People have been fishing for millennia and they have always tried to improve their methods (von Brandt 1964), both to increase their catch and to compensate for the declining catch per unit effort (CPUE) due to diminishing abundance of the underlying resources that fishing causes (Engelhard 2016). Since the late 19th century, following the introduction of steam trawlers in England, which marks the start of industrialized fishing, the improvement of technology has been relentless; the current vessels are much more powerful than steam vessels of similar tonnage (Engelhard 2008, Thurstan et al. 2010, Engelhard 2016).
The relationship between fishing mortality and fishing effort is F = f × q, where F is the fraction of the fish population that dies from fishing during a unit of time (conventionally one year); f is a measure of fishing effort (e.g., boats/d or fishing-h/d of a certain type of fishing vessel); and q is the catchability coefficient (Beverton and Holt 1957, Arreguín-Sánchez 1996).
Technological creep (C) can be conceived as either a dimensionless change in catchability (q) or a dimensionless change in some aspect of nominal effort (Gulland 1956, Sanders and Morgan 1976). Either way, it affects fishing mortality. Conversely, if F is to be kept at a given level, effective effort will increase due to technological creep, and nominal effort must be reduced accordingly.
Technological improvement can be conceptually separated into two groups: (1) major improvements in gear design, fish finding, and catch handling resulting in massive increase in effective fishing effort when they are implemented throughout a fleet within a few years; and (2) small background alterations in the rigging of a vessel or the skill of skippers at handling new technology or applying information technology, etc. (see Marchal et al. 2006). Technology creep factors receive far too little attention from fisheries scientists and even less from fisheries managers, for example, when they attempt to freeze the amount of fishing effort at a certain level but fail to account for the increase in effective effort of the vessels whose number is frozen. A similar problem occurs when subsidizing fleet retirement programs that allow decommissioning funds to be applied for the purchase of new, more efficient vessels (Munro and Sumaila 2002 and references therein, Pauly et al. 2002). Most studies of the creep factor refer to cases of the first type because the effect is strong and visible and thus justifiably attracts scientific and management attention. However, because of the cumulative effect, the changes of the second type are also important; they occur relentlessly, even when no major technology improvements appear to be taking place.
Engelhard (2016) emphasizes the general lack of quantitative information allowing for the estimation of the speed at which changes in fishing power happen over time and encourages more research on the topic. Our contribution’s aims, therefore, are to present a number of estimates of this creep factor (both previously published and newly estimated) and, based on those, to propose an empirical relationship derived to allow inferences on long-term values of creep factor by combining both types of technologial improvements (Fig. 1).
A literature search to update the data of Pauly and Palomares (2010) was conducted (originally in 2013 and updated in 2017), targeting estimates of time series trends of fishing power or fishing efficiency available from online resources. We searched the Aquatic Sciences and Fisheries Abstracts (ASFA), Web of Science (WS), and Google Scholar (GS) using the search terms “fishing power” and “fishing efficiency” occurring in the title. This search yielded 127, 45, and 155 hits for fishing power and 127, 31, and 133 hits for fishing efficiency in ASFA, WS, and GS, respectively. Of these records, 24 contributions contained usable time series data of fishing power (51 case studies; see Table 1) from which estimates of the annual increase of fishing power or fishing efficiency were obtained.
Though straighforward, the methods used to transform the source data to percentage annual increase in fishing power (C%) differed because the source data were heterogeneously expressed as: annual rate of change (Ward 2008), annual compounding increase (Hannesson et al. 2008, Thorson and Berkson 2010), average increase in fishing power (Brown et al. 1995, Zhou et al. 2015), fishing power in smack units (Engelhard 2008), increase in catchability coefficient (Atmaja and Nugroho 2011), change in technology coefficient (Gelchu and Pauly 2007) or efficiency (Hutton et al. 2003), change in loading capacity (Ruiz-Luna et al. 1997), average trend in fishing power (Marchal et al. 2002), or chain of total factor productivity (Squires 1994). Details of these transformations are provided in Table 1. In cases for which the source data were from comparisons of fishing power or efficiency from different vessel types fishing in parallel, the instantaneous rate of technological creep (C; yr−1) was obtained, and the corresponding annual percentage increase (C%) is reported (Table 1) along with the resulting regression statistics (see Gascuel et al. 1993, Gelchu and Pauly 2007, Engelhard 2008).
The first result we obtained is a series of 51 estimates of C (Table 1). We first comment in detail on two sets of these estimates. We then continue with their analysis.
Our first case, based on portions of the data in Table 1, presents estimates of C (Fig. 1) in English trawl fisheries for cod and plaice based on the classical method for fishing power estimates (Gulland 1956) for trawlers fishing in parallel. This time series, extending from 1880 to 2005, was reported by Engelhard (2008:table 1). Here, all ranges were replaced by the corresponding mid-ranges, and a few points were identified as outliers (e.g., those representing sailing vessels; see Fig. 2). The resulting slopes of the plot (Fig. 2, Table 1) provide relatively low estimates of C for cod and plaice, respectively, pertaining to an extraordinarily long time series of 129 yr.
Our second case is the result of an informal workshop reported by Fitzpatrick (1996; see Table 2), whose main result was that, over a period of 30 yr, the participating skippers of a wide range of vessel types perceived an increase in the efficiency of fishing gear, i.e., fishing power, equivalent to C% = 4.43%/yr ± 0.00255. Details of this exercise are not available, which would enable the results from the different fisheries included therein to be discussed separately. Thus, the entire exercise contributed only one estimate to our analysis (Table 1, Fig. 3).
Other estimates that were obtained from secondary data are documented (Table 1). Overall, these estimates of C range from 0.0049 to 0.201 yr−1, with a group of suspiciously low values (0.0049–0.0245 year−1) published by O’Neill et al. (2003) and O’Neill and Leigh (2007), who studied fishing power in various Australian invertebrate fisheries (see numbers 30 and 44–51 in Table 1).
When the estimates of C in Table 1 are converted to ln(C%) and plotted against the logarithm of the number of years for which they were estimated, the result is the significantly negative relationship (P < 0.005; Fig. 3) summarized by the equation:
C% = 13.8 × Y−0.511
which links C% to the duration of the period (Y, in years) for which C% was estimated. The average fishing power increase was 3.4% for the average period of 15 yr (Fig. 3). The regression relationship is not very tight, but given the heterogeneous nature of the data that went into the point estimates and of the underlying models (general linear models, chains of comparisons of successive trawler type, subjective assessments, etc.), a better fit probably cannot be expected.
Our results can be used in a practical way for a specific fishery or for global fisheries (see Anticamara et al. 2011) for which there is no other estimate of technological creep. The following equations can be used to calculate increase in effort (E) or decrease in CPUE as indicators of abundance.
E(t) = E(t = 0) × (1 + pd)t
where t is the time in years after t = 0 and pd is the percentage creep reexpressed as a decimal fraction, i.e., C%/100. The multiplier for correcting CPUE is then:
Corr(t) = (1 − pd)t
CPUECorr(t) = CPUE(t) × Corr(t)
where the first value of CPUE is treated as t = 0, which leads to Corr(t = 0) = 1.
These equations are now part of the CMSY method (a Monte Carlo method for estimating maximum sustainable yield) as originally presented by Froese et al. (2017) but whose implementation code is now modified to include an option for accommodating technological creep in the CPUE data that can be used as constraints (see http://oceanrep.geomar.de/33076/).
Eq. 1 provides estimates of C% values of 1.3% for 100 yr, 1.9% for 50 yr, 4.3% for 10 yr, and 6.1% for 5 yr. No pattern could be identified for the data (Table 1) that would have allowed for specific fisheries (pelagic vs. demersal, large scale vs. small scale) to be identified (except for the low values of O’Neill and collaborators [2003, 2007]).
Our results also have a deeper societal aspect related to the rapid decline, in the Anthropocene, of global biodiversity (Butchart et al. 2010), particularly in the oceans (Worm et al. 2006). This decline is due, in large part, to the terrible efficiency of the technology that we deploy to torture what we want from soils (e.g., through fertilizers applied to irrigated monocultures) or from the oceans (e.g., by deploying thousands of trawlers, which destroy sea-floor communities). The problem is that we do not really notice this because of shifting baselines (Pauly 1995): To us, a tractor plowing a field in the 21st century looks like a tractor at the beginning of the 20th century, and a trawler plowing the sea in the 21st century looks like a trawler at the beginning of the 20th century. However, the newer technologies are profoundly different in that they have much greater environmental impacts than do the older ones. We will be in trouble as a species if we do not account for this difference.
When analyzing time series of CPUE obtained from commercial vessels (as opposed to research vessels, whose rigging and operations are standardized and are supposed to remain similar over decades), Eqs. 2–4 can be used in the absence of any knowledge about the technological creep in a given fishery. This method also should apply to the effort used in stock assessments in surplus production modeling (Schaefer 1954), CMSY (Froese et al. 2017), or related methods.
This study was supported by the Sea Around Us, a research activity of the University of British Columbia funded by several philanthropic foundations, among which are the David and Lucille Packard Foundation, Oak Foundation, and the Marisla Foundation. We thank Sebastian Villasante for inviting us to contribute to the special feature of Ecology and Society. We also thank Rainer Froese for Eqs. 2–4, which should facilitate the future use of our results.
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