, 2004)

  • Habitat Regions
  • tropical
  • freshwater
  • Aquatic Biomes
  • lakes and ponds
  • rivers and streams
  • temporary pools

Physical Description

Inia geoffrensis is the largest of the river dolphins, with males achieving a length of up to 2.55 m (average: 2.32 m) and a mass of up to 207 kg (average: 154 kg). Females are smaller, getting up to 2.18 m (average: 2.00 m) in length and 154 kg (average: 100 kg) in mass. This difference in size marks this species as one of the most sexually dimorphic cetaceans, and having larger males makes it unique among river dolphins, where females are generally the larger sex. (Best and da Silva, 1993 da Silva, 2002 Martin and da Silva, 2006)

Body color varies with age, with young individuals being dark gray and adults possessing a solid or blotched pink hue, although males have been found to be significantly pinker than females. Some adults are darker on their dorsal surface than others, and it is thought that coloration may depend on temperature, clarity of water, and geographic location. (Best and da Silva, 1993 da Silva, 2002 Martin and da Silva, 2006)

Their bodies appear to be rather fat and heavy, but they are very flexible. None of their cervical vertebrae are fused, which allows them to move their heads in all directions. They possess broad triangular flukes and wide pectoral flippers, which sometimes possess a sixth phalanx. Their long humeri enable their flippers to move in a circular motion, giving them exceptional maneuverability when navigating through vegetation in flooded forests. However, these characters also restrict the overall speed of swimming. (Best and da Silva, 1989a Best and da Silva, 1993 da Silva, 2002)

The skulls of I. geoffrensis are less asymmetrical than other odontocetes, but torsion of the prominent rostra and mandibles is not uncommon. Their eyes are small, yet they seem to have good vision both above and underwater. They also have small, flaccid melons on their foreheads that can be shaped by muscular control when used for echolocation. (Best and da Silva, 1989a Best and da Silva, 1993 da Silva, 2002)

Botos are distinguished from other river dolphins by several characteristics. On top of their rostrums, they have diagnostic stiff vibrissae. They possess heterodont dentition as well, with their anterior teeth being conical and their posterior teeth having flanges on the lingual portions of the crowns. They also have long, low dorsal keels (from 30 to 61 cm in length) rather than the typical triangular dorsal fins of other dolphins. Inia geoffrensis can be distinguished from Sotalia fluviatilis (tucuxis), a sympatric species of river dolphin, by their color, the mobility of their head and flippers, and their diving behavior. (Best and da Silva, 1989a Best and da Silva, 1993 da Silva, 2002)

  • Other Physical Features
  • endothermic
  • homoiothermic
  • bilateral symmetry
  • Sexual Dimorphism
  • male larger
  • male more colorful
  • Range mass 98.5 to 185 kg 216.96 to 407.49 lb
  • Range length 1.24 to 2.55 m 4.07 to 8.37 ft


Little is known about the mating system of Inia geoffrensis . Before it was determined that this species exhibited sexual dimorphism, some workers postulated that botos were monogamous. However, males are now known to be larger than females, and very aggressive sexual behavior in males has been observed. Some authors have observed hostility between pink botos in the wild, while others have noted extremely aggressive activity during copulation in captivity. Males also have a higher degree of damaged fins, flukes, and blowholes due to biting and abrasion, in addition to more abundant scarring due to tooth-raking. This evidence suggests that there may be intense competition for access to females. This might indicate a polygynous mating system, but polyandry and promiscuity cannot be ruled out. (Best and da Silva, 1984 Caldwell, et al., 1989 Martin and da Silva, 2006 McGuire and Winemiller, 1998)

Courting and foreplay have been observed for botos in captivity. Males seem to initiate sexual activity by nibbling at the flippers or flukes of females, but if the females are not receptive, they might respond aggressively. This might not deter the males, however, who may still try and copulate with her. Copulation has been observed to be very frequent (one pair in captivity copulated 47 times in less than 3.5 hours) and to occur in three different positions: facing ventrally at right angles, lying parallel head-to-head, and head-to-tail. (Best and da Silva, 1989a Best and da Silva, 1993)

Male botos reach sexual maturity at about 2.0 m in length, while females attain sexual maturity when they are 1.60-1.75 m long. Reproduction is seasonal, with births occurring between May and July. This birthing period corresponds with peak water levels in rivers, and since females remain in flooded areas longer than males, this offers several advantages. As water levels begin to decrease, the density of prey items in flooded areas begins to increase due to loss of habitat, offering easy access to nourishment for fueling the high energy demands of giving birth and nursing. The gestation period is estimated to be about 11 months, and births in captivity took from 4-5 hours. Mothers give birth to single calves, and once the umbilical cords break, they help their calves to the surface for air. Inia geoffrensis calves are about 0.80 m long at birth and have been shown to grow about 0.21 m per year in captivity. Mothers lactate for well over a year, and several individuals are known to have been lactating and pregnant simultaneously. The interval between births is estimated as being between 15-36 months, and the calving period is 2-3 years. (Best and da Silva, 1984 Best and da Silva, 1989a Best and da Silva, 1993 Brownell, 1984 da Silva, 2002 Harrison and Brownell, 1971)

  • Key Reproductive Features
  • iteroparous
  • seasonal breeding
  • gonochoric/gonochoristic/dioecious (sexes separate)
  • sexual
  • viviparous
  • Breeding interval Botos breed once every year
  • Breeding season Botos breed between June and August
  • Range number of offspring 1 to 1
  • Average number of offspring 1 AnAge
  • Average gestation period 11 months
  • Average gestation period 287 days AnAge
  • Average weaning age 12 months
  • Average time to independence 2-3 years
  • Average age at sexual or reproductive maturity (female) 5 years
  • Average age at sexual or reproductive maturity (male) 5 years

The rather long periods of lactation and calving observed in Inia geoffrensis signify a strong mother-calf bond. Most boto pairs seen in the wild are mothers with their calves, and one pair in captivity was inseparable for three years. Some authors have suggested that this long period of parental care may be for learning and development of the young, as seen in bottlenosed dolphins (Tursiops truncatus). (Best and da Silva, 1989b Best and da Silva, 1993 McGuire and Winemiller, 1998)

  • Parental Investment
  • precocial
  • pre-hatching/birth
    • provisioning
      • female
      • female
      • provisioning
        • female
        • female
        • provisioning
          • female
          • female


          The longevity of Inia geoffrensis in the wild is unknown, but healthy individuals in captivity can live from 10-26 years. However, the average longevity of captive botos has been reported to be only about 33 months. (Best and da Silva, 1993 Caldwell, et al., 1989)

          • Range lifespan
            Status: captivity 26 (high) years
          • Average lifespan
            Status: captivity 3 years
          • Average lifespan
            Status: wild 30.0 years Max Planck Institute for Demographic Research
          • Average lifespan
            Status: captivity 18.0 years Max Planck Institute for Demographic Research


          Inia geoffrensis is typically solitary and is rarely seen in tight groups of more than three individuals (pairs are usually mothers with their calves). However, loose aggregations associated with either feeding or mating do occur periodically. Botos do not appear to establish a social hierarchy through aggression in captivity, but violent acts are not uncommon and have even resulted in the death of some individuals. They have also been known to react protectively to individuals that have been captured or injured. They are active during both day and nighttime hours, and they are known to associate with other animals, including tucuxis (Sotalia fluviatilis) and giant otters (Pteronura brasiliensis), when pursuing prey items. (Best and da Silva, 1989b Best and da Silva, 1993 Caldwell, et al., 1989 da Silva, 2002)

          Botos are slower swimmers than most other dolphins (normally about 1.5-3.2 km/hr), but they are capable of speed bursts (14-22 km/hr). They are often found above moderate river rapids, indicating that they are capable of sustaining strong swimming for a long period of time. They do not dive very deep, and they rarely raise their flukes out of the water. When they come to the surface, the tips of their rostrums, their melons, and their dorsal keels emerge simultaneously, and they have been observed rolling, waving flippers, and lob-tailing. (Best and da Silva, 1989a Best and da Silva, 1993 da Silva, 2002)

          Botos are quite playful and curious in the wild. It is not unusual for them to rub against canoes and grasp canoe paddles of fishermen in the rivers, and they have been observed pulling grass under water, throwing sticks, and playing with logs and smaller animals (including fish and turtles). In captivity, I. geoffrensis is less timid than bottlenose dolphins (Tursiops truncatus), yet they have been more difficult to train than most other dolphins. (Best and da Silva, 1989a Best and da Silva, 1993 da Silva, 2002)

          Home Range

          Apparently occupying the same area for more than a year, most botos are rather sedentary. They display no obvious defense of home ranges, but if they do, the ranges are likely large and overlapping. This species does undertake seasonal migrations correlated with water level and fish abundance, but these shifts are minor excursions from the area they occupy during the rest of the year. (Best and da Silva, 1989a Best and da Silva, 1989b Best and da Silva, 1993)

          Communication and Perception

          Inia geoffrensis uses echolocation to catch prey, navigate, and perceive its environment. The frequency of these clicks does not appear to be significantly different from that of Tursiops truncatus, with 45 kHz being a dominant frequency. These clicks, which range from 16-170 kHz, are also used to communicate between individuals. Botos in captivity have been shown to make 10 distinct calls, including echolocation-like burst click runs, barks, whimpers, squeaks, and cracks. They also appear to use open mouths when communicating, as suggested by some tooth rake scars seen on all individuals. (Best and da Silva, 1993 Martin and da Silva, 2006)

          • Communication Channels
          • visual
          • tactile
          • acoustic
          • Perception Channels
          • visual
          • tactile
          • acoustic
          • echolocation
          • chemical

          Food Habits

          A single boto’s stomach may contain more species of fish than the total number of prey species seen in other dolphins. Their very diverse diet includes at least 43 different species of fish in 19 families, with prey items ranging in size from 5-80 cm (average: 20 cm). They apparently prefer fish from the families Sciaenidae (drums or croakers), Cichlidae (cichlids), Characidae (characins and tetras), and Serrasalmidae (piranhas), but their heterodont dentition allows them to crush armored prey as well, including river turtles (Podocnemis sextuberculata) and crabs (Poppiana argentiniana). Their diet is most diverse during the wet season, when the fish spread out into the floodplain and are more difficult to catch, and becomes more selective during the dry season when fish densities are higher. (Best and da Silva, 1989a Best and da Silva, 1989b Best and da Silva, 1993 da Silva, 2002 McGuire and Winemiller, 1998)

          Botos are usually solitary feeders, most active between 0600-0900 hours and 1500-1600 hours and consuming about 2.5% of their body weight every day. They often hang out near waterfalls and river mouths where river currents disrupt schools of fish and make them easier to catch. They also make use of disturbances made by canoes to catch disoriented prey. Sometimes they even form loose aggregations with Sotalia fluviatilis (tucuxis) and Pteronura brasiliensis (giant otters) to hunt fish in a coordinated fashion, herding and attacking shoals of fish together. Apparently, there is little competition between these species, as each prefers different types of fish. In addition, food sharing has actually been observed between botos in captivity. (Best and da Silva, 1989a Best and da Silva, 1989b Best and da Silva, 1993 da Silva, 2002)

          • Primary Diet
          • carnivore
            • piscivore
            • eats non-insect arthropods
            • Animal Foods
            • reptiles
            • fish
            • aquatic crustaceans


            There are no known records of a natural predator of botos, but black caimans (Melanosuchus niger), bull sharks (Carcharhinus leucas), anacondas (Eunectes), and jaguars (Panthera onca) are potentially capable of handling them. Some botos also possess crescent-shaped wounds that have been attributed to catfish of the families Cetopsidae and Trichomycteridae. (Best and da Silva, 1989a Best and da Silva, 1993)

            Ecosystem Roles

            The diverse diet of Inia geoffrensis causes it to have an impact on a number of different species. Of its prey items, botos may have the largest effect on the family Sciaenidae, since they seem to prefer these species. They have also formed mutualistic relationships with Sotalia fluviatilis (tucuxis) and Pteronura brasiliensis (giant otters) by forming coordinated hunting groups with them. Botos have several parasitic trematodes and nematodes, many of which are host-specific. If the crescent-shaped wounds seen on botos can indeed be attributed to catfish from the family Trichomycteridae, then they have an ectoparasite as well. (Best and da Silva, 1989a Best and da Silva, 1993)

            • Pholeter gastrophilus
            • Hunterotrema caballeroi
            • Hunterotrema macrossoma
            • Halocerus
            • Preitrachelius insignis
            • Trichomycteridae

            Economic Importance for Humans: Positive

            There is little direct hunting of botos by native people, although Portuguese colonists may have hunted them to obtain oil for lamps. If botos are found dead, native people may use the fat as a cure for asthma and the oil to treat rheumatic pains or even infections in their cattle. They sometimes use the eyes, genitalia, and teeth as love charms and amulets as well. However, they never use the meat or skin. In addition, fishermen have been known to use botos to lead them to schools of fish. (Best and da Silva, 1989a Best and da Silva, 1989b Best and da Silva, 1993)

            Economic Importance for Humans: Negative

            While there is little overlap between the fish that Inia geoffrensis prefers and the species that fisheries seek, botos have been known to tear fish from nets, causing damage to expensive fishing gear and, in some cases, a drastic reduction in fish catch. (Best and da Silva, 1989b Culik, 2000)

            Conservation Status

            Human activities are exerting a lot of pressure on Inia geoffrensis populations. There have been many negative interactions with fisheries. As fishing technologies have improved, the incidental catching of botos has greatly increased. They have also been harpooned, shot, and poisoned for stealing fish out of nets and damaging the fishing equipment. A greater human demand for fish decreases the abundance of potential prey items for botos as well. (Best and da Silva, 1993 Culik, 2000 da Silva, 2002 Vidal, 1993 Best and da Silva, 1989b Best and da Silva, 1993 Culik, 2000 da Silva, 2002 Vidal, 1993)

            Hydroelectric dams have been problematic in several ways. They decrease the available food supply by preventing various species of fish from migrating downstream, while also decreasing the oxygen level downstream. Dams split up populations of I. geoffrensis , potentially reducing gene pools in these subpopulations to levels where they may not have enough genetic diversity to survive, thereby increasing the risk of extinction. (Best and da Silva, 1989b Best and da Silva, 1993 da Silva, 2002 Vidal, 1993)

            Deforestation for agriculture in floodplains reduces fish populations by eliminating the fruits and seeds in the flooded forests that they feed upon, thus decreasing the potential food supply for botos. The rivers inhabited by I. geoffrensis are polluted by pesticides from agricultural fields and heavy metals (including mercury) from gold refining, which negatively affect both botos and their prey items. (Best and da Silva, 1989b Best and da Silva, 1993 Culik, 2000 da Silva, 2002 Vidal, 1993)

            Inia geoffrensis is classified as vulnerable by the IUCN. They have traditionally been difficult to keep in captivity, due to aggression and fairly short longevity. If boto numbers begin to dwindle to dangerously low levels in the wild, it would be alarming because populations may not be able to be maintained for long in captivity. (Caldwell, et al., 1989 da Silva, 2002)

            • IUCN Red List No special status
              More information
            • IUCN Red List No special status
              More information
            • US Federal List No special status
            • CITES No special status

            Other Comments

            Boto is the internationally-recognized common name of Inia geoffrensis , but other common names include the Amazon River dolphin, bufeo, bufeo colorado, tonina, delfin rosado, and pink dolphin. (da Silva, 2002)

            Botos are part of the folklore of Amazonian people. There are several legends giving botos supernatural powers, which is why they are typically respected and protected. Some myths tell of botos turning into beautiful men or women during the night and luring members of the opposite sex down into the river, never to return. Another myth speaks of the spirits of drowned people entering the bodies of botos. (Best and da Silva, 1993 da Silva, 2002)

            There is not consensus as to whether the ancestors of I. geoffrensis entered the Amazon River basin from the Pacific Ocean before the Andean orogeny 15 million years ago or from the Atlantic Ocean much more recently. (Best and da Silva, 1993)


            Tanya Dewey (editor), Animal Diversity Web.

            Ryan Bebej (author), University of Michigan-Ann Arbor, Phil Myers (editor, instructor), Museum of Zoology, University of Michigan-Ann Arbor.


            living in the southern part of the New World. In other words, Central and South America.

            uses sound to communicate

            having body symmetry such that the animal can be divided in one plane into two mirror-image halves. Animals with bilateral symmetry have dorsal and ventral sides, as well as anterior and posterior ends. Synapomorphy of the Bilateria.

            an animal that mainly eats meat

            uses smells or other chemicals to communicate

            a substance used for the diagnosis, cure, mitigation, treatment, or prevention of disease

            The process by which an animal locates itself with respect to other animals and objects by emitting sound waves and sensing the pattern of the reflected sound waves.

            animals that use metabolically generated heat to regulate body temperature independently of ambient temperature. Endothermy is a synapomorphy of the Mammalia, although it may have arisen in a (now extinct) synapsid ancestor the fossil record does not distinguish these possibilities. Convergent in birds.

            mainly lives in water that is not salty.

            offspring are produced in more than one group (litters, clutches, etc.) and across multiple seasons (or other periods hospitable to reproduction). Iteroparous animals must, by definition, survive over multiple seasons (or periodic condition changes).

            makes seasonal movements between breeding and wintering grounds

            having the capacity to move from one place to another.

            the area in which the animal is naturally found, the region in which it is endemic.

            an animal that mainly eats fish

            breeding is confined to a particular season

            reproduction that includes combining the genetic contribution of two individuals, a male and a female

            uses touch to communicate

            the region of the earth that surrounds the equator, from 23.5 degrees north to 23.5 degrees south.

            uses sight to communicate

            reproduction in which fertilization and development take place within the female body and the developing embryo derives nourishment from the female.

            young are relatively well-developed when born


            2004. "Boto (Amazon River Dolphin), Inia geoffrensis fact sheet" (On-line). American Cetacean Society. Accessed January 27, 2006 at

            Best, R., V. da Silva. 1989. Amazon River Dolphin, Boto, Inia geoffrensis (de Blainville, 1817). Pp. 1-23 in S Ridgway, R Harrison, eds. Handbook of Marine Mammals: River Dolphins and the Larger Toothed Whales . London: Academic Press.

            Best, R., V. da Silva. 1989. Biology, Status and Conservation of Inia geoffrensis in the Amazon and Orinoco River Basins. International Union for Conservation of Nature and Natural Resources (IUCN), Species Survival Commission , Occasional Paper 3: 22-34.

            Best, R., V. da Silva. 1993. Inia geoffrensis. Mammalian Species , 426: 1-8.

            Best, R., V. da Silva. 1984. Preliminary Analysis of Reproductive Parameters of the Boutu, Inia geoffrensis, and the Tucuxi, Sotalia fluviatilis, in the Amazon River System. Report of the International Whaling Commission , Special Issue 6: 361-369.

            Brownell, R. 1984. Review of Reproduction in Platanistid Dolphins. Report of the International Whaling Commission , Special Issue 6: 149-158.

            Caldwell, M., D. Caldwell, R. Brill. 1989. Inia geoffrensis in Captivity in the United States. International Union for Conservation of Nature and Natural Resources (IUCN), Species Survival Commission , Occasional Paper 3: 35-41.

            Culik, B. 2000. "Inia geoffrensis (de Blainville, 1817)" (On-line). Convention on Migratory Species. Accessed January 27, 2006 at

            Harrison, R., R. Brownell. 1971. The Gonads of the South American Dolphins, Inia geoffrensis, Pontoporia blainvillei, and Sotalia fluviatilis . Journal of Mammalogy , 52: 413-419.

            Martin, A., V. da Silva. 2004. River dolphins and flooded forest: seasonal habitat use and sexual segregation of botos (Inia geoffrensis) in an extreme cetacean environment. Journal of the Zoological Society of London , 263: 295-305.

            Martin, A., V. da Silva. 2006. Sexual dimorphism and body scarring in the boto (Amazon River dolphin) Inia geoffrensis . Marine Mammal Science , 22: 25-33.

            McGuire, T., K. Winemiller. 1998. Occurrence Patterns, Habitat Associations, and Potential Prey of the River Dolphin, Inia geoffrensis, in the Cinaruco River, Venezuela. Biotropica , 30: 625-638.

            Vidal, O. 1993. Aquatic Mammal Conservation in Latin America: Problems and Perspectives. Conservation Biology , 7: 788-795.

            da Silva, V. 2002. Amazon River Dolphin. Pp. 18-20 in W Perrin, B Würsig, J Thewissen, eds. Encyclopedia of Marine Mammals . San Diego: Academic Press.


            Our faculty are drawn from several departments on the main campus𠅋iology, Mathematics, and STIA (Science, Technology, and International Affairs)𠅊nd conduct research on a range of topics. They have extensive experience catalyzing student interest in research and guiding students to becoming independent scientists.

            Ali Arab: Professor Arab’s research focuses on the statistical modeling of complex data including spatial data, time series data, Spatio-temporal data, remote sensing data and satellite imagery, and complex count data (e.g., data with excessive zero counts). The application areas of Professor Arab’s research include environmental studies, ecology, epidemiology, and human rights. Current research topics include: 1) modeling long-term spatial patterns and temporal trends in the spring arrival of migratory birds in North America 2) modeling spatial and temporal abundance patterns of fish species in large rivers (with application to data from a monitoring program in the Missouri River) and 3) modeling satellite imagery data with applications to environmental studies (e.g., detecting deforestation, receding water levels in lakes and rivers, and land use change) and human rights studies (detecting mass graves, forced evictions and relocations, and assessing damage to civilian infrastructures during conflicts).

            Peter Armbruster: The Armbruster lab combines studies of ecology, quantitative genetics, genomics, and molecular physiology to elucidate processes of phenotypic evolution in natural populations and the molecular bases of adaptation. Container-breeding mosquitoes are used as a model system, with a focus on the invasive and medically important mosquito Aedes albopictus. In addition to addressing fundamental hypotheses in ecology and evolution, much of the work is directly relevant to public policy. For example, the Armbruster lab recently participated in collaborative ecological studies examining the causes of variation in vector abundance among socio-economically diverse neighborhoods in Washington, DC. These studies have applications in vector control and increase our understanding of the potential for disease outbreaks. Another major area of research in the Armbruster lab has been the molecular physiology of climatic adaptation in Ae. albopictus, with a specific emphasis on photoperiodic diapause. These studies have important implications for understanding invasion and range expansion and for anticipating biological responses to climate change.

            Shweta Bansal: The Bansal lab focuses on the mathematical modeling of host contact patterns and pathogen dynamics to answer fundamental questions in infectious disease ecology. Current research topics include: 1) disease transmission dynamics under disruptions to the host population contact structure (e.g., translocations in desert tortoises) and 2) the tension in complex societies, such as bottlenose dolphins and carpenter ants, between the benefit of cooperation versus the cost of disease transmission.

            Edd Barrows: The work of the Barrows lab is primarily a long-term study identifying the arthropod species of Dyke Marsh Wildlife Preserve (DMWP) in Virginia. This preserve is part of the George Washington Memorial Parkway (GWMP) administered by the U.S. National Park Service. The main goals of this research are to document the arthropod diversity in DMWP, including habitat usage, abundances, and aspects of phenology and life history. This study provides baseline information to the U.S. National Park Service for use in documenting and tracking the preserve’s biological diversity in view of its environmental challenges, including invasive species, erosion, pollution, and climate change. Work to date has contributed to the passing of a 2009 U.S. Congressional bill (House Resolution 701), which recognizes DMWP as 𠇊 unique and precious ecosystem.” 

            Hans Engler: Prof. Engler’s research is in applied mathematics and computational statistics, with scientific questions coming from climate science, transportation modeling, and remote sensing, among other areas. He employs methodologies from applied mathematics to study conceptual models for parts of the climate system and uses computational approaches for the analysis of large datasets that arise through observation or large-scale simulations. Current projects focus on spatio-temporal patterns in transportation networks (bikeshare systems) and in planetary climate models with latitudinal or longitudinal structure, and also conceptual models for glaciation cycles in paleoclimatology.

            Mark Giordano: The Giordano lab pursues research examining the interaction between environmental science and policy, primarily in Asia and Africa. Current projects measure the impact of water saving technologies on actual water use in India and Pakistan, examine how internationally funded environmental impact assessments are used in dam construction decisions in the Mekong region, and explore if unilateral changes in groundwater management and use can reduce tensions over shared water in the Aral Sea Basin. (Prof. Giordano will be unavailable for mentoring students in the summer of 2016.)

            Matthew Hamilton: The Hamilton lab focuses on evolutionary and conservation genetics and pursues both empirical and computational research. Current research topics include comparison of nucleotide substitution rate variation in annual and perennial plants, estimation of genetic effective population size (Ne) in age-structured and spatially structured populations, genetic diversity in foundation plant species and their consumer insect species communities, and the action of genetic drift and natural selection in response to ecological heterogeneity. Most projects in the lab are testing fundamental conceptual hypotheses that can be applied to biological habitat or species conservation and management.

            Sarah Johnson: The Johnson lab pursues research at the nexus of planetary science and geo-biology, striving to understand how Mars, a planet once very similar to Earth, could have evolved in such a dramatically different way and searching for evidence of habitable or once-habitable environments there. Current projects use systems modeling, with particular emphasis on the role of sulfur in the evolution of planetary environments, as well as the power of novel techniques for genetic and bio-signature analysis.

            Janet Mann: Professor Mann’s long-term study of wild bottlenose dolphins (field site in Shark Bay, Australia, a UNESCO World Heritage site see affords students access to one of the richest empirical datasets in existence for any mammal. The Shark Bay Dolphin Research Project (SBDRP) began in 1984, and Mann has curated the long-term project data since the early 1990s, including development and design of a web-based database in collaboration with Dr. Lisa Singh (Associate Professor, Computer Science). The Mann lab studies a range of questions concerning bottlenose dolphin development, life history, behavior, communication, social relationships, habitat, reproduction, diet, genetics, mortality, predators, prey, human impacts, and conservation. In addition to the SBDRP, Professor Mann has teamed up with Dr. Eric Patterson and Professor Bansal in studying bottlenose dolphins in the Potomac River and Chesapeake. This new study focuses on behavior patterns associated with disease transmission. We conduct transect surveys of the population in the lower Potomac and Chesapeake. Students will have an opportunity to learn field research techniques and work with our burgeoning dataset.

            Leslie Ries: The Ries lab focuses on large-scale drivers of species distributions and community change, with a particular focus on how climate and land-use changes impact butterflies in North America. The majority of research draws on large-scale data sets collected by citizen scientists to examine range shifts, population trends, and interactions between resources, consumers, and predators. The main focus of research is the monarch butterfly, the most intensively monitored species for which we have large-scale data sets not only on adult abundance but also on larval development, natural enemy pressure, and movement. Another major focus is on community composition shifts, using species-level traits to understand differential responses to the same driver. Much of this research uses physiological responses to temperature to understand observed shifts in abundance or distribution. Laboratory measurements of physiological responses in a number of butterfly species can be used to make predictions about distributional shifts, which can then be tested using continent-wide monitoring data on butterflies.

            Mahlet Tadesse: Professor Tadesse’s research focuses on the development of statistical methods for the analysis of high-dimensional data with an emphasis on “-omics” applications (genomics, proteomics, metabolomics). Over the past few years, she has also been building collaborative efforts focused on climate change and ecology. In particular, she is working on methods for modeling species distributions in highly biodiverse ecosystems, taking species interactions into account. The ability to predict the evolving structure of such ecosystems in the face of climate change (e.g., increased temperatures, alteration of rainfall patterns) and anthropogenic pressure (e.g., mining, timber logging, fuel-wood exploitation, land conversion) is crucial for designing effective conservation and sustainable management policies and programs. (Prof. Tadesse will be unavailable for mentoring students in the summer of 2016.)

            Martha Weiss: Research in the Weiss lab addresses questions of evolutionary ecology, with a focus on the role of behavior, by both plants and insects, in mediating interactions amongst the two groups of organisms. Current projects include investigations of determinants of diet breadth in herbivorous insects, the role of learning in larval and adult Lepidoptera, the ecological context of defecation behaviors, and chemical and visual components of ant-mimicry.

            Gina Wimp: The Wimp lab seeks to understand the forces, both natural and anthropogenic, that shape ecological interactions in salt marsh communities. Specifically, the research examines the impact of predators, nutrient runoff, and habitat fragmentation on salt marsh population, community, and ecosystem dynamics. For example, habitat fragmentation is one of the primary factors leading to species extinctions worldwide, and predators may be especially susceptible to fragmentation relative to lower trophic levels. Previous research in the Wimp Lab has demonstrated that different predator functional groups𠅊nd even different species within a functional group𠅊re differentially susceptible to habitat edges and fragmentation. It therefore becomes important to examine the way in which the loss of different predators and predator functional groups may influence important ecosystem processes, such as prey suppression. 

            Watch the video: Webinar: Species Distribution Modeling and Scenario Planning (September 2022).


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