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Habitat Selection: Implicationsfor Monitoring, Management, andConservationNiclas Jonzén aa Department of Ecology, Division of Theoretical Ecology,Ecology Building, Lund University, SE-223 62 Lund, SwedenPublished online: 14 Mar 2013.
To cite this article: Niclas Jonzén (2008) Habitat Selection: Implications for Monitoring,Management, and Conservation, Israel Journal of Ecology & Evolution, 54:3-4, 459-471, DOI:10.1560/IJEE.54.3-4.459
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ISRAEL JOURNAL OF ECOLOGY & EVOLUTION, Vol. 54, 2008, pp. 459–471DOI: 10.1560/IJEE.54.3–4.459
E-mail: [email protected] 9 November 2007, accepted 1 May 2008.
Habitat Selection: implicationS for monitoring, management, and conServation
Niclas JoNzéN
Department of Ecology, Division of Theoretical Ecology Ecology Building, Lund University
SE-223 62 Lund, Sweden
AbSTRACT
Habitat selection is an important process that affects the distribution and abundance of organisms, and habitat selection theory is one of the flagships of theoretical ecology. because of the importance of habitat selection in natural ecosystems and the successful history of the theoretical concepts, it has been suggested that habitat selection theory can inform decision-making in population monitoring and solve management and conservation problems. In this paper I further emphasize the potential for habitat selection theory to be a useful framework to address fundamental problems of relevance for monitoring, management, and conservation. I also identify what I perceive as important gaps in our knowledge and weaknesses of current habitat selection theory when approaching real-world problems.
Keywords: habitat selection, ideal free distribution, monitoring, conservation, harvesting, dispersal, spatial population dynamics
INTROdUCTION
The study of habitat selection has a long history in ecology (e.g., Grinnell, 1917; Mayr, 1926; Lack, 1933; Svärdsson, 1949; Morisita, 1950). Initiated by the seminal work on the Ideal Free distribution (IFd) by Fretwell and Lucas (1969) and Fretwell (1972), the ideas about how competition shapes habitat use soon developed into a theory of density-dependent habitat selection (Rosenzweig, 1981, 1991). Habitat selection theory provides a general framework for understanding how individuals ought to be distributed in heterogeneous environments. As a theoretical tool it has served ecology well and it has even been suggested that habitat selection provides a causal link between individual behavior, population regulation, and community structure (Morris, 2003), thus synthe-sizing seemingly disparate fields of ecological theory.
In this paper I will discuss some implications of habitat selection for population moni-toring, management, and conservation. by focusing on problems faced by the applied ecologist, I hope to demonstrate the possibilities and limitations of applying current habi-
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tat selection theory to real-world problems. To achieve this we must start by asking: What is the spatial scale of relevance for ecological applications such as population monitoring, management, and conservation? berryman (2002) argued that “for those who wish to ap-ply ecological science to the solution of real problems, there seems to be little choice but to accept the concept of population as their modus operandi.” By defining population as “a group of individuals of the same species that live together in an area of sufficient size to permit normal dispersal and/or migration behavior and in which numerical changes are largely determined by birth and death processes” (berryman, 2002), it is clear that the relevant spatial scale may be rather large but it will of course vary between organisms differing in dispersal/migration ability. However, many monitoring, conservation, and management issues are found at both smaller and larger scales than the population scale sensu berryman and may include protection of a subpopulation of a rare species inhabit-ing some small forest fragments, or monitoring of a taxonomic group across a continent. Therefore, importance of habitat selection and the success of applying habitat selection theory to real-world problems may differ between applications, especially since habitat preferences are often scale-dependent (Orians and Wittenberger, 1991).
MONITORING
Environmental monitoring programs have generated a wealth of time-series data on population abundance (or some index of abundance). The main purpose of the data col-lection is often to detect population trends. Some large-scale monitoring programs, such as the breeding bird Surveys in North America (James et al., 1996) and Great britain (Greenwood et al., 1995), have a spatial resolution such that the national indices are based on data collected in survey blocks on a smaller scale. Spatially extended monitor-ing makes it possible to discover geographic patterns in population trends, e.g., differ-ences between habitats.
The interpretation of habitat differences in population trends may depend on the underlying processes of habitat selection. Whereas the ideal free distribution may be the most common habitat selection model, there are alternative models that may have inter-esting consequences for monitoring. For instance, in a landscape where we can separate source habitats from sink habitats (Pulliam, 1988; Pulliam and danielson, 1991) the ob-servation of a negative population trend in a sink habitat may be due to (1) a local trend caused by a demographic change in the sink, (2) a global trend in demographic rates that can be detected in both sources and sinks, or (3) an effect of a demographic change in a source habitat that is manifested in a sink habitat via decreased dispersal from the source. Jonzén et al. (2005) studied how the relative power to detect a negative trend in the source by monitoring either the source or the sink population varied with life his-tory parameters, environmental stochasticity, and observation uncertainty. Interestingly, monitoring the sink was often most efficient even though the actual reproductive decline occurred in the source.
The source–sink structure obstructs the interpretation of monitoring data by uncou-pling local production and population trends (brawn and Robinson, 1996). However, the
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correct identification of sources and sinks renders possible a more efficient monitoring for trend detection (Jonzén et al., 2005). In practice, the existence of source–sink dynam-ics is difficult to demonstrate (Watkinson and Sutherland, 1995), but can sometimes be inferred from habitat differences in age structure. There is nevertheless evidence that source–sink structure does occur in a number of species and on spatial scales of rel-evance to monitoring and environmental decision-making (e.g., brawn and Robinson, 1996; dias, 1996; boughton, 1999; McCoy et al., 1999).
The implications of habitat selection on the optimal monitoring of population change are not limited to source–sink systems. A similar result was reported by bowers (1996) who, using a spatially explicit simulation model, found that a region-wide decrease in survivorship was detected earlier by measuring population size in poor habitats. If the data at hand are not time series of abundance (or some index thereof) but limited to infor-mation on the presence or absence in a given habitat, the optimal strategy for detecting population change will be different. Even if the largest decline is still in the poor habi-tats, the relatively low number of observed transitions (from present to absent) in poor habitats makes it more efficient to monitor the good habitats (Rhodes et al., 2006).
In conclusion, the underlying habitat selection strategies can strongly affect the spa-tio-temporal population patterns found in survey data. On the other hand, knowledge about how individuals are distributed, and why, opens up an opportunity to optimize monitoring programs.
MANAGEMENT
One of the most important applications of ecological theory is the scientific underpin-ning of fisheries management. Considering that, of the 600 marine fish stocks monitored by FAO, 52% are fully exploited, 17% are overexploited, 7% are depleted, and 1% are recovering from depletion (FAO 2007), it is not surprising that fisheries management is searching for alternative or complementary management tools. No-take marine protect-ed areas are gaining in popularity and are often seen as being part of a holistic ecosystem approach towards sustainable development (Pikitch et al., 2004). In this context habitat science is likely to play a central role by providing a theoretical framework that unifies seemingly disparate ideas on the distribution and abundance of populations, population response to environmental variation, life history, and ecological scales (Rice, 2005).
In an early treatment of how marine protected areas may perform in the long run, Lundberg and Jonzén (1999a) considered the distribution of fish across a reserve and the fished area as a habitat selection problem. Assuming that a fraction of the total area is set aside as a reserve and that the population is harvested in the remaining area, one could start asking questions about how the optimal harvest fraction and the sustain-ability of the fishery depend on habitat quality, proportion set aside as a reserve, and how the fish are distributed across the protected and exploited areas. Assuming an ideal free distribution it is straightforward to show that the quality of the reserve affects its possibility to protect a large proportion of the population from being harvested, but nei-ther the catch nor the optimal harvest rate is influenced (Lundberg and Jonzén, 1999a).
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These results change dramatically if we assume a net flow from the reserve to the fished area, e.g., a source–sink structure (Lundberg and Jonzén, 1999b; Tuck and Possingham, 2000). Much of the debate regarding when no-take reserves will increase the fishery yield (Gårdmark et al., 2006; Hart, 2006) or constitute an economically optimal fishery management (Sanchirico et al., 2006), hinges on the assumptions about how individuals ought to be distributed. Optimal fisheries management thus reinforces the need for an increased understanding of habitat use and dispersal in marine systems.
The acceptance of habitat selection theory by fishery scientists is, in part, the result of its prediction that the geographical area occupied by a population is positively related to abundance (Shepherd and Litvak, 2004), which, strictly speaking, does not prove densi-ty-dependent habitat selection (Morris, 1989; Shepherd and Litvak, 2004). In a review of density-dependent habitat selection and the ideal free distribution in marine fish spatial dynamics, Shepherd and Litvak (2004) highlighted a number of shortcomings of habitat selection theory and how it is applied to the study of marine fish dynamics. The main criticism is that tests for the presence of density-dependent habitat selection are indirect. Alternative processes (e.g., spatially correlated stochastic factors such as temperature) can give rise to the same predicted patterns, including habitat-specific/total population growth relationships (Shepherd and Litvak, 2004) and positive abundance–area relation-ships (Gaston et al., 1997). It has also been argued that current habitat selection theory is based on assumptions that are more likely to be fulfilled in terrestrial environments than in the open sea (Rice, 2005).
do we need to revise habitat selection theory entirely for it to be useful for manage-ment of marine systems? Not necessarily. Many managed fish stocks are demersal, and benthic habitats are not fundamentally different from terrestrial systems. Furthermore, many fish species inhabiting the open sea as adults have early life-stages found in ben-thic and estuarine habitats similar to terrestrial systems (Rice, 2005). So we need to ap-praise that in many ecological systems there will be spatio-temporal variation in habitat quality such that a given area may vary in habitat quality over time, and the scale of temporal predictability of this variation may vary between systems. In that respect the mismatch between the temporal stability of habitat quality assumed by habitat selection theory, on the one hand, and the high annual and seasonal variation, on the other hand, is very striking in the open ocean (Rice, 2005).
CONSERVATION
From a theorist’s point of view, exploitation and conservation are two sides of the same coin, and therefore much of the work on reserve selection in fisheries management is equally important for the conservation of biodiversity. An additional concern in ter-restrial systems is the fragmentation of landscapes and how that affects population vi-ability. How can we maintain viable populations in a world of increasingly fragmented landscapes? This is a major conservation issue where habitat selection plays a central role. If individuals would be truly ideal and free they would always select habitat such that fitness is equal across space and maximal for current density. But there are many
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reasons why the best habitats may be underused (Recer et al., 1987; Kennedy and Gray, 1993; Earn and Johnstone, 1997; Hakoyama, 2003) or why sink populations can exist (Pulliam, 1988; Holt, 1997; diffendorfer, 1998; delibes et al., 2001). So-called eco-logical traps (Schlaepfer et al., 2002) occur when individuals cannot distinguish sources and sinks or may even prefer poor-quality habitats (delibes et al., 2001) that can wipe out source populations (Kristan, 2003). Ecological traps have received both theoretical (e.g., Kokko and Sutherland, 2001; Kristan, 2003) and empirical study (see references in Schlaepfer et al., 2002; Gilroy and Sutherland, 2007), and have been singled out as an important conservation concern (Gilroy and Sutherland, 2007). They are most likely to occur when there are rapid environmental changes, such that previously high-quality habitat deteriorates, but the habitat preferences are not changed (Remes, 2000), or if the cues used in a poor novel habitat cause it to be perceived as a good one by mistake (Gilroy and Sutherland, 2007).
Novel habitats do not have to be of low quality. Overuse of bad habitats is prob-lematic from a conservation point of view; so is underuse of good habitats (Gilroy and Sutherland, 2007). Organisms may simply dismiss high-quality habitats because they misjudged the available cues. For instance, human activities may cause increased rates of disturbance that would not necessarily affect fitness if the organisms would have decided to settle in those disturbed habitats (Mallord et al., 2007).
The set of cues used for judging habitat quality may also be imprinted in the natal habitat at an early age (natal habitat preference induction, NHPI) (davis and Stamps, 2004). NHPI can have consequences for the probability of success in captive–release and translocation programs (Stamps and Swaisgood, 2007). The importance of NHPI provides an instructive example of how knowledge about the actual mechanisms behind the spatial distribution of organisms is needed to apply ecological concepts to real-world problems.
PRObLEMS ANd PERSPECTIVE
PoPULaTion DynamicS, ScaLE, anD haBiTaT SELEcTiondecision-making in population monitoring, management, and conservation often
depends on our best estimates of the distribution and abundance of organisms. Habitat selection is a key process in determining the distribution of organisms in a heteroge-neous environment, and from the examples above it should be clear that the process of habitat selection can have very important implications for a wide range of problems and challenges faced by a society striving for sustainability. Furthermore, habitat selec-tion theory has a successful history in behavioral ecology, and it can also be useful for understanding spatial population dynamics on a large scale (Haugen et al., 2006), which was the original motivation of the theory (Fretwell and Lucas, 1969; Fretwell, 1972). but there are certainly important gaps in our knowledge that limit how useful habitat selection theory is in practice at these large spatial scales.
Ecologists were slow in recognizing the importance of scaling issues (Wiens, 1989), but the publication of some influential papers (e.g., Wiens, 1989; Levin, 1992) made
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ecologists aware that biological processes often vary across spatial scales (Hobbs, 2003). This concern includes an organism’s preference or avoidance of a given habitat or other landscape features, which suggests that habitat selection studies should be scale depen-dent (e.g., boyce, 2006). Monitoring, management, and conservation issues occur on many different scales and often the scale is predetermined by political borders. The need for managers to be explicit about the scale of habitat selection has been stressed repeat-edly (e.g., Ciarniello et al., 2007) and it has been recommended that researchers should at-tempt to identify the habitat attributes that affect fitness outcomes at each relevant spatial scale on which to base management prescriptions (Chalfoun and Martin, 2007).
Our observations are potentially scale dependent, but also the spatial scale of the actual habitat selection process must be considered when applying theoretical concepts to the con-servation and management of natural populations. Organisms may assess and respond to different habitat characteristics (e.g., density or microhabitat) at different scales (Oatway and Morris, 2007). Experimental tests of some of the theory’s predictions (e.g., Morris, 1989; Tregenza et al., 1996; Abramsky et al., 2001) have been restricted to a rather small spatial scale where foragers distribute themselves between patches differing in food supply. However, habitat selection theory has also been applied to large spatial scales (e.g., Knight and Morris, 1996; Suárez-Seoane et al., 2002). The assumptions of ideal and free behavior are more likely to be fulfilled at a smaller scale, whereas the perceptual range will influence the distribution of individuals at larger scales (Lima and zollner, 1996; Tyler and Hargrove, 1997; Shepherd and Litvak, 2004). Many studies that have compared an observed distribu-tion of individuals across habitats with what is predicted from theory actually assume that the IFD is fulfilled (Jonzén et al., 2004, but see Haugen et al., 2006). In fact, spatial patterns may not be the result of active habitat selection but rather natural selection and spatial in-heritance (Schauber et al., 2007). As nicely demonstrated by Cressman et al. (2004), the IFd is an evolutionary-stable strategy in single-species, 2-habitat models, and the IFd can be achieved without invoking any movement between habitats (Ranta et al., 1999; Palmqvist et al., 2000). Convergence to the IFd occurs, provided that individuals do not move to habitat patches with lower payoff than the current one, and that some individuals always move to a patch with the highest payoff (Cressman and Křivan, 2006). Hence, the IFD may be achieved in both closed populations and coupled systems where individuals move around to maximize their individual fitness. However, if the actual spatial distribution is the only data at hand there is no way we can differentiate between these two situations.
From the applied ecologist’s point of view, is it important to know whether IFd patterns are produced by habitat selection or population dynamics? For instance, if we would like to predict the effect of harvesting in an area on the population density in a nearby protected area, does it matter if there is dispersal between the harvested and the protected area, or whether the temporal dynamics in each area are independent con-ditional on the possibly synchronizing effects of environmental stochasticity? To my knowledge this has not been studied in detail, and evaluating the risk of using the wrong model for a given applied problem should be an important exercise.
If it turns out that we can safely ignore the relative importance of habitat selection and population dynamics when the spatial distribution conforms to the IFd, there is still
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reason to be worried about large-scale studies that assume that the IFD is fulfilled but do not verify the assumption. The existence of sink populations, ecological traps, and underused habitats clearly suggests that we should at least investigate the possibility of the alternatives. It is also important to realize the fundamental difference between habitat selection on the foraging scale and the selection of habitat across vast distances. The movements between spatial elements are common to both, but become particularly critical at large scales because the possibility for immediate adjustments of the fitness consequences is limited (Palmqvist et al., 2000). Therefore we could expect net flow between habitats (see also Morris et al., 2004), and any deviations from a cost-free flow with no net dispersal between habitats can cause substantial deviations from the IFd (Palmqvist et al., 2000; but see Morris, 1992). Accepting that to choose between habitats one has to move, the coupling between habitat selection and dispersal is obvious.
DiSPErSaL anD haBiTaT SELEcTionGiven spatio-temporal heterogeneity, there is no simple characterization of fitness.
However, using numerical methods, Holt and Barfield (2001) showed that the evolution of dispersal often leads to approximate equilibration of local fitness measurements. The optimal dispersal strategy is thus similar to the IFd, which is the Evolutionary Stable Strategy (ESS) solution of habitat selection in temporally constant environments. Theo-retical work on the evolution of dispersal (e.g., Travis et al., 1999; Kun and Scheur-ing, 2006) suggests that dispersal should depend on density, a prediction that also has received some empirical support (reviewed by Matthysen, 2005). A density-dependent fraction of emigrants generally produces a distribution closer to the IFd than a constant fraction of emigrants (Palmqvist et al., 2000). Hence, even though habitat selection theory focuses more on intra-generation patterns of habitat use, and dispersal studies of-ten deal with inter-generational flow of individuals between habitats (Holt and Barfield, 2001), the concepts of dispersal and habitat selection may often lead to the same predic-tions of how individuals ought to be distributed (but see Palmqvist et al., 2000). Thus, the recent calls for more realistic dispersal models (Travis and French, 2000; bowler and benton, 2005) and the need for empirical investigations to determine the applicabil-ity of different models of the behavioral dynamics of habitat selection (Abrams et al., 2007), should challenge the way we think about habitat selection. by combining studies of co-evolution of traits such as dispersal and local adaptation (Kisdi, 2002) with studies mapping dispersal and habitat selection processes to population dynamics, we may be able to advance our knowledge about how individuals ought to be distributed in spatio-temporally fluctuating environments (Bach et al., 2007).
STochaSTiciTy anD haBiTaT SELEcTion Habitat selection theory is deterministic. In reality, however, habitat quality varies
not only across space, but also over time. Hence, we need to know more about how we should map the behavioral decisions of individuals onto their population patterns in a stochastic world. Stochasticity is important for at least four reasons. First, the actual fit-ness measure upon which we base assumptions about optimal behavior differs between
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deterministic and stochastic models. Second, by adding randomness we introduce an element of uncertainty that is an integral part of the environment where adaptive behav-iors such as dispersal have evolved. Hence, we may need to investigate stochasticity to understand why certain strategies exist and to be able to treat the uncertainty in infor-mation available for decision making (Hakoyama, 2003). Third, if we think that habitat selection is important enough to inform our decision about such issues as reserve design in fisheries management (Lundberg and Jonzén, 1999a), we need to conquer the process uncertainty produced from imperfect models (Hilborn and Mangel, 1997). Finally, it is not clear to what extent the isodar methodology—which can be used to address a number of important issues for management and conservation (Morris, 2003)—can be used in the presence of environmental stochasticity. Jonzén et al. (2004) made an attempt to ap-proach that question by generating Monte Carlo data using a stochastic habitat selection model and estimating the isodars without making any assumptions about the statistical properties of the environmental stochasticity. At each time step t, the fitness function for each habitat i was assumed to be a density-dependent random variable such that
.logWN
NEa b N 0 5
i t ei t
i t
i t i t ii i1
1
2v= + -= --
-e
]]
]
] ]o
gg
g
g g
where ai and bi are habitat-specific constants. Environmental variation was modeled by inserting a random variable Ei(t) drawn from a bivariate normal distribution with zero mean and a variance–covariance matrix Σ. It was further assumed that the standard deviation of the environmental stochasticity was equal across habitats. The isodar was estimated by regressing population density in one habitat on population density in the other habitat and estimating the slope and the intercept, i.e., following the protocol of isodar analysis. The estimated isodars will be biased unless the cross-correlation of en-vironmental stochasticity between two habitats is one (Jonzén et al., 2004). On a small scale the cross-correlation is likely to be close to one; the problem will mainly occur on larger scale.
However, the majority of isodar studies are based on spatial rather than temporal data. The simplest studies typically contrast snapshot densities between paired habi-tats. Furthermore, habitat quality is assumed to be time-invariant, because if there was variation we could no longer assume no net dispersal at all time intervals (Morris et al., 2004). In practice there will be temporal variation in habitat quality, and the estimated isodar can depend on when we sample. A key question is whether the relative quality of habitats may change differentially over time, which would make snapshot data less useful. However, it should be emphasized that it is not known to what extent that is a problem in practice for inference based on the isodar methodology. In fact, it may even be possible to measure the degree (and spatial scale) of stochasticity that exists in natural populations using isodars (Morris, 2003). The spatial scale of stochasticity is important to evaluate the scales where stochastic fluctuations in habitat quality could make isodar analysis problematic.
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WHERE dO WE GO NExT?
For a specific applied problem, one may need to know where in the landscape the organ-isms can be found and how many there are. This task can be handled by resource selection functions (Manly et al., 1993) but the results often turn out to be case specific and not easily generalized to other areas (boyce and Mcdonald, 1999). Hence, we also need to under-stand why individuals are found in certain areas to claim any real understanding of patterns of habitat use. A strategic question concerns whether we need to be explicit about adaptive movement (Fryxell et al., 2005) or whether we could sidestep the actual dispersal process and use the isodar methodology (Morris, 1987) to learn about differences in habitat qual-ity and the scale of habitat selection (Morris, 1992; Oatway and Morris, 2007). If we need to simultaneously estimate local population parameters and dispersal (Lele et al., 1998), the dispersal functions should include the possibility of density-dependence (Travis et al., 1999; Travis and French, 2000). Estimating spatio-temporal dynamic processes requires extensive and informative data. Unless we have a priori information on either the popula-tion dynamics or dispersal process, our estimates are bound to be very uncertain. An al-ternative approach would be to combine isodar analysis with actual fitness measurements. Fitness is always difficult to measure and different fitness components may be important at different scales. A recent review (Johnson, 2007) of how to measure habitat quality for birds concluded: “Habitat ecologists should use caution before relying on shortcuts from more labor-intensive demographic work”. That warning should not stop us from exploring the scale of habitat selection using distributional data (Morris, 1992; Oatway and Morris, 2007), evaluating how simple approaches (e.g., isodars) perform in situations where envi-ronmental stochasticity and population dynamics are interacting with the habitat selection process (Palmqvist et al., 2000; Jonzén et al., 2004), and finding ways of improving these approaches. The importance of the problem should provide enough motivation to keep building on the successful theory of habitat selection.
ACKNOWLEdGMENT
I am grateful to the editors of this special issue of the israel Journal of Ecology & Evo-lution for inviting me. Especially I would like to thank doug Morris for always being ready to challenge my thinking about habitat selection theory. Per Lundberg, Jörgen Ripa, doug Morris, and an anonymous reviewer provided useful comments that im-proved this paper. Financial support was given by the Swedish Research Council.
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