app.py

from transformers import pipeline
import streamlit as st

summarizer = pipeline("summarization", model="facebook/bart-large-cnn")

ARTICLE = """ 
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A functional defnition
to distinguish ponds from lakes
and wetlands
David C. Richardson 1,19*, MeredithA. Holgerson 2,19, Matthew J. Farragher 3
,
Kathryn K. Hofman 4
, Katelyn B. S. King 5
, María B.Alfonso 6
, Mikkel R.Andersen 7
, Kendra Spence Cheruveil 8
, KristenA. Coleman9
, Mary Jade Farruggia 10,
Rocio Luz Fernandez 11, Kelly L. Hondula 12, GregorioA. López Moreira Mazacotte 13, Katherine Paul1
, Benjamin L. Peierls 14, Joseph S. Rabaey 15, Steven Sadro 10,
María Laura Sánchez 16, Robyn L. Smyth 17 & Jon N. Sweetman 18
Ponds are often identifed by their small size and shallow depths, but the lack of a universal evidencebased defnition hampers science and weakens legal protection. Here, we compile existing pond
defnitions, compare ecosystem metrics (e.g., metabolism, nutrient concentrations, and gas fuxes)
among ponds, wetlands, and lakes, and propose an evidence-based pond defnition. Compiled
defnitions often mentioned surface area and depth, but were largely qualitative and variable.
Government legislation rarely defned ponds, despite commonly using the term. Ponds, as defned in
published studies, varied in origin and hydroperiod and were often distinct from lakes and wetlands
in water chemistry. We also compared how ecosystem metrics related to three variables often
seen in waterbody defnitions: waterbody size, maximum depth, and emergent vegetation cover.
Most ecosystem metrics (e.g., water chemistry, gas fuxes, and metabolism) exhibited nonlinear
relationships with these variables, with average threshold changes at 3.7± 1.8 ha (median: 1.5 ha)
in surface area, 5.8 ± 2.5 m (median: 5.2 m) in depth, and 13.4 ± 6.3% (median: 8.2%) emergent
vegetation cover. We use this evidence and prior defnitions to defne ponds as waterbodies that are
small (< 5 ha), shallow (< 5 m), with< 30% emergent vegetation and we highlight areas for further
study near these boundaries. This defnition will inform the science, policy, and management of
globally abundant and ecologically signifcant pond ecosystems.
Lentic (still) waterbodies have long been placed into categories to improve our understanding of aquatic ecosystems, aid science communication, and facilitate management decisions1,2
. For instance, lentic ecosystems
have been sorted into discrete categories by size or depth3,4
, trophic status5
, and mixing regime6,7
. Ofen, lentic
waterbodies are categorized by diferent ecosystem types, such as lakes, ponds, and wetlands (Fig. 1). Categorizing
OPEN
1
Biology Department, State University of New York at New Paltz, New Paltz, NY, USA. 2
Department of Ecology
and Evolutionary Biology, Cornell University, Ithaca, NY, USA. 3
School of Biology and Ecology, Climate Change
Institute, University of Maine, Orono, ME, USA. 4
Departments of Biology and Environmental Studies, St. Olaf
College, Northfeld, MN, USA. 5
Department of Fisheries and Wildlife, Michigan State University, East Lansing,
MI, USA. 6
Instituto Argentino de Oceanografía (IADO), Universidad Nacional del Sur (UNS)-CONICET, Florida
8000, Complejo CCT CONICET Bahía Blanca, Edifcio E1, B8000BFW Bahía Blanca, Argentina. 7
Centre for
Freshwater and Environmental Studies, Dundalk Institute of Technology, Dundalk, Ireland. 8
Department
of Fisheries and Wildlife and the Lyman Briggs College, Michigan State University, East Lansing, MI,
USA. 9
Department of Geography, York University, Toronto, ON, Canada. 10Department of Environmental Science
and Policy, University of California, Davis, Davis, CA, USA. 11National Scientifc and Technical Research Council
(CONICET), Cordoba, Argentina. 12Battelle, National Ecological Observatory Network (NEON), Boulder, CO,
USA. 13Department of Ecohydrology and Biogeochemistry, Leibniz-Institute of Freshwater Ecology and Inland
Fisheries (IGB), Müggelseedamm 310, 12587 Berlin, Germany. 14Lakes Environmental Association, Bridgton,
ME, USA. 15Department of Ecology, Evolution, and Behavior, University of Minnesota-Twin Cities, St. Paul, MN,
USA. 16CONICET-Universidad de Buenos Aires, Buenos Aires, Argentina. 17Environmental and Urban Studies, Bard
College, Annandale‑on‑Hudson, NY, USA. 18Department of Ecosystem Science and Management, Penn State
University, University College, PA, USA. 19These authors contributed equally: David C. Richardson and Meredith
A. Holgerson. *email: richardsond@newpaltz.edu
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waterbodies using physical and biological characteristics facilitates generalizations and decision making, but
categories may not always align with ecological inferences2
.
Categorizing small waterbodies is particularly challenging. Te majority of the world’s lentic waterbodies are
small: over 95% are less than 10 ha (0.1 km2
)3,8
. Across the history of limnology, small and shallow waterbodies
are widely referred to as ponds8–11, yet pond defnitions difer across the globe and are not based on scientifc
evidence. Te lack of a universal, scientifcally-based pond defnition that diferentiates ponds from other lentic
waterbodies hampers science, policy, and management, and creates confusion. For example, the number of lakes
at regional and global scales is contested and depends on the lower bounds of what is considered a “lake”3
. In the
United States (US), Wisconsin and Minnesota debated which state had the most lakes based on Wisconsin including smaller waterbodies<0.1 ha as lakes, whereas Minnesota considered these small waterbodies as wetlands12–14.
Tese defnitions afect which waterbodies are included in monitoring programs, and how ecosystem properties
are regionally or globally upscaled. For instance, ponds are ofen grouped with lakes when upscaling greenhouse
gas emissions4
, but there is concern over double counting ponds as wetlands and thus overestimating aquatic
emissions15. Tese examples emphasize the importance of waterbody categorization for science, management,
and legal protection.
Distinguishing ponds from lakes and wetlands is common among the public, scientists, and managers. Yet,
while scientists speculate that ponds may be fundamentally diferent in ecosystem structure and function compared to lakes and wetlands16, these data have not been collected and analyzed with the explicit purpose of defning boundaries between aquatic ecosystem types. Terefore, our study had four objectives: (1) compile current
pond defnitions from scientists and policy makers, (2) determine if ponds, lakes, and wetlands, as defned by
researchers, difer in ecosystem structure, (3) use ecosystem structure and function metrics to identify if there
are boundaries between ponds and lakes or wetlands, and (4) develop a scientifcally based pond defnition based
on ecosystem function and prior defnitions. To address our objectives, we compiled existing pond defnitions
from scientifc literature and evaluated legislative defnitions of ponds, wetlands, and lakes. We also assembled
a large dataset of pond characteristics and ecosystem function from a global literature survey and compared
ecosystem structural and functional metrics among ponds, wetlands, and lakes. Finally, we propose an evidencebased pond defnition.
Results and discussion
Current scientifc defnitions of ponds. We compiled existing scientifc defnitions of ponds by conducting a backwards and forwards search of papers referenced in or subsequently referencing three seminal
pond papers8,17,18 (see “Methods”). We ultimately compiled 54 pond defnitions from scientifc literature (data
available19). Te variables most ofen included in defnitions were surface area (91% of defnitions), depth (48%),
permanence (48%), origin (i.e., natural or human-made; 33%), and standing water (33%; Fig. 2a). When surface
area or depth were included in defnitions, they were ofen mentioned qualitatively (e.g., “small” and “shallow”).
Of the 61% of defnitions that included a maximum pond surface area, the range was 0.1 to 100 ha, the median
was 2 ha, and all but two defnitions were≤10 ha (Fig. 2b). For depth, only 17% of studies provided a maximum
depth cutof, which ranged 2 to 8 m (Fig. 2c). Of the 26 defnitions mentioning permanence, 22 stated that ponds
could be temporary or permanent and only three indicated that ponds are exclusively permanent waterbodies.
Of the 18 defnitions mentioning origin, 17 mentioned that ponds could be natural or human-made with the
remaining study indicating ponds can have diverse origins.
Other important factors included in defnitions related to morphometry. For example, 30% of defnitions
mentioned the potential for plants to colonize the entire basin, which relates to high light penetration (mentioned
in 11% of defnitions) and/or shallow depths. For example, Wetzel11 defnes ponds as having enough light penetration that macrophyte photosynthesis can occur over the entire waterbody. As such, these conditions may be
Figure 1. We call lentic waterbodies by a variety of names in the English language including ponds, lakes,
wetlands, reservoirs, oxbows, prairie potholes, vernal pools, lagoons, dams, puddles, and shallow lakes. Tese
names may or may not correspond to ecological and systematic diferences. Generally, laypeople and experts, as
individuals, will quickly diferentiate among broad categories of ponds, lakes, and wetlands; however, individuals
may respond in diferent ways depending on their background and experiences. We present three diferent
images of waterbodies that could each be categorized as lake, pond, or wetland using objective (e.g., morphology
or vegetative cover) or more subjective criteria keeping cognizant of the complexity within and potential overlap
among waterbody types.
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comparable to the littoral region of lakes (11% of defnitions). Lastly, 7% of pond defnitions mentioned mixing
versus stratifcation, whereby ponds mix more than lakes20 yet less than shallow lakes due to a smaller fetch16.
To assess if there was agreement in pond defnitions among papers, we examined the number of times each
defnition was cited. Across the 54 defnitions, there were 89 citations of 48 unique papers. Ultimately, most
papers (75%) were only cited only once, indicating no consensus in pond defnition. Te most cited paper was
Biggs et al.21, which accounted for 15% of citations. Te next two most cited papers were Oertli et al.17 and Sondergaard et al.18, which were seminal papers included in our backwards-forwards search, and each comprised
8% of citations.
International defnitions. At an international level, there is no consensus on how to discriminate among
ponds, lakes, and wetlands. In North America, wetlands are generally considered to be shallow:<2 m in Canada22
and<2.5 m in the US23, which diferentiates them from lakes. Some nations, such as Australia, South Korea, and
Uganda, explicitly include ponds and lakes in federal wetland defnitions24 (see also22). Te inclusion of ponds
and some lakes within wetland defnitions ofen stems from the Ramsar Convention, an international body
interested in global wetland conservation that has been signed by 172 countries representing 6 continents as
of 202125. Te Ramsar Convention defned wetlands as “areas of marsh, fen, peatland, or water” across marine,
brackish, and freshwater with varying degrees of permanence and natural or artifcial states with a maximum
depth of 6 m26, which overlaps depths found in many defnitions of ponds and shallow lakes. In other countries,
ponds are included in lake defnitions under federal conservation laws. For example, in the Danish “nature
protection” law §3, lakes are defned as waterbodies with a surface area of>100 m2
. As 98% of Danish ‘lakes’ are
smaller than 1 ha27, this law protects many small waterbodies that may be considered ponds elsewhere. Still other
agencies have only qualitative pond defnitions: the European Commission simply defnes ponds as “relatively
shallow” and may also be called “pool, tarn, mere, or small lake,” a defnition also used by the International
Union for Conservation of Nature28,29. Tese examples underscore that waterbody defnitions vary globally, are
generally qualitative, and are rarely based on scientifc evidence relating to ecosystem structure or function. Te
defnitions possibly derive from diferent management, protection, and monitoring strategies; for instance, the
European Union’s Water Framework Directive excludes waterbodies<50 ha (0.5 km2
) in size from monitoring30.
Figure 2. Summary of “pond” defnitions from scientifc literature including (a) presence of various
morphological, biological, and physical characteristics in the defnition as blue bars (n=54 defnitions total).
Bold black lines indicate the number of defnitions with surface area and depth values. Histograms of the upper
limits from “pond” defnitions for (b) surface area and (c) maximum depth.
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Current U.S. Federal and State defnitions. In the US, waterbody defnitions vary among federal agencies, with implications for both legal protection and monitoring. Te US Environmental Protection Agency
(EPA) and the US Army Corps of Engineers (ACE) defne wetlands based on saturated soils and hydrophytic
vegetation, which has the potential to include ponds within the category of wetlands. Conversely, the US Fish
and Wildlife Service (USFWS) distinguishes among wetlands and lakes based on surface area, depth, and emergent vegetation31. Lakes are≥8 ha or if smaller, they must be≥2.5 m in maximum depth. In contrast, wetlands
are are dominated by>30% emergent plant cover; if there is less, the site may still be a wetland if<2.5 m deep
and<8 ha in size. Terefore, ponds are ofen considered by USFWS to be wetlands, but this is not always the case:
ponds have been used as an example of a waterbody that can be classifed as lake, wetland, or both32. Te lack of
an explicit, unifed, and scientifcally based pond defnitions across three federal agencies (EPA, ACE, USFWS)
is confusing and contributes to ponds being underrepresented in US aquatic waterbody monitoring relative to
their numerical dominance on the landscape3,8
. For example, US EPA monitoring programs include ponds in
both the National Wetland Condition Assessment and the National Lake Assessment; however, “ponds” represent a small number of waterbodies in each of these surveys (<12% classifed qualitatively as “pond” in 2011
wetland survey; 13% of waterbodies were<5 ha in 2012 lake survey).
Refecting political and geographic variability at the national scale, most US states have their own waterbody
protections33. We surveyed US state agencies to examine state defnitions of ponds, lakes, and wetlands (see
“Methods”). Our survey responses included 42 of 50 (84%) states (Fig. 3). Only one state (Michigan) explicitly
defned ponds, 11 states defned lakes (26%), and 30 states defned wetlands (71%). While only one state defned
ponds, half of the surveyed states used the term “pond” in their legislation. Specifcally, ponds were referenced
as state waters (e.g., Vermont) or were included in state defnitions for lakes (e.g., Kansas) or wetlands (e.g.,
Rhode Island). It is unclear how these defnitions impact monitoring and protection or why the distinctions
were originally made. For instance, many states monitor lakes based on minimum size thresholds, which vary
widely from < 1 ha in Arizona and Alaska, 2–4 ha in many northeastern states, and up to 8 ha in Washington
and Nebraska. Te variety of defnitions and monitoring size cutofs do not appear to be scientifcally based,
but may stem from arbitrary decisions, historic references, mapping capabilities from decades ago, and resource
limitations for monitoring; the same rationale for defnitions likely apply to local, regional, and international
organizations around the globe.
Comparing lake, pond, and wetlands characteristics from literature. We compared biological,
physical, and chemical characteristics of waterbodies that scientists called lakes, ponds, or wetlands in published
studies. To obtain data for the pond characteristics, we used the same literature search summarized above for
pond defnitions (also, see “Methods”). From the 519 papers that we examined, we extracted data on sites the
authors called “ponds” and other variants (e.g., ‘small ponds’, ‘fsh ponds’, but NOT ‘lakes’). We fltered waterbodies that were≤20  ha surface area and≤9  m depth (global distribution; n=1327) to include waterbodies
slightly greater than the maximum depth and maximum surface area used to defne ponds in prior studies34,35.
To compare ponds to lakes and wetlands, we used existing lake (US and Europe; n=55,173) and wetland (US;
n=400) databases; waterbodies were classifed as lake or wetland by the scientists or managers who published
the database. Wetlands were classifed as<1 m in depth with no defned surface area and lakes were all>0.02 ha
with no defned depth (see “Methods’’ for details).
From the waterbodies that scientists called “ponds,” hydroperiod and origin varied over a large range of
characteristics. Of the 608 ponds with hydroperiod data, permanent ponds accounted for 74% (n =450) and
temporary ponds for 26% (n=158). Out of 648 ponds with known origins, 65% (n=418) were constructed or
manipulated and 35% (n=230) were natural. Terefore, scientists consider ponds to be inclusive of both permanent and temporary hydroperiod and have natural or human-made origins.
Figure 3. US state responses to surveys indicating if the state has a defnition of wetland, lake, or pond and if
the state used the term “pond” in their legislation. NR=no response.
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When examining water chemistry, nutrients, and biotic data across diferent waterbody types, as defned by
publishing scientists and managers, we found that ponds were distinct from lakes and wetlands in two metrics
(TN, pH), similar to wetlands in one metric (TP), and similar to lakes in one metric (chl a; Fig. 4; Tables S1, S2).
For example, ponds had distinctly high TN concentrations, which were greater than either lakes or wetlands
(Fig. 4b; Table S2). Ponds and wetlands had similarly high TP concentrations, which were signifcantly greater
than lakes; ponds were also most variable in TP (Fig. 4a; Table S2). Lastly, ponds chlorophyll (chl) a concentrations were similar to lakes, with wetlands being most variable but lower, on average (Fig. 4d; Table S2).
Does ecosystem structure and function distinguish ponds from lakes and wetlands? We evaluated the relationship between key metrics of ecosystem structure or function with three quantitative variables
that ofen showed up in pond, lake, or wetland defnitions: surface area, maximum depth (hereafer depth), and
emergent vegetation cover. Our metrics of ecosystem structure or function include nutrients (total phosphorus
(TP), total nitrogen (TN)), water chemistry (pH), primary producer biomass (chl a), metabolism (gross primary
production—GPP, respiration—R, net ecosystem production—NEP), and heat and gas distributions and movement (diel temperature ranges—DTR, methane fuxes, gas transfer velocities). Te data was collated from global
surveys of literature and federal or international databases (see “Methods”) with ultimately ten comparisons for
surface area, six comparisons for depth, and four comparisons for emergent vegetation cover with a range of
sample sizes for each comparison (n=67 to 7931, see Tables S3, S5, S7). We assessed each relationship for four
Figure 4. Comparison of various chemical and biological parameters across wetlands, ponds, and lakes, with
waterbody category based on the term used by publishing scientists and managers (Table S2). Violin plots
indicate distributions of waterbody characteristics, the white box indicates 25th to 75th percentile with median
in the middle, whiskers indicate 1.5×interquartile range, and outliers are black closed circles. Letters inside the
plot indicate signifcant diferences in means (LSD, alpha=0.05). Note all x-axes have logarithmic scales.
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diferent patterns in increasing order of complexity: null, linear, segmented (nonlinear), and logistic (nonlinear)
patterns and selected the best ft and most parsimonious relationship.
Ecosystem structure and function were mostly nonlinearly related to surface area (n=9/10 variables), depth
(n=5/6 variables), and emergent vegetation cover (n=3/4 variables) with both segmented and logistic relationships occurring (Figs. 5, 6, 7; Tables S3–S8). For surface area, six variables had logistic relationships: TP (Fig. 5b),
methane fuxes (Fig. 5d), chl a (Fig. 5f), diel temperature range (Fig. 5h), gas exchange rates (k600; Fig. 5i), and
pH (not pictured). Te infection occurred at 0.8 ha for TP, 1.1 ha for methane fuxes, 1.5 ha for chl a, 1.7 ha for
pH, 4.6 ha for diel temperature range, and 17.5 ha for gas exchange rates (Table S3). NEP (Fig. 5c), R (Fig. 5e),
and TN (Fig. 5g) all had segmented linear relationships where smaller systems had steeper slopes than larger
systems (Table S4). Te breakpoint in surface area was 1.0 ha for NEP, 1.2 ha for R, and 3.8 ha for TN (Table S3).
For depth, two variables had logistic relationships: diel temperature range (Fig. 6e) and chlorophyll a (Fig. 6f),
with the infection occurring at 5.9 m and 14.9 m, respectively (Table S5). pH (Fig. 6b), TP (Fig. 6c), and TN
(Fig. 6d) all had segmented linear relationships where smaller systems had steeper slopes than larger systems
(Table S6) with breakpoints occurring at 1.0, 2.1, and 5.2 m, respectively (Table S5). For emergent vegetation
cover, TN (Fig. 7b), TP (Fig. 7c), and pH (Fig. 7d) all had segmented linear relationships where systems with
more emergent vegetation had steeper slopes than more open systems (Table S8). Te breakpoint in emergent
vegetation cover was 6.0% for TN, 8.2% for TP, and 26.0% for pH (Table S7).
To summarize across all three metrics (surface area, depth, and emergent vegetation cover), we evaluated
where the boundaries of nonlinear relationships generally occurred, which informs boundaries between ponds,
lakes, and wetlands (Table 1). For surface area, the boundary was 3.7±1.8 ha (mean±standard error) and the
median was 1.5 ha, consistent with the median of 2 ha from scientifc defnitions (Fig. 2b). Te depth boundary
was 5.8 ± 2.5 m (mean ± standard error) and the median was 5.2 m, within the range of scientifc defnitions
(Fig. 2c). Te emergent vegetation cover boundary was 13.4±6.3% (mean±standard error) and the median was
8.2%, both of which were lower than the previously identifed wetland lower bound of 30%31.
Figure 5. Relationships between lentic waterbody size (excluding wetlands) and ecosystem structure and
function metrics: (a) gross primary production (GPP), (b) total phosphorus concentrations (TP), (c) net
ecosystem production (NEP), (d) methane fuxes (CH4 fux), (e) respiration (R), (f) chlorophyll a concentrations
(Chl a), (g) total nitrogen concentrations (TN), (h) diel temperature ranges (DTR), and (i) gas transfer piston
velocity (k600). Optimal model fts from null, linear, segmented, and logistic curves in bold foreground lines. For
nonlinear segmented and logistic models (b–i), plots are ordered by boundaries between ponds and lakes, as
defned by model breakpoints or infection points (vertical background lines).
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Pond morphology (e.g., size and depth) creates fundamentally distinct conditions that govern ecosystem
structure and function. Specifcally, ponds experience less wind-driven turbulence than larger waterbodies due
to small fetch and sheltering from the landscape36. We found that gas exchange rates (k600) decreased at~18 ha,
presumably due to reduced wind shear (Fig. 5i; also supported by37) and altered thermal dynamics. For instance,
ponds and shallow lakes can warm dramatically during the day, inducing stratifcation, and cool of and mix completely overnight38. We found higher diel temperature ranges were more common in waterbodies<5 ha (Fig. 5h)
and<6 m (Fig. 6e; see also39). Such diferences in temperature and mixing can promote internal nutrient loading40
and ecosystem respiration41, which may explain the higher TN (Figs. 4b, 5g), TP (Figs. 4a, 5b) and ecosystem
respiration (Fig. 5e) found in ponds. Lastly, diferences in water column mixing, increased nutrients, and higher
respiration can all contribute to the higher greenhouse gas emissions found in ponds relative to lakes (Fig. 5d)4,42.
Metrics of phytoplankton biomass (chl a) and total ecosystem production in the water (GPP) exhibited weak
or inconsistent relationships with surface area and depth, likely due to diferences in the location and types of
primary production across waterbody types. While total primary production in deep lakes is ofen dominated
by phytoplankton43, shallow waterbodies can shif toward non-planktonic primary production like benthic algae
or foating, emergent, or submerged macrophytes44. Ponds have pelagic phytoplankton, benthic algae (i.e., periphyton), and sediment rooted-submerged or foating macrophytes. In contrast, wetland productivity ofen predominantly occurs above the air–water interface45. Where emergent vegetation dominates, they may limit light
and reduce water column nutrients, both of which are needed by phytoplankton and periphyton. Macrophytes
can also modify water column and sediment geochemistry by providing autotrophic organic carbon and oxygen
to rooting systems in the sediments46. Consequently, these opposing drivers can explain the high variability in
primary production we observed (Fig. 5f, Table S2). Distinguishing ponds from wetlands will ultimately be aided
by additional ecosystem measurements of metabolism, greenhouse gas production, and additional metrics (e.g.,
carbon burial) across shallow waterbodies with a range of emergent vegetation cover.
Figure 6. Relationships between lentic waterbody maximum depth (Max depth) and various ecosystem
structure and function metrics: (a) methane fuxes (CH4 fux), (b) pH, (c) total phosphorus concentrations (TP),
(d) total nitrogen concentrations (TN), (e) diel temperature ranges (DTR), and (f) chlorophyll a concentrations
(Chl a) from literature data extraction with optimal model fts from null, linear or null, segmented linear, and
logistic curves in bold foreground lines. For nonlinear segmented and logistic models (b–f), plots are ordered by
model breakpoints or infection points (vertical background lines), indicative of boundaries between ponds and
lakes.
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A functional pond defnition. Our review of existing pond defnitions highlights that surface area and
depth are the most common variables used to defne ponds; yet how small and how shallow a waterbody must
be to classify as a pond is unclear, with defnitions ranging by orders of magnitude. Emergent vegetation is a
third variable useful in distinguishing wetlands from ponds, but the threshold value,>30% emergent vegetation
coverage for wetlands established at the US federal level, is not based on documented changes in ecosystem function. Comparing characteristics among waterbodies that scientists self-categorized into lakes, ponds, or wetlands, ponds were sometimes distinct from lakes and wetlands (pH, TN), sometimes similar to wetlands (TP),
and sometimes similar to lakes (chl a), suggesting ponds are an ecologically distinct type of ecosystem. Lastly, we
Figure 7. Relationships between lentic waterbody emergent vegetation cover (Emergent veg.) and various
ecosystem structure and function metrics: (a) chlorophyll a concentrations (Chl a), (b) total nitrogen
concentrations (TN), (c) total phosphorus concentrations (TP), (d) pH from literature data extraction with
optimal model fts from null, linear or null, segmented linear, and logistic curves in bold foreground lines. For
nonlinear segmented and logistic models (b–d), plots are ordered by model breakpoints or infection points
(vertical background lines), indicative of boundaries between ponds and wetlands.
Table 1. Nonlinear boundary values, parameter estimate±standard error (SE), from comparisons between
surface area, maximum (max.) depth, and emergent vegetation (veg.) cover and ecosystem structure/function
metrics including gross primary production (GPP), total phosphorus concentrations (TP), methane fuxes
(CH4 fux), respiration (R), net ecosystem production (NEP), chlorophyll a concentrations (Chl a), pH, total
nitrogen concentrations (TN), diel temperature ranges (DTR), and gas transfer piston velocity (k600). Boundary
estimates are included if the nonlinear models (segmented regression or logistic relationships) were selected as
optimal fts with standard error as determined when ftting the parameter. NA indicates a null or linear ft, –
indicates not enough data was available to perform the analysis.
Ecosystem metric
Surface area
Boundary est.±SE (ha)
Max. depth
Boundary est.±SE (m)
Emergent veg. cover
Boundary est.±SE (%)
GPP NA – –
TP 0.8±1.2 2.1±1.2 8.2±1.2
NEP 1.0±1.4 – –
CH4 fux 1.1±1.7 NA –
R 1.2±1.5 – –
Chl a 1.5±1.7 14.9±1.2 NA
pH 1.7±1.5 1.0±1.4 26.0±1.3
TN 3.8±1.4 5.2±1.4 6.0±1.3
DTR 4.6±1.3 5.9±1.3 –
k600 17.5±1.5 – –
Mean 3.7±1.8 5.8±2.5 13.4±6.3
Median 1.5 5.2 8.2
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found clear nonlinear relationships when we examined relationships between ecosystem structure or function
and surface area, depth, and emergent vegetation cover; these boundaries help to quantitatively defne ponds.
Specifcally, we found that across available ecosystem metrics, ecosystems shif in structure and function at
average (±SE) values of 3.7 (±1.8) ha in size, 5.8 (±2.5) m in depth, and 13.4 (±6.3) % emergent vegetation cover
(Table 1). For surface area, all but one ecosystem metric (k600) was below 5 ha in surface area, which fts well
within the range of most existing defnitions (≤10 ha; Fig. 2), and we suggest may be used to distinguish ponds
from lakes. For maximum depth, all but one ecosystem metric (chl. a) was below a 6 m depth threshold, which
also fts well within the range of depths reported in pond defnitions (Fig. 2), and matches the published threshold of 5 m maximum depth for shallow lakes44. Our depth analysis was less robust than surface area because we
had less depth data, a common challenge in lentic studies47; we therefore advise further studies in waterbodies
to explicitly evaluate this threshold. Until further work is done, we recommend using 5 m as a maximum depth
threshold for ponds as it is close to both threshold shifs in ecosystem function and matches with the shallow
lake literature44,48. We had the fewest ecosystem metric comparisons for emergent vegetative cover, and observed
three nonlinear boundaries ranging from 6 to 26% cover. Te mean (13.4%), though smaller, is not statistically
diferent than the 30% emergent vegetation cover (one sample t-test, t2=− 2.6, p=0.12) proposed by Cowardin
et al.31 to separate wetlands from lakes. We recommend separating ponds and wetlands using the 30% coverage
in emergent vegetation threshold for now, but recognize that the Cowardin et al.31 metric is not data driven and
our analysis was limited by existing data. Future studies must examine how ecosystem structure and function
shifs across a gradient of emergent vegetation cover to better functionally distinguish wetlands from ponds and
could ultimately lower that boundary.
Our review of data from the literature showed scientists and managers view ponds as permanent or temporary
and natural or human made in origin. Terefore, we felt it necessary to provide the inclusivity of these concepts
in a pond defnition. Other defnitions also link depth to light availability, where light penetrates to the sediments
across the pond (e.g.,11). However, light availability is not only mediated by depth; even in the shallowest systems
light can be limiting due to turbidity, dissolved organic matter, and submerged or foating plants (e.g.,49,50). For
example, foating duckweed can cover most of a pond’s surface area and reduce light penetration to<1% relative
to the light above the water’s surface49, and dramatically change the ecology of shallow systems51.
As our analyses indicate that ponds are functionally distinct from lakes and wetlands, we propose the following scientifcally informed pond defnition (Fig. 8):
Ponds are small and shallow waterbodies with a maximum surface area of 5 ha, a maximum depth of 5 m,
and < 30% coverage of emergent vegetation. Ponds will have light penetration to the sediments if water
clarity permits and can be permanent or temporary and natural or human-made.
Our proposed defnition is based on the current state of the science; we anticipate that future research will
further resolve diferences among these fve categories. For example, we call for future research to examine how
ecosystem structure and function shif across our proposed boundaries, particularly for depth and emergent
Figure 8. Conceptual model to defne lentic waterbodies based on three diferent criteria (depth, surface area,
and emergent vegetation). Boundaries for all three axes come from our analysis and are informed by existing
pond, lake, and wetland defnitions. Figure by Visualizing Science.
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vegetation, which had smaller sample sizes and fewer ecosystem metrics than surface area. Additional variables
such as basin geometry (e.g., area:volume ratios), sheltering from wind, water residence time, water clarity, and
geographic location, may also afect a waterbody’s ecosystem structure and function, creating some overlap
between classifcations especially along the upper and lower bounds of our pond defnition. For instance, a
landscape with little wind sheltering increases water column mixing that could cause a waterbody the size of a
pond to function more like a shallow lake. We therefore advocate for additional sampling of lentic waterbodies,
especially in locations where lentic waterbodies are understudied or being rapidly constructed like tropical and
subtropical regions52, to help resolve boundaries among waterbody types and further refne the pond defnition.
Conclusion
Scientists, policy makers, water resource managers and the public all use the word “pond” to describe small and
shallow waterbodies, which are globally abundant8
 and hotspots for biogeochemistry4,8
 and biodiversity53. Yet,
the lack of a universal pond defnition means that ponds can fall between lake and wetland jurisdictions and
categorizations22, thus potentially limiting their legal protections. Globally, the situation is similar to US policy
as some nations defne ponds as wetlands (e.g., the Ramsar Convention), some as lakes (e.g., Denmark), and
others specifcally defne ponds (e.g., United Kingdom). Te pond defnition presented here will favor more
frequent and consistent use of the term and ultimately improve the protection, monitoring, and scientifc study
of ponds, which are globally abundant and structurally and functionally distinct from other lentic waterbodies.
Methods
Literature survey. To compile biological, physical, and chemical characteristics of ponds, we conducted a
literature search based on three seminal papers establishing the ecological importance of ponds: Oertli et al.35,
Søndergaard et al.18, and Downing8
, each of which has>100 citations and is more than ten years old. We conducted a backwards and forwards search in April 2019 to compile all papers cited by these three papers, and
all papers that cited them, yielding 519 unique papers. We extracted physical, chemical, and biological data for
papers that reported data for individual waterbodies defned as ponds by the publishing scientists. To ensure
consideration of all potential ponds, we checked that waterbodies selected were small (≤20 ha in surface area)
and shallow (≤9 m in maximum or mean depth), boundaries that are slightly greater than the maximum depth
(8 m)35 and maximum surface area (10 ha)34 used to defne ponds in a few prior studies. We used the resulting
1327 waterbodies in our analysis, which had a global distribution (Fig. S1)19.
Scientifc defnitions. To investigate how scientifc researchers defned ponds, we reviewed all 519 papers
for pond defnitions. We included defnitions where the authors explicitly referred to their study waterbodies
as ponds (e.g., we excluded “shallow lakes” and “small lakes”), yielding 40 pond defnitions. Te defnitions
included 89 citations of 48 unique papers; we evaluated all cited papers that were not already in our compilation
for additional defnitions and citations. Tis process added 14 defnitions, plus an additional fve cited papers not
assessed due to our inability to access or translate them (data available19).
Federal and state defnitions. We examined policy defnitions using the United States (US) federal and
state legislation as an example because we posited diferences at this scale would refect the challenges faced by
governments from other countries in formulating a unifed pond defnition. At the federal level, we examined
three agencies with monitoring or regulatory responsibilities: US Environmental Protection Agency (EPA), US
Army Corps of Engineers (ACE), and US Fish and Wildlife Service (USFWS). Due to the difculty of fnding all
state policies, we sent electronic surveys to individuals working in state environmental agencies in all states. We
asked whether their state defned lakes, ponds, and wetlands, and requested the legislative sources. We received
responses from 42/50 states and evaluated all defnitions provided and their associated legislation.
Pond, lake, and wetland data. We compared chemical and biological characteristics among various
lentic waterbodies as defned by scientists as ponds, wetlands, or lakes. For ponds, we used data from the literature data extraction as described above (n=1327). Wetland data came from the US EPA’s 2011 National
Wetland Condition Assessment, which surveyed wetlands with standing water<1 m in depth and variable surface area54,55. We selected wetland sites that were freshwater and had water chemistry data (n=400). Lake data
was extracted from LAGOS-NE (lakes≥4 ha; n=51,101)56, EPA’s 2012 National Lake Assessment (lakes≥1 ha;
n=1130)57,58, and the European Environmental Agency’s Waterbase database (lakes>0.02 ha; n=2942)59. From
these sources, we compared nutrients (total phosphorus (TP), total nitrogen (TN)), water chemistry (pH), and
primary producer biomass (chl a) among waterbody types (ponds, wetlands, and lakes).
We also examined diferences in six additional metrics of ecosystem function across waterbodies using data
from a variety of sources and ranging in sample size from 67 to 198 global sites (gross primary production—GPP,
respiration—R, net ecosystem production—NEP, diel temperature ranges—DTR, methane fuxes, gas transfer
velocities). We extracted metabolism metrics (GPP and R) from an existing literature review60 and two published
studies of various sized lentic ecosystems41,61. DTR, calculated as the diel diference between the maximum and
minimum surface temperature for each waterbody, were extracted from multiple studies38,39. Areal methane
fuxes42 and gas transfer velocities (k600)37 were extracted from existing literature reviews.
Comparing lake, pond, and wetlands characteristics from literature. We evaluated whether there
were diferences among waterbody types as defned by scientists and managers. We determined signifcant differences in waterbody characteristics across waterbody types using ANOVA and post-hoc Least Signifcant
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Diference (LSD) analysis. We determined the variation within each freshwater type using the coefcient of
variation (cv) and tested for signifcant diferences using Levene’s test. We acknowledge that the confounding
defnitions of waterbodies resulted in overlapping size distributions; therefore any statistical diferences across
waterbody types will be conservative.
Does pond ecosystem structure and function distinguish ponds difer from lakes and wet- lands? We evaluated where the cutofs might exist along pond to lake and pond to wetland gradients. We
used the relationship between surface area, depth, or emergent vegetation cover represented by x below and
each ecosystem variable (n=10 variables for surface area; n=6 variables for depth, n=4 for emergent vegetation cover) represented by y below for four diferent patterns in increasing order of complexity: null, linear,
segmented (nonlinear), and logistic (nonlinear) patterns. We ft null models by taking the arithmetic mean
(Eq. 1), linear models using ordinary least-squares linear regression (Eq. 2), segmented using regressions with
one breakpoint (Eq. 3) via the “segmented” package62, and logistic using sigmoidal curves (Eq. 4) via the nls
function in R (Fig. S2). Parameters a – h and bp (breakpoint) were ft using the methods above.
We log-transformed surface area and depth to account for the several order of magnitude scale and nonnormality. Similarly, we transformed some of the ecosystem variables depending on normality and distributions.
To select among the four models for each relationship, we examined the AICc fts and selected the minimum
AICc as the optimal ft with consideration of other model fts within 11 units of the minimum AICc using root
mean squared error and parsimony63. If one of the nonlinear models was selected, we then objectively quantifed
the boundary among ecosystem types (i.e., pond vs. lake or pond vs. wetland) using either the breakpoint or the
infection point parameter from the segmented regression or sigmoid curve, respectively (Fig. S2c,d).
Data availability
All data used for this manuscript is available through an Environmental Data Initiative data publication (Richardson et al. 2022: https://doi.org/10.6073/pasta/ec507ac70846b17d0633d95aa3c680c6).
Received: 7 January 2022; Accepted: 8 June 2022
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Acknowledgements
Tis work was supported by the Global Lake Ecological Observatory Network (GLEON). We thank participants
in the GLEON 19 pond ad hoc meeting for discussions that formed the basis for this project. MRA was supported
as part of the BEYOND 2020 project (grant-aid agreement no. PBA/FS/16/02) by the Marine Institute and funded
under the Marine Research Programme by the Irish Government. Funding to KSC and KBSK was from the
US National Science Foundation (EF-1638679 and EF-1638539). KKH was supported by the Adam S. Tomas
Endowment and by the St. Olaf Collaborative Undergraduate Research and Inquiry Program. Tis material is
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based upon work supported by the National Science Foundation Graduate Research Fellowship Program under
Grant No. 2036201 to MJF (Farruggia). Any opinions, fndings, and conclusions or recommendations expressed
in this material are those of the authors and do not necessarily refect the views of the National Science Foundation. Credit for Fig. 8 to Fiona Martin at Visualizing Science (https://www.visualizingscience.com/).
Author contributions
D.C.R. and M.A.H. led the project. D.C.R., M.A.H., M.J.F., K.K.H., and K.B.S.K. organized the literature searching, survey, data analysis, fgure preparation, and wrote the manuscript. All authors completed data mining from
literature survey and were substantively involved in two of the following three categories: (1) project conceptual development, (2) data extraction/analysis/interpretation, and (3) writing/revising manuscript. All authors
approved the fnal version of this manuscript prior to submission.
Competing interests
Te authors declare no competing interests.
Additional information
Supplementary Information Te online version contains supplementary material available at https://doi.org/
10.1038/s41598-022-14569-0.
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,
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