Assessing the ecological integrity of endorheic wetlands, with focus on Mediterranean temporary ponds

Ecological Indicators 54 (2015) 1–11

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Ecological Indicators journal homepage: www.elsevier.com/locate/ecolind

Review

Assessing the ecological integrity of endorheic wetlands, with focus on Mediterranean temporary ponds Maarten Van den Broeck a,c,∗ , Aline Waterkeyn a,b , Laila Rhazi c , Patrick Grillas b , Luc Brendonck a a

Laboratory of Aquatic Ecology, Evolution and Conservation, University of Leuven, Charles Deberiotstraat 32, Leuven 3000, Belgium Tour du Valat Research Centre for Mediterranean Wetlands, Le Sambuc, Arles 13200, France c Laboratoire de Botanique, Mycologie et Environnement, Université Mohammed V, Faculté des Sciences de Rabat, 4 Avenue Ibn Battouta, Rabat BP 1014, RP, Morocco b

a r t i c l e

i n f o

Article history: Received 2 October 2014 Received in revised form 6 February 2015 Accepted 10 February 2015 Keywords: Biological indicators Temporary wetlands Ecological assessment WFD

a b s t r a c t EU countries are required to perform an assessment of all freshwater habitats larger than 50 ha by 2015 to meet the requirements set by the Water Framework Directive (2000). To achieve this, an array of indicators and multimetric indices has been developed to monitor European waters. In general, these indicators are developed for large water bodies, while they are still largely lacking for smaller wetlands. This is in contrast with the conservation value, valuable ecosystem services and the often unique biodiversity of these systems, and the fact that like large (>50 ha) wetlands they are also covered by the Ramsar Convention. In (semi) arid regions, such as the Mediterranean basin, small water bodies are often of a temporary nature, are abundant and provide an important source of water for the local people, their livestock and agriculture. The quantity and quality of temporary wetlands are, however, decreasing at an alarming rate worldwide. Although some monitoring techniques were recently proposed, there is still an urgent need for a consistent policy and a user friendly set of monitoring tools for temporary wetlands that can be applied in different regions. In the following review, we present a whole range of indicators used to monitor different types of freshwater habitats, and discuss how some of these methods could be applied to temporary wetlands. Finally, we formulate some recommendations for temporary wetland monitoring and conservation. © 2015 Elsevier Ltd. All rights reserved.

Contents 1. 2.

3.

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Overview of suitable indicators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1. Abiotic indicators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2. Biotic indicators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2.1. Vertebrates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2.2. Macroinvertebrates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2.3. Zooplankton . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2.4. Macrophytes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2.5. Phytoplankton . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2.6. Multimetric indices (MMI) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2.7. Modeling approaches . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1. Temporal and spatial regional variability in temporary ponds . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

∗ Corresponding author at: KU Leuven, Department of Biology, Charles Deberiotstraat 32, Box 2439, 3000 Leuven, Belgium. Tel.: +32 16373750. E-mail address: [email protected] (M. Van den Broeck). http://dx.doi.org/10.1016/j.ecolind.2015.02.016 1470-160X/© 2015 Elsevier Ltd. All rights reserved.

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3.2.

Monitoring guidelines for temporary wetlands . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2.1. Dry phase . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2.2. Aquatic phase . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3. Concluding remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1. Introduction Wetlands are one of the most biologically diverse ecosystems on earth (Mitsch and Gosselink, 2007). They usually house a diverse fauna and flora, including many rare and threatened species (Keddy, 2010). Wetlands also perform many important ecosystem services, including water storage, carbon sequestration, flood reduction, sediment trapping and reducing the effects of pesticides and other types of pollution through filtration (Costanza et al., 1997; Joyce, 2012). Although wetlands only cover 6% of the total land surface (Naiman and Décamps, 1997), the value of these areas is estimated to range between 49 billion to 3.4 trillion euros per year, measured as the budget needed if these services were to be replaced (Schuyt and Brander, 2004). According to the Ramsar Convention (Ramsar, 2013), wetlands include marshes, peatland and fens, ditches, lakes, ponds, lagoons, floodplains, estuaries and coastal zones (including coral reefs) not deeper than six meters (at low tide in case of tidal systems). They are characterized by: (i) a water saturated soil, (ii) a different soil composition compared to the surrounding nonwetland areas and (iii) specifically adapted vegetation that tolerates high water levels either permanently or temporarily, depending on the type of wetland (Maltby et al., 2009). In general, high pressure due to human population growth often results in the disappearance of natural landscape components, such as natural water bodies and riparian zones. Globally, a wetland loss of 50% in the last century is commonly reported (Finlayson and D’cruz, 2005), and different models predict a loss ranging from 11% to 62% by 2080 for coastal wetlands alone (Nicholls, 2004). Together with intensified agriculture, anthropogenic pressure has also led to diffuse pollution and eutrophication in freshwater ecosystems (Arheimer et al., 2005). This loss and degradation of wetlands in general and their ecosystem services is further accelerated by climate change and introductions of invasive species. Despite the urgent need of protection of wetlands, monitoring and conservation are often hampered by sometimes inconsistent and contradictory international agreements and national policies (Turner et al., 2010), which makes their ecological assessment difficult to achieve. Even though the socio-economic value and ecological importance of wetland systems has widely (but only recently) been accepted, particularly in the subtropics, no recovery has yet been observed (Prigent et al., 2012). The Water Framework Directive (WFD) (Directive 2000/60/EC) commits European Union member states to achieve good qualitative and quantitative status of all (ground and surface) water bodies by 2015 according to a set of standard criteria. In order to assess the current status of European surface waters and monitor any changes after management practices are implemented, a wide array of indicators were developed, not only based on standard chemical parameters, but also on biological characteristics, focusing on macrophytes, fish, phytoplankton and benthic macroinvertebrates (Solimini et al., 2009). Many indicators have been developed for different types of aquatic ecosystems ranging from small streams to large lakes as reviewed in Bird and Day (2010) and Birk et al. (2012). However, the WFD monitoring programs do not incorporate assessment techniques for temporary wetlands. In fact, the WFD currently excludes most temporary systems, since many of them are smaller than the stated size threshold of 50 ha. On the other hand, the Natura 2000 Network and the Ramsar Convention

8 8 8 9 9 9

do have a special resolution on temporary wetlands (Ruiz (2008) and Ramsar Resolution VIII.33, respectively) that suggests the need for monitoring programs based on biological indicators to protect and manage temporary wetlands. In arid and semi-arid regions, temporary waters are often very abundant and are an important water source (Brendonck and Williams, 2000; Williams, 2006; Bouahim et al., 2011). They are usually defined as wetlands that occur in endorheic depressions, characterized by alternating dry and wet phases, where the wet phase is sufficiently long to establish the specific soil conditions and floral and faunal communities of ephemeral ponds (Williams, 2006). They often house diverse plant and animal communities (Williams, 1997; Blaustein and Schwartz, 2001) and contribute tremendously to regional (gamma) biodiversity (Gibbs, 2000; Nicolet et al., 2004; Williams et al., 2004), sometimes even more than large water bodies (Biggs et al., 2014). They offer (temporary) housing to both general (opportunistic) species as well as to unique (temporary pond specific) species that are adapted to living under time stress and extreme environmental conditions (Grillas et al., 2004). Unfortunately, temporary wetlands are often neglected and disappear at an alarming rate, with percentage loss during the last century ranging from 60% to 97% in different parts of the world (Brendonck and Williams, 2000; Nicolet et al., 2004; Rhazi et al., 2012). Due to their small size and shallowness, these habitats are poorly buffered and easily destructed or degraded by human activities, such as urbanization, agriculture and pollution (Rhazi et al., 2012). Additionally, climate change is expected to have a much greater impact on these small water volumes compared to larger lakes (Parmesan, 2006). Therefore, vigilant monitoring and conservation of these systems is crucial. On the other hand, climate change could also increase the number of habitats by transforming currently perennial systems into temporary ones. Mediterranean temporary ponds are a peculiar type of temporary wetlands, which mainly occur around the Mediterranean basin in southern Europe and North-Africa, but also in other regions experiencing a Mediterranean climate (i.e. mild and rainy winters, hot and dry summers), such as the southwestern coastal region of South Africa, South-West Australia, California and Chile (Grillas et al., 2010). The Mediterranean temporary ponds in southern Europe are included as a EU Priority Habitat under the auspices of the Habitats Directive (Natura code 3170, 92/43/CEE, 21 May 1992). These ponds harbor several rare or threatened species of plants, amphibians and invertebrates listed on international conventions (Habitats Directive, the Bern Convention and the IUCN Red List) (Grillas et al., 2004). The Ramsar Convention is also implemented in the Mediterranean Wetlands Strategy, aimed at “stopping and reversing the loss and degradation of Mediterranean wetlands as a contribution to the conservation of biodiversity and to sustainable development in the region” (Ramsar, 2013). However, lack of political recognition of small waterbodies as an entity and vital part of the water environment remains unacceptably high throughout Europe (Oertli et al., 2005b). Currently, efforts are made to emphasize the need to protect and include small (temporary) waters in the WFD, such as the “workshop on the protection and management of small water bodies” which took place in November 2013 and was organized by the European Environmental Bureau, in co-operation with the European Commission, the Lithuanian Presidency and the Freshwater Habitats Trust (Biggs et al., 2014).

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In this paper, we do not aim at presenting an exhaustive overview of all existing indicators for wetlands that are currently used or available. Instead, we aim to critically review examples of different indicators that were developed to assess the ecological quality of endorheic wetlands in general and evaluate their usefulness for temporary wetlands more specifically. Although these indicators cannot simply be extended, we believe that they are a good starting point. Using thorough ecological research, they should then be modified and calibrated to be representative for temporary aquatic systems. We also review novel indicator approaches based specifically on the ecology of temporary wetlands. 2. Overview of suitable indicators Niemi and Mcdonald (2004) define ecological indicators as components of structural, compositional, and landscape related processes that are used to quantify human impact on ecosystems. These indicators may be of a chemical, physical or biological nature. Since ecological indicators are essential components of the wetland ecosystem, understanding the role they play in the ecosystem is vital (Innis et al., 2000). By correlating groups of biotic variables, such as species richness and life history traits, with abiotic factors, such as hydrology, oxygen concentration, salinity, nutrient and pollutant levels, the indicator value of such biotic factors can be assessed. Traditionally, only abiotic indicators such as water chemistry were used to assess anthropogenic disturbance. Later on, biotic indicators were preferred since they integrate overall water and habitat quality and therefore document how episodic and cumulative disturbances impact the ecological integrity of an ecosystem (Burton et al., 1999). Nowadays, modern statistical approaches are used to construct indicators for different types of habitats. Such a statistical approach is based on calculating distance matrices (e.g. Bray–Curtis distances, Euclidian distances) from (a)biotic variables between sampling sites. Then, by using Mantel tests (Mantel, 1967) or other matrix correlation tests, community concordance can be assessed between these matrices, as described by for example Padial et al. (2012), to provide information on how communities are correlated with the measured environmental variables. More than 75 studies were reviewed to select indicators of various freshwater habitats. Table 1 summarizes different indicator groups, including some specific examples, their advantages and disadvantages, and indicates for which type of habitat and anthropogenic disturbance/pressure they were originally developed. 2.1. Abiotic indicators Abiotic indicators are specific soil or water conditions that can be used to quantify the level of anthropogenic disturbance impacting the wetland ecosystem. They can be divided into physical and chemical indicators. Most physical indicators (e.g. soil granulometry, % of organic matter and sediment redox potential) can be impacted by land use and changes in the hydrology of the system such as drainage. Hydrological changes have an impact on the duration of flooding, maximum depth and number of flooding events per year of temporary wetlands. Chemical indicators (e.g. pH, concentrations of ions, dissolved gases, pollutants and nutrients) are potentially useful for detecting and quantifying the level of environmental stress impacting the ecosystem (Feld et al., 2009). For example, measuring the nutrient concentrations (e.g. total nitrogen (tN), total phosphorus (tP), ammonium (NH4 + ), orthophosphates (PO4 3− ) and nitrates/nitrites (NOx − )) in the soil or water column can give an indication of the level of eutrophication, while the concentration of ions may reflect secondary salinization and pH values give an idea of the level of acidification. Specifically for wetlands

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and shallow lakes with fluctuating water levels, the correct productivity assessment is important for distinguishing between natural and anthropogenic eutrophication (Elkiathi et al., 2013). For example, Golterman (2004) described accurate P-fractionation methods to measure the P-binding capacity of wetland sediments as an estimation of eutrophication (as this represents the P-bioavailability for primary producers). To assess the degree of organic pollution, chemical and biological oxygen demand (COD and BOD) tests can be used since they are an indirect measure of the amount of organic components that can be broken down (i.e. oxidized, biologically or chemically) by aerobic organisms in the water column (Kadlec and Wallace, 2008). 2.2. Biotic indicators Biotic indicators or bio-indicators can involve single indicator species or entire assemblages/communities whose presence/absence, abundance or diversity patterns can provide information about ecological changes and health of a system (Angermeier and Davideanu, 2004; Cousins and Lindborg, 2004). Animal indicators for aquatic systems mainly focus on macroinvertebrates, since they are well studied and have a widespread distribution (Resh, 2008). Many bio-indicators are also based on macrophytes and phytoplankton (Resh, 2008; Bornette and Puijalon, 2011). Below, we evaluate the suitability of different animal and plant groups that are present in temporary ponds as biotic indicators For each group, this assessment is followed by a presentation of some specific examples (see also Table 1), making use of species composition, community structure and diversity of assemblages/communities, or on functional traits of selected species. 2.2.1. Vertebrates Although fish are the most common vertebrate group used as biotic indicators in aquatic environments (e.g. indices of biotic integrity, physiological/behavioral traits, etc. (Adamus and Brandt, 1990)), they are lacking in most temporary waters and will not further be discussed. Similar for mammals, although relatively simply to identify, their presence is highly variable and is affected by migration (Croonquist and Brooks, 1991). For amphibians, a relatively easy way of quantifying their abundances (especially Anura) has been developed via their call intensity, as used in the Wisconsin Index (WI) (Balcombe et al., 2005; Paloski et al., 2014). Although it is not always possible to quantify exact numbers of individuals, this method is cheap, relatively fast and non-invasive. Rare studies with waterfowl have shown some potential for their use as a bioindicator, with contrasting bird communities in pristine habitats compared to heavily impacted habitats, both on a local and regional scale (Amat and Green, 2010). Bird species composition and diversity in general can therefore mainly act as an indicator of land use alteration, habitat fragmentation, and other human influences. While waterfowl can easily be identified, have a high societal value and a well-known life history, they are highly mobile and are often more affected by habitat characteristics (e.g. water surface, vegetation) and season than by water quality. 2.2.2. Macroinvertebrates Macroinvertebrates are the most commonly used organisms as aquatic bioindicators because of their well-known life history and ecology (Rosenberg and Resh, 1993; Innis et al., 2000). Abundance and composition of macroinvertebrates have been used variably to develop sometimes country specific biotic indices. Invertebrates are usually highly adapted to a particular environment, making them susceptible to stressors such as lack of oxygen or high salinity (Keddy, 2010), and are therefore integrated in the WFD (as composition and abundance of benthic invertebrates). Oertli et al. (2005a) developed a relatively cost-efficient method for assessing the status

4 Table 1 Overview of indicators used for surface water monitoring, with emphasis on lentic wetland systems. The different indicator groups are presented with some specific examples, also mentioning their indicator value for the wetland type for which they were developed, their advantages and disadvantages and the respective reference(s). Biotic integrity: defines the general state of the ecosystem functioning. Indicator group

Indicator

Indicative of

Wetland type

Advantages

Disadvantages

References

Abiotic indicators

Concentrations of nutrients and ions

- Eutrophication - Salinization - Acidification Organic pollution

All wetlands

Easy to measure

Snapshot of current state

Yang et al. (2008)

All wetlands

COD faster than BOD

COD no differentiation between biological active/inactive substances

Kadlec and Wallace (2008)

Sediment P-binding capacity

Eutrophication

All wetlands

Wisconsin Index (WI), frog call intensity Diversity of waterfowl

Biotic integrity

All wetlands

High sensitivity and accuracy towards other P-fractionation methods - Cheap and relatively fast Non-invasive

- Land cover alteration - Habitat fragmentation

All wetlands

- Eutrophication - Organic pollution Biotic integrity

Ponds

COD/BOD

Vertebrates

PLOCH Diversity of higher taxa of Coleoptera MI diversity

Coleoptera diversity Zooplankton

Macrophytes

ACCO (as part of QAELS)

- Human disturbance - Phosphorous concentration - Pesticides - Eutrophication - Magnesium concentration - Conductivity - Turbidity - Nutrient input - Salinity - Organic pollution

Mediterranean ponds Mediterranean ponds

- Fast - No detailed identification needed Less detailed identification required (genus level)

- Only for Anura - Not always possible to estimate absolute numbers - More affected by site and season than by water quality - Mobility and migrations

Paloski et al. (2014)

Beck and Hatch (2009)

Oertli et al. (2005a) Sánchez-Fernández et al. (2006) Interannual variation not considered

Trigal et al. (2007, 2009)

Temporary wetlands

- Cheap

Limited period per year where sampling can occur

Gutiérrez-Estrada and Bilton (2010)

Mediterranean wetlands

- Occur in great numbers - Easily captured - High sensitivity towards disturbances - Known interactions with macrophytes and phytoplankton - Limited mobility - Easy and cost efficient sampling, possible during whole year - Deeper layers with eggs available to reflect past changes - Immobile - Well known identification and life history - Susceptible to stress Large areas at once

- Low perceived societal value - Seasonal/daily fluctuations - Difficult identification

Boix et al. (2005)

Difficulties with determining diversity of eggs (hatching/identification)

Angeler and García (2005), Vandekerkhove et al. (2005)

- Sometimes long recovery time after disturbance - Variable succession patterns

Bornette and Puijalon (2011), Croft and Chow-Fraser (2007)

Reflections from different vegetation species are difficult to distinguish

Adam et al. (2010)

- Hatching of eggs - Diversity of unhatched eggs

Physical and chemical disturbances

Temporary and permanent wetlands

WMI

Eutrophication Organic pollution

Lakes

Multispectral remote sensing

Physical and chemical disturbances

All wetlands

M. Van den Broeck et al. / Ecological Indicators 54 (2015) 1–11

Macroinvertebrates

- High societal value - Well-known life history and identification Relatively economical

Golterman (2004)

Table 1 (Continued) Indicator

Indicative of

Wetland type

Advantages

Disadvantages

References

Phytoplankton

DIWC

- Marshes - Estuaries

Resh (2008)

Trophic State Index (TSI)

Snapshot of current state

Carlson (1977)

- Nutrient input - Salinity - Organic pollution Biotic Integrity

Mediterranean wetlands

- Long history of use - Cosmopolitan distribution - Low sampling effort - Relatively easy to measure - Can be applied for whole range of standing waters See ACCO

Low perceived social value

Multimetric indices (MMI)

- Pesticides - Sedimentation - Habitat alteration Eutrophication

See ACCO

Boix et al. (2005)

Lakes

- Low perceived societal value - Seasonal/daily fluctuations - Difficult identification

Lougheed and Chow-Fraser (2002)

MMI for macroinvertebrates

Eutrophication

Arable ditches

- Low perceived societal value - Communities may be predated selectively by birds or fish

Verdonschot et al. (2012)

PSYM

Biotic integrity

Ponds and canals

Only developed for UK

Biggs et al. (2000)

LEMN

Eutrophication

Shallow coastal lagoons

Extensive measurements required

Souchu et al. (2000), Brehmer et al. (2011)

RIVPACS

Biotic integrity

(UK) rivers

- Sometimes no Reference conditions left - Has to be developed for each region

Wright et al. (1998)

QAELS

WZI

Predictive modeling

Lakes

- Occur in great numbers - Easily captured - High sensitivity towards disturbances - Known interactions with macrophytes and phytoplankton - Limited mobility - Life histories and ecology often well understood - Often specialized and sensitive towards disturbances - Widespread and abundant - Limited mobility - Rapid assessment - No detailed identification needed for macroinvertebrates - Changes in structural hydrologic patterns are often the underlying cause for changes in biological correlations - Sometimes long-term data available - O/E ratios are easy to interpret - Standardized statistical modeling

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Indicator group

5

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of Swiss ponds, called PLOCH (‘Plans (PL) d’eau (O) suisses (CH)’). The quality status is allocated based on the presence/absence of aquatic Gastropoda and Coleoptera and the abundance of adult Odonata (in addition to quadrat based presence/absence of macrophytes, and abundance of adult Amphibia). The number of genera or even families of aquatic Coleoptera (which spares the effort of detailed species identifications) were shown to be useful for fast, cost-efficient monitoring of the overall quality of Southern Spanish lentic freshwater ecosystems (Sánchez-Fernández et al., 2006). The use of macroinvertebrates as bioindicators for Spanish wetlands and Mediterranean ponds in general has been studied as well. Trigal et al. (2007), for example, found that the pond condition index (to assess human induced disturbance) along with pesticides and phosphorous concentration, were the best predictors of macroinvertebrate communities. In a follow-up study, an index was created for the same ponds using different macroinvertebrate diversity characteristics that changed with increasing levels of eutrophication (Trigal et al., 2009). Also, for temporary ponds Gutiérrez-Estrada and Bilton (2010) showed that the diversity of Coleoptera was highly correlated with depth, conductivity, turbidity and magnesium concentration. Another example is the RIC index (‘richness of insects and crustaceans), developed by Boix et al. (2005). This measurement of taxon richness is the sum of the number of crustacean, adult Coleoptera and Heteroptera genera, plus the number of immature insects and is part of the QAELS index (Catalan for ‘water quality of lentic shallow environments’). 2.2.3. Zooplankton Although they are not included in the WFD, there are many characteristics of zooplankton (microcrustaceans) that make them useful as bioindicators especially in lentic water bodies (Lougheed and Chow-Fraser, 2002; Boix et al., 2005): (i) they occur in great numbers and are easily captured, (ii) their community structure varies according to trophic state differences and responds quickly to changes in the environment, (iii) trophic interactions between microcrustaceans and phytoplankton as well as macrophytes are well known and (iv) they have a high taxonomic resolution allowing very detailed ecological assessments. In case of problems to identify all groups down to species level, even a mixed-taxon resolution can be applied to obtain acceptable results in a more cost-efficient way. This is illustrated by the ACCO index, as part of the QAELSindex for Mediterranean wetlands (Boix et al., 2005). This index is based on the relative abundance of zooplankton groups (ACCO: Abundance of Cladocera, Copepoda and Ostracoda), and assesses the taxon sensitivity to water quality. Nevertheless, zooplankton as an indicator group requires high determination skills and usually has a low perceived societal value. Moreover, seasonal or even daily fluctuations can occur making the timing of sampling the active communities an important factor. To survive periodic unsuitable conditions (such as drought, predation, low temperatures or oxygen levels), zooplankton species produce resting eggs that accumulate as resting egg banks in the soil and from which they recolonize the habitat when suitable conditions restore (Brendonck and De Meester, 2003; Angeler and García, 2005). The conceptual study by Angeler and García (2005) suggests that the process of hatching from the egg banks can be disturbed by both physical and chemical stressors and may therefore be used as an indicator for anthropogenic stress. This allows monitoring the ecological integrity of systems when it is difficult to sample the active communities. It can also be used to complement the snapshot sampling of active communities as the egg bank integrates temporal variation in community dynamics. Sediment sampling can easily be done year-round, even when ponds are dry. Deeper sediment layers can even be sampled (using a sediment core) to reconstruct past changes on the basis of biological remains and life resting eggs archived in the sediment (Cousyn

et al., 2001; Whitmore and Riedinger-Whitmore, 2014). A study by Vandekerkhove et al. (2004) showed that it is possible to estimate the biodiversity of cladocerans in shallow lakes by directly identifying the ephippial eggs using morphological characteristics. As a result, only a single sampling effort is required and sediment samples can be processed quickly after collection. Identification of resting stages is not yet studied down to species level in all taxa, so adequate determination keys are required (Vandekerkhove et al., 2004). 2.2.4. Macrophytes Most examples of studies in which plant communities are used to assess ecological quality of a habitat concern terrestrial systems (Bobbink et al., 2010). However, macrophytes also have an important indicator value in aquatic environments due to their well-known life histories and succession pattern, susceptibility to stress, immobility and the relative ease of identification (Bornette and Puijalon, 2011). Aquatic plants respond directly (through competition for light and nutrients) or indirectly (through food web interactions) to changes in water quality. Especially eutrophication is a major stressor for aquatic biodiversity, and particularly for macrophytes, as shown by Rosset et al. (2014). Bornette and Puijalon (2011) reviewed the different responses to abiotic factors of aquatic plants in freshwater habitats. This review shows that functional traits, dispersal and dynamics of aquatic plant communities are not only impacted by eutrophication, but also by other environmental parameters, including light, temperature, substrate characteristics and water movements. A study of 110 boreal lakes of Finland (Alahuhta et al., 2013) shows that submerged plants are more dependent on water transparency, nutrient concentration and carbon in the water than the emergent vegetation. In contrast, emerging macrophytes are only influenced by the low availability of light in the early stage of development and can use carbon dioxide from the air. These emerging plants can also obtain nutrients from the sediment and are therefore less related to water quality. Macrophyte community composition and abundance is also part of the indicators integrated in the WFD (Dudley et al., 2013). It is also generally accepted that there is a positive correlation between the diversity and complexity of vegetation and diversity of vegetation dependent animal taxa (Nicolet et al., 2004). Croft and Chow-Fraser (2007) developed a Wetland Macrophyte Index (WMI) for the Great Lakes to infer the ecological condition of the wetlands based on presence/absence of emergent and submerged macrophyte species. A macrophyte-based nutrient index for Swiss ponds was developed by Sager and Lachavanne (2010), whereby the ecological response to total phosphorous was measured using indicator values and species cover. Also in temporary ponds in southwest Portugal, efforts have been made to distinguish different pond types based on presence/absence of selected indicator plant species, which helps decision makers in a practical way with assessing temporary pond status (Pinto-Cruz et al., 2011). Similarly, in temporary ponds on both sides of the Strait of Gibraltar (Iberian Peninsula and Morocco), the plant community structure is related to nutrient loading, temperature and precipitation (Lumbreras et al., 2012). Lastly, the richness and abundance of rare and temporary pond specific species are a good indicator of chemical and physical disturbances in temporary ponds (Rhazi et al., 2001b; Bouahim et al., 2014). Rare and temporary pond specific species are not tolerant of disturbances, impacting negatively their richness and abundance. Submerged and emergent macrophytes in wetlands can be mapped by remote sensing technology (Adam et al., 2010; Dekker and Hestir, 2012). Based on their spectral signature, macrophyte cover (both submerged and emergent vegetation) and water quality variables such as chlorophyll a, cyano-phycocyanin levels (reflecting the presence of algal blooms) and suspended matter

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can be estimated. Although free and multispectral imagery from Landsat are frequently used for large waterbodies, due to its coarse (30 m) resolution, it is not suitable for small systems (De Roeck et al., 2008). However, high multispectral imagery (e.g. Ikonos, Worldview-2 and RapidEye), is now commercially available (spatial resolution 2–5 m) and can be used for this purpose (Dekker and Hestir, 2012). 2.2.5. Phytoplankton Phytoplankton (such as diatoms and cyanobacteria) have a long history as ecological indicators, being characterized by a cosmopolitan distribution, short generation time and low sampling effort (Resh, 2008). They are therefore also included as an indicator group in the WFD, where the quality score is based on the composition, abundance and biomass of phytoplankton. The composition and total biomass of algal species in aquatic systems serves indeed as an important metric for organic pollution and nutrient loading, such as nitrogen and phosphorus (Brucet et al., 2013). Different analytical methods exist, such as the in situ measurement of phycocyanin (characteristic pigment of cyanobacteria), analyzing the morphological variability of phytoplankton to assess the trophic state of lakes (Naselli-Flores, 2013) or the more universal assessment of chlorophyll a, as a general indicator of phytoplankton biomass (Paerl et al., 2003; Boyer et al., 2009). Particularly diatoms are often used for biomonitoring, for example in the Diatom Index of Wetland Condition (DIWC), which was developed for marshes in Florida, with epiphytic diatom density as the most responsive factor to a range of anthropogenic disturbances (pesticides, sedimentation and habitat alteration) (Lane and Brown (2007). As with macroinvertebrates, identification until genus level, or even taxa based approaches such as functional groups, can also act as a surrogate for phytoplankton richness and community structure (Gallego et al., 2012). For a review on using phytoplankton (among others) as a bioindicator in European lakes, see Brucet et al. (2013), where they showed that assessment methods based on phytoplankton had the highest number of correlations between anthropogenic stress and impact on their environment. 2.2.6. Multimetric indices (MMI) Proper assessment of surface water health conditions in general and of wetlands more particular requires the simultaneous assessment of its physical, chemical, and biological components (Sims et al., 2013). The integration of multiple species groups together with physical and/or chemical parameters in multimetric indices (MMI) is therefore preferred over single component indicators. The trophic state index (TSI) for lakes, developed by Carlson (1977), is based on Secchi disk transparency, chlorophyll and total phosphorus. While abiotic variables are relatively easy to measure, they have limited value when not complemented with biological assessments since they only represent (spatial/temporal) snapshots of the current state of a wetland. Therefore, most multimetric indices implement biotic variables as well. The QAELS index, developed by Boix et al. (2005) for assessing the water quality of temporary and permanent freshwater and thalassohaline wetlands, is a good example of the integration of different faunal groups and water parameters to assess ecological integrity. This index is based on the relative abundance of each microcrustacean taxon (ACCO) and is improved by adding the taxonomic richness of insects and crustaceans (RIC). Lougheed and Chow-Fraser (2002) developed a similar Wetland Zooplankton Index (WZI) for the Laurentian Great Lakes Basin, based on water quality (total phosphorus, total nitrogen, total suspended matter, chlorophyll a, temperature, dissolved oxygen, pH and conductivity) and zooplankton associations with aquatic vegetation. Another example is the MMI by Verdonschot et al. (2012) to assess the eutrophication status of arable ditches, which includes the number of Trichoptera and Gastropoda families,

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total freshwater taxa and predator taxa and the nutrient loading. The Predictive SYstem for Multimetrics (PSYM) method, similar to the PLOCH method, was developed for (permanent) ponds in the UK, to compare a particular site to other ponds on ecological quality (Biggs et al., 2000). Here, the combination of several macroinvertebrate and macrophyte metrics, including the number of Odonata, Megaloptera and Coleoptera families, the number of uncommon plant species and the trophic ranking scores (i.e. eutrophication measure) for aquatic and emergent plants, are used to assess the general quality assessment of lentic systems. The Lagoon Eutrophication Monitoring Network (LEMN) method, developed by Souchu et al. (2000) for shallow lagoons in Southern France, integrates information on water quality (temperature, salinity, turbidity, concentrations of dissolved oxygen, nutrients chlorophyll a, tP and tN), soil quality (organic matter content, total phosphorus, total nitrogen, granulometry, reduction potential), phytoplankton (abundance of cells < and >2 ␮m), acrophytes and macroinvertebrates (species composition, species richness, specific biomass density), to give an indication of the eutrophication status of the lagoons. 2.2.7. Modeling approaches Another way to estimate biological quality of freshwater habitats is via predictive modeling approaches. An example is the RIVPACS (River InVertebrate Prediction and Classification System) method, originally developed for rivers, which allows predicting macroinvertebrate communities of a habitat based on measured environmental variables (Wright et al., 1998). A series of pristine UK river sites were carefully selected as reference habitats. For each site, macroinvertebrate data and physicochemical characteristics (geographical data, historical data, substrate composition an alkalinity) were collected, sometimes during multiple sampling campaigns over the year, after which rivers were classified in river site groups. Modeling allows then to relate the community data with the physicochemical data. When a physicochemical sample is taken from a (new) site of interest, the expected macroinvertebrate fauna can be predicted according to the reference site. The WFD defines this expected fauna as the ‘biological reference condition’. If then a macroinvertebrate sample is taken from this new site, the observed macroinvertebrate community can be compared with the expected reference community. The ecological quality of the site is then assessed by the observed/expected (O/E) ratio. The advantage of this method (at least for UK rivers) is that the O/E ratio can easily be converted to Ecological Quality Ratios (EQRs), which are used by the WFD. In addition, O/E ratios can be interpreted more intuitively and have a biological explanation (Cao and Hawkins, 2004). 3. Discussion We have given a broad overview of different indicator types and indices that are currently used to assess the ecological integrity of (mostly permanent) surface waters. We will now discuss to what extent we expect some of these methods to be useful for the monitoring of temporary wetlands, once adapted to this particular ecosystem. First, we focus on the most important ecological characteristics of temporary wetlands, and we will then use this as a basis to evaluate and recommend potentially useful monitoring approaches. We will also suggest specific sampling guidelines to acquire the needed biological information in a standardised way. 3.1. Temporal and spatial regional variability in temporary ponds Temporary ponds are often characterized by their small size (generally not larger than 5 ha) and shallow depth (often only a few decimeters deep). Such small water volumes are prone to strong diurnal and seasonal fluctuations in abiotic parameters such as pH, oxygen, nutrients, temperature and conductivity

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(Grillas et al., 2004; Williams, 2006). Water levels also tend to vary strongly between and within years depending on climate conditions (Vanschoenwinkel et al., 2009; Waterkeyn et al., 2009; Sahuquillo et al., 2012). This implies that snapshot measurements of water quality parameters only provide information on the current state of the wetland, neglecting sometimes prominent temporal fluctuations. The timing of these measurements (time of day, season, year) is therefore critically important for the outcome of the assessment. The seasonal fluctuations in abiotic conditions also result in a strong turnover of the animal and plant communities, with clear successional phases, each represented by a characteristic group of organisms (Wiggins et al., 1980; Rhazi et al., 2001b; Gascón et al., 2005; Jocqué et al., 2007; Waterkeyn et al., 2009; Vanschoenwinkel et al., 2010). The community assembly process strongly depends on the hydroperiod (length of inundation) of the wetlands since it represents the time window during which organisms must hatch/germinate/colonize, grow and reproduce (Brooks, 2000; Serrano and Fahd, 2005; Vanschoenwinkel et al., 2009). Also inter-annual differences in environmental conditions can lead to different communities in subsequent inundations (Rhazi et al., 2009; Waterkeyn et al., 2009). The timing of sampling therefore greatly determines which taxa will be encountered (i.e. high beta-diversity in time). Additionally, ponds within one single region are often characterized by different communities and environmental characteristics and contribute therefore to high beta-diversity in space (Williams et al., 2004; Angeler et al., 2008), which makes it difficult to find general indicator species. Therefore, conserving and monitoring networks of temporary ponds instead of focussing on single ponds was suggested to be more accurate and efficient (Gómez-Rodríguez et al., 2009; Rhazi et al., 2012; Rosset et al., 2014). 3.2. Monitoring guidelines for temporary wetlands As fishes are generally lacking from temporary wetlands, with the exception of killifish and lungfish in some (sub)tropical regions, indices based on this faunal group are not useful and will not be further discussed. Biotic indices such as the waterfowl index and the frog call intensity index are also less useful for assessing the biotic integrity of temporary systems since the presence of these faunal groups is often limited in time. Nonetheless, since temporary ponds are often used as breeding spots for amphibians (Sala et al., 2008; Gómez-Rodríguez et al., 2009), the quantification of larvae during springtime could be integrated in a multimetric index, especially since amphibian larvae are very sensitive to pollution (Baker et al., 2013). With the development of multi-spectral remote sensing techniques (Adam et al., 2010; Dekker and Hestir, 2012), large wetland areas can be assessed at once and followed-up on the long term. However, since most temporary wetlands are small sized water bodies, even only quantification of these habitats remains difficult (De Roeck et al., 2008). In contrast to the assessment of the ecological status of shallow permanent lakes that can be done by visiting the lake only once to have an overview of the catchment, measure pH and turbidity, and document the general structure of macrophyte communities (Moss et al., 2003), temporary ponds are such fluctuating, highly dynamic systems that well adapted sampling protocols are required that incorporate this temporal variability (Brock et al., 2003). Therefore, we propose standardized guidelines that can be used for sampling ponds with a periodically dry phase, taking into consideration this seasonality. 3.2.1. Dry phase During the dry phase of a temporary pond, the sediment is easy to sample. By collecting a spatially integrated sample of the upper 3 cm of the sediment (i.e. ‘the active egg bank’; Brendonck and De Meester, 2003), and hatching it in the laboratory under controlled

climate conditions, ‘hidden’ pond biodiversity can be assessed (and restored) even when the pond is not inundated (Brendonck and Williams, 2000; Angeler and García, 2005; Vandekerkhove et al., 2005). However, since a proportion of eggs may remain unhatched after one laboratory inundation, ‘sugar floatation’ can be used to isolate the remaining eggs (Onbé, 1978; Marcus, 1990). This fraction can then be further identified under a microscope based on egg morphology (Vandekerkhove et al., 2005; Brendonck et al., 2008). Similarly, this also applies to annual temporary pond plants, which produce seeds that build up the seed bank in the upper layers of the sediment (Rhazi et al., 2001a). The isolation of seeds is based on the method by Malone (1967), and identification can be performed by comparing seed morphology. Due to partial hatching at each occasion combined with disturbance of the surface sediment, eggs in the resting egg bank often originate from different generations (Brendonck and De Meester, 2003). As such, so-called ‘mixed’ egg banks may not always provide information on recent changes in the ecological quality of wetlands. In systems where conditions are temporarily unfavorable for certain species that do survive as resting propagules in the sediment, assessing the ‘hidden biodiversity’ may therefore result in a different ecological status than if the assessment was only based on the active communities (Vandekerkhove et al., 2005). This aspect is often neglected in existing indices for permanent waters, but can be implemented in indices for temporary waters to create a more integrated long term view of the wetland condition. The sediment itself can also be used for measuring soil abiotic indicators, such as granulometry, chemical characteristics (pH, total N and P) and dry organic matter content, as described in the LEMN method by Souchu et al. (2000). These variables are less sensitive to fluctuations in the water column and measurements can even be done during the dry season when the sediment is exposed. Some anthropogenic impacts that are possibly invisible during the wet season, such as the amount of landfill or patterns of cattle use intensity (trampling and grazing), may also be easier to assess during the dry season.

3.2.2. Aquatic phase The sediment has a strong impact on the recycling of dissolved minerals (Golterman, 2004). In temporary ponds, this effect is even stronger since the dissolved minerals are recycled through the sediment each time they dry out. Therefore, trophic thresholds developed for deep stratified lakes cannot be used for assessing the ecological quality of temporary ponds, implying the necessity for a new method of eutrophication assessment (Elkiathi et al., 2013). As such, based on the equilibrium model of Golterman (2004), the ratio between the amount of total phosphorus and the particulate P pools could act as an indicator for eutrophication in these temporary habitats. Preliminary analyses based on Spanish and Moroccan wetland data appear to confirm this (Serrano, unpublished data and Van den Broeck et al., unpublished data). Sampling of the active communities (zooplankton, phytoplankton, macroinvertebrates, macrophytes and amphibians) should be accomplished to provide additional information on species that permanently inhabit the pond but that did not hatch in the lab from the egg or seed bank. Such sampling is evidently also needed to collect the active dispersers that usually leave the pond before it dries. Due to the high animal and plant species turnover, multiple sampling campaigns during the wet season are preferred (Boix et al., 2005; Boven and Brendonck, 2009). Sampling one month after inundation and one month before drying out, completed with a sampling campaign half-way through the inundation, gives in most cases an integrated view of the community structure. An additional floral survey can be performed after drying out, to assess the community structure of the late successional plants as well.

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The high sensitivity toward disturbances and known ecology of zooplankton, macroinvertebrates and macrophytes allow the integration of these diverse groups in biotic indices for temporary waters. The integrative QAELS index developed by Boix et al. (2005) offers interesting perspectives. First, this index is based on multiple sampling periods which are necessary to generate an integrated overview of the turnover of species and fluctuating dynamics. Second, it integrates both zooplankton and macroinvertebrates into one single index. Although zooplankton is a very important part of the ecological integrity of wetlands (Moss et al., 2003), the WFD currently lacks the incorporation of a zooplankton component. The use of macro-invertebrates in (multi-metric) indices, however, is already widely used in permanent systems (Criado and Alaez, 1995; Verdonschot et al., 2012), and recently also in temporary ponds (Gutiérrez-Estrada and Bilton, 2010). Since it has been shown for some groups (e.g. Coleoptera) that considering only higher taxonomic levels already allows the monitoring of water bodies (Criado and Alaez, 1995; Oertli et al., 2005a; Gutiérrez-Estrada and Bilton, 2010), detailed species identifications, which are often difficult and time-consuming, may not always be necessary. Nevertheless, if detailed species identifications are required, we consider DNA barcoding as a potential tool to overcome identification difficulties, such as shown for example by Hajibabaei et al. (2011) for benthic freshwater macroinvertebrates. Indices based on macrophytes are also highly relevant since temporary ponds often house characteristic highly specialized species (Bouahim et al., 2014) that play important roles in temporary wetland ecosystems (Bornette and Puijalon, 2011). Predictive modeling approaches offer additional tools for the bioassessment of temporary waters, not only using macroinvertebrates, but also other taxonomic groups that show strong associations with their environment. Although temporary ponds show strong spatial and temporal variation, multiple sampling of active faunal and floral communities, in combination with analyzing the resting communities and the environmental variables, should indicate which sites are more pristine and can be used as reference sites in the area. Temporary ponds of interest can then be assessed using the O/E ratio, cfr. Wright et al. (2000). However, currently too little data is available to validate this method. Lastly, for monitoring temporary ponds, we specifically promote the use of large branchiopods (Anostraca, Notostraca, Laevicaudata, Spinicaudata and Cyclestherida). These very characteristic temporary wetland species are considered to be the flagship species of these habitats (Colburn, 2004). They have a worldwide distribution (Brendonck et al., 2008), their ecology is well studied and they produce resting egg banks which can easily be sampled and hatched in the laboratory (Brendonck and Williams, 2000). Most of them also have a species specific egg morphology (Brendonck and De Meester, 2003) making them easily identifiable after isolation. Different studies indicate that large branchiopods are very sensitive toward stressors, such as salinity (Waterkeyn et al., 2010), hydrological changes (Pyke, 2005; Tuytens et al., 2014), pollution (Hamer and Brendonck, 1997) and habitat modification (Vanschoenwinkel et al., 2013). 3.3. Concluding remarks Although temporary wetlands are a main water source in (semi) arid regions and often house a unique diversity, official monitoring programs like the WFD currently do not cover this habitat type. Despite the broad range of different types of existing indicators and indices for lakes and wetlands, many limitations still exist for their general application (Innis et al., 2000). One such constraint is the lack of taxonomically skilled specialists who can easily identify taxonomic groups down to the desired level. Combined with further needed research on the auto-ecology of particular taxa, this should

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lead to the development of more reliable indicators that can be used by wetland managers. These limitations and recommendations also apply for the current set of monitoring tools for temporary wetlands that were generally only developed and applied for a particular region. These restrictions can lead to the further degradation of temporary ponds worldwide, especially in combination with the various and often inconsistent international agreements and national policies. Based on existing tools for assessing different types of water bodies, we have made some recommendations for standardized monitoring of this largely ignored but ecologically and socio-economically important habitat type. We have especially highlighted the use of large branchiopods, mixed resting egg and seed banks and sediment quality to assess the quality status of temporary ponds. As both resting egg and seed banks and sediment integrate temporal variation in water and habitat quality, they could to some extent replace labor intensive frequent snapshot sampling of the active communities. When these methods are further investigated, they can be validated for a large set of temporary pond types from different regions, followed by the realization of user friendly protocols and training sessions, especially for land owners and conservationists. Acknowledgments Maarten Van den Broeck is a research fellow funded by a VLIRUOS (VLADOC) grant. This project has been achieved with the financial support of a VLIR-UOS SI project (No. ZEIN2011Z092/ 2011-101). References Adam, E., Mutanga, O., Rugege, D., 2010. Multispectral and hyperspectral remote sensing for identification and mapping of wetland vegetation: a review. Wetlands Ecol. Manage. 18, 281–296. Adamus, P.R., Brandt, K.H., 1990. Impacts on quality of inland wetlands of the United States: a survey of indicators, techniques, and applications of community-level biomonitoring data. In: EPA Report 600/3-90/073. Alahuhta, J., Kanninen, A., Hellsten, S., Vuori, K.-M., Kuoppala, M., Hämäläinen, H., 2013. Variable response of functional macrophyte groups to lake characteristics, land use, and space: implications for bioassessment. Hydrobiologia 737, 1–14. Amat, J., Green, A., 2010. Waterbirds as bioindicators of environmental conditions. In: Hurford, C., Schneider, M., Cowx, I. (Eds.), Conservation Monitoring In Freshwater Habitats, vol. 5. Springer, Netherlands, pp. 45–52. Angeler, D.G., García, G., 2005. Using emergence from soil propagule banks as indicators of ecological integrity in wetlands: advantages and limitations. J. North Am. Benthol. Soc. 24, 740–752. Angeler, D.G., Viedma, O., Cirujano, S., Alvarez-Cobelas, M., Sánchez-Carrillo, S., 2008. Microinvertebrate and plant beta diversity in dry soils of a semiarid agricultural wetland complex. Mar. Freshwater Res. 59, 418–428. Angermeier, P.L., Davideanu, G., 2004. Using fish communities to assess streams in Romania: initial development of an index of biotic integrity. Hydrobiologia 511, 65–78. Arheimer, B., Löwgren, M., Pers, B.C., Rosberg, J., 2005. Integrated catchment modeling for nutrient reduction: scenarios showing impacts, potential and cost of measures. AMBIO 34, 513–520 (A Journal of the Human Environment). Baker, N.J., Bancroft, B.A., Garcia, T.S., 2013. A meta-analysis of the effects of pesticides and fertilizers on survival and growth of amphibians. Sci. Total Environ. 449, 150–156. Balcombe, C.K., Anderson, J.T., Fortney, R.H., Kordek, W.S., 2005. Wildlife use of mitigation and reference wetlands in West Virginia. Ecol. Eng. 25, 85–99. Beck, M.W., Hatch, L.K., 2009. A review of research on the development of lake indices of biotic integrity. Environ. Rev. 17, 21–44. Biggs, J., Nicolet, P., Mlinaric, M., Lalanne, T., 2014. Report of the Workshop on the Protection and Management of Small Water Bodies. European Environmental Bureau (EEB). Biggs, J., Williams, P., Whitfield, M., Fox, G., Nicolet, P., 2000. Biological techniques of still water quality assessment. Phase 3. Method development. In: Environment Agency R&D Technical Report E110. Environment Agency, Bristol. Bird, M., Day, J., 2010. Aquatic invertebrates as indicators of human impacts in South African wetlands. In: Wetland Health and Importance Research Programme (WRC Project No. K5/1584). Birk, S., Bonne, W., Borja, A., Brucet, S., Courrat, A., Poikane, S., Solimini, A., Van De Bund, W., Zampoukas, N., Hering, D., 2012. Three hundred ways to assess Europe’s surface waters: an almost complete overview of biological methods to implement the water framework directive. Ecol. Indic. 18, 31–41. Blaustein, L., Schwartz, S.S., 2001. Why study ecology in temporary pools? Isr. J. Zool. 47, 303–312.

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